• 93 •

Anduli

Revista Andaluza de Ciencias Sociales

ISSN: 1696-0270 • e-ISSN: 2340-4973

ANALYSIS OF MACROECONOMIC FACTORS AFFECTING

AIRLINE STOCK PRICES

ANÁLISIS DE FACTORES MACROECONÓMICOS QUE

AFECTAN LA COTIZACIÓN DE ACCIONES DE LAS

COMPAÑÍAS AÉREAS

Abdulkadir Alici

aalici@erbakan.edu.tr

Necmettin Erbakan University, Turquía

https://orcid.org/0000-0002-4796-6385

Abstract

Change in stock prices and development of the

market vary depending on decisions made by

investors. Decisions made by investors are de-

termined by corporate performance, the coun-

try’s economic situation, political developments

and changes in macroeconomic factors. In this

study, the relationship between stock prices of

airlines and macroeconomic variables was ana-

lyzed. To accomplish this, daily macroeconomic

data of 14 airlines covering the years 2009-2018

were analyzed by Toda Yomamato and Hatemi-

J asymmetric causality tests, and macroeco-

nomic factors (Brent oil price, dollar exchange

rate and interest rate) determining stock prices

were identied. According to the results of the

Toda and Yomamato causality test, it was found

that there was a signicant relationship among

variables of the dollar exchange rate, the price

of oil and stock prices of airlines. Except for one

airline, there was no signicant relationship be-

tween interest rate and stock price. According to

the results of the Hatemi-J asymmetric causal-

ity test, it was found that there were signicant

relationships among variables of the dollar ex-

change rate, oil price, interest rate, and airline

stock prices. In light of these ndings, innova-

tion and adoption of alternative fuel technology

by the aviation industry and airlines can be a

successful alternative to oil price risk. Results

on exchange rate and interest rate changes

indicate that airlines and related governments

should focus on policies that increase growth of

the aviation industry.

Keywords: Air transportation, Airline perfor-

mance, Toda Yomamato causality test, Hatemi-

J asymmetric causality test.

Resumen

La evolución de las cotizaciones bursátiles y el de-

sarrollo del mercado varían en función de las deci-

siones tomadas por los inversores. Las decisiones

de los inversores vienen determinadas por los re-

sultados de las empresas, la situación económica

del país, la evolución política y los cambios en los

factores macroeconómicos. En este estudio se ana-

lizó la relación entre los precios de las acciones de

las compañías aéreas y las variables macroeconó-

micas. Para ello, se analizaron los datos macroeco-

nómicos diarios de 14 aerolíneas correspondientes

a los años 2009-2018 mediante las pruebas de

causalidad asimétrica Toda Yomamato y Hatemi-

J, y se identicaron los factores macroeconómicos

(precio del petróleo Brent, tipo de cambio del dólar

y tipo de interés) que determinan los precios de las

acciones. Según los resultados de la prueba de

causalidad de Toda y Yomamato, se constató que

existía una relación signicativa entre las variables

del tipo de cambio del dólar, el precio del petróleo

y las cotizaciones bursátiles de las compañías aé-

reas. Excepto en el caso de una compañía aérea,

no existía una relación signicativa entre el tipo de

interés y el precio de las acciones. Según los re-

sultados de la prueba de causalidad asimétrica de

Hatemi-J, se constató que existían relaciones signi-

cativas entre las variables del tipo de cambio del

dólar, el precio del petróleo, el tipo de interés y el

precio de las acciones de las compañías aéreas. A

la luz de estos resultados, la innovación y la adop-

ción de tecnología de combustibles alternativos por

parte de la industria de la aviación y las compañías

aéreas puede ser una alternativa acertada al ries-

go del precio del petróleo. Los resultados sobre las

variaciones del tipo de cambio y el tipo de interés in-

dican que las aerolíneas y los gobiernos relaciona-

dos deberían centrarse en políticas que aumenten

el crecimiento de la industria de la aviación.

Palabras clave: transporte aéreo, desempeño de

las aerolíneas, prueba de causalidad Toda Yoma-

mato, prueba de causalidad asimétrica Hatemi-J.

Cómo citar este artículo/ citation: Alici, Abdulkadir (2024). Analysis of Macroeconomic Factors Affecting Stock

Price in Airlines. ANDULI. Revista Andaluza de Ciencias Sociales, (25), 93-137.

https://doi.org/10.12795/anduli.2024.i25.05.

Recibido: 13.07.2023 ; Revisado: 01.09.2023 Aceptado: 15.12.2023. DOI: https://doi.org/10.12795/anduli.2024.i25.05.

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 94 •

1. Introduction

One of the main goals of companies is to maximize the market value by increasing

the stock price. The economic situation of the countries is of great importance in

determining the stock price of companies. The relationship between the general eco-

nomic situation and stock prices has been of interest to experts for many years. Some

researchers tried to predict the rise or fall in stock prices with the help of economic

indicators. There are many studies1 that reveal the relationship between macroeco-

nomic factors and the stock price.

Theoretically, the value of a rm’s stock should equal the expected present value of

the rm’s future cash ow, and its future cash ow depends on the rm’s performance.

Airline companies face operational, nancial, strategic and unpredictable (hazard)

risks (Zea, 2002, s.2). As airlines’ future cash ows are exposed to these risks, it is

of great importance to manage risks. Airlines have a fragile structure against eco-

nomic uctuations and negativities. Therefore, there are many nancial risk factors

that affect airlines. The most common nancial risks in the literature are; fuel price,

exchange rate, interest rate, liquidity risks (Morrell, 2007; Vasigh, 2015; Budd and

Ison, 2017; Fernando, 2006; Loudon, 2004; Tsai, 2008). Thus, a change in any mac-

roeconomic variable can potentially affect stock prices.

There are many studies in different sectors that reveal the relationship between mac-

roeconomic factors and stock price, and a meaningful relationship can be mentioned.

However, although there are not enough studies on the airline industry, studies in the

literature prove the existence of a relationship between macroeconomic factors and

stock returns. Unsystematic risks in the airline industry can cause many damages to

businesses. The biggest unsystematic nancial risk factor is oil price. Increases in

the prices of jet fuel (which increases in parallel with the price of Brent oil) negatively

affect the stock prices by reducing the protability of airlines (Vasigh, 2015, s. 149;

Morrell and Swan, 2006, s. 3). It is understood that there are signicant relationships

between oil price volatility and stock price, especially in studies conducted in the

transportation sector (Hammoudeh ve Li, 2005; Nandha ve Brooks, 2009; Narayan

ve Sharma, 2014; Aggarwal vd. 2012). In four studies (Loudon, 2004; Tsai, 2008;

Elyasiani et al, 2011; Kristjanpoller & Concha, 2016) conducted in the airline industry,

it was concluded that oil prices have signicant effects on stocks. Airlines generally

report that foreign exchange movements have a negative impact on prots (Morrell,

2007, p. 178). Global airlines often generate revenue in multiple currencies and pay

for fuel, labor and other costs. As such, they are exposed to exchange rate uctua-

tions (Pyke and Sibdari, 2018, p. 293). In the study conducted by Yashodha et al.,

2011, it was concluded that the exchange rate risk is effective on stock prices for

Cathay Pacic and China Airways airlines. While interest rates are not as volatile as

fuel prices or exchange rates, the amounts of debt accrued by global airlines are a

serious risk from adverse changes in interest rates. If market interest rates rise, air-

lines will have to pay higher interest rates. For example, at the end of 2012, American

Airlines had outstanding debts of around $7 billion (Vasigh vd., 2014, s. 491-491).

1% increase in the interest rate would increase American Airlines’ interest expenses

by $70 million. The increase in interest expenses may reduce the protability and

negatively affect the value of the company’s stock. In the study by Tsai, (2008), it was

concluded that the stock price of the South African Airways is affected by the interest

rate. However, in the study of Loudon (2004), no signicant relationship was found

between the interest rate and the stock price for Qantas and Air New Zealand airlines.

1 The literature summary about the studies is available in the annex2-3

Artículos • Abdulkadir Alici

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The subject of this study is the mutual nancial relationships between the stock prices

of airline companies and macroeconomic factors. In this context, the main objective of

the study is to determine the macroeconomic factors affecting the stock prices of air-

line companies around the world. Many studies have been conducted in different sec-

tors examining the relationship between macroeconomic factors and stock prices and

signicant results have been obtained. There are several studies on the airline sector.

In most of these studies, a few (2-3) airline companies were examined in the research

sample. In this study, 14 airline companies were analysed. Therefore, it is thought to

make a great contribution to the literature. Another contribution of this study to the lit-

erature is that Toda-Yamamato (1995) causality test and Hatemi-J (2012) asymmetric

causality test are analysed together. In addition, another point that distinguishes the

study from similar studies is that a large period (2009-2018) is analysed by using daily

data. Thus, it is aimed to obtain more reliable results.

There are many factors affecting the stock prices of airlines. The study investigates

the effect of macroeconomic factors on stock prices. Before the analysis, the relation-

ship between macroeconomic factors and stock price was explained in a concep-

tual framework, and also the relevant literature was addressed. After that, analysis

method was presented. In the analysis part of the study, the ndings were reached

and comments were made about the ndings.

1.1. Conceptual Framework and Literature

The aim of the study is to estimate the relationship between stock price of airlines and

macroeconomic factors. In this context, it is necessary to decipher the conceptual

relationship between stock prices and macroeconomic factors. Macroeconomic fac-

tors used in the study include Brent oil price, Dollar exchange rate, and interest rate.

After the collapse of the Bretton Woods system, almost all countries soon switched

to a oating exchange rate regime, and uctuations in exchange rates began to af-

fect nancial markets. In addition, a large increase in world trade volume and capital

movements has made the exchange rate one of the important determinants of oper-

ating protability and stock prices (Yilmaz et al., 2006, p.5). Sudden ups and downs

in exchange rates negatively affect the capital market and can cause large losses to

businesses and investors (Korkmaz and Ceylan, 2015).

Due to a higher-valued currency, companies can buy the raw materials they need for

production cheaper. This results in reducing the costs of the business and increas-

ing its earnings. In low exchange rate environments, interest rates fall while foreign

exchange reserves and money supply increase. This, in turn, reduces the nancing

costs of businesses. Reductions in nancing costs and prices of imported goods in-

crease future cash ows of businesses (Candan, 2015). Therefore, a decrease in the

exchange rate can raise stock prices.

Most of the revenue generated by airlines is exposed to exchange rate risk. Airlines

sell tickets to many countries and in many currencies. Airline operations in foreing

countries results in an additional exchange risk. For example, revenues from the op-

erations of South Africa Airways operating in Rwanda must be converted into US

dollars and then into the South African currency Rand (Tsai, 2008). Exchange rate

uctuations affect the protibility of airlines. For example, Turkish Airlines ies to

many international routes. Due to exchange rate changes(increase/decrease) in dol-

lar and Euro currencies, there is an increase/decrease in the protability of Turkish

Airlines. Here, currency uctuations and the number of international passengers are

the determining factors affecting protability.

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A rise in oil prices means an increase in raw material prices and production costs.

High production costs lead to a decline in the prot of businesses. This reduces cash

ow in stock markets and leads to a decline in stock returns (Saygan and Süslü,

2011). A rise in oil prices can cause prices to rise in the market, that is, ination. Such

a situation could lead the country’s central banks to increase interest rates in order to

control the increase in the ination rate. High interest rates can cause stock returns to

fall (Basher and Sadorsky, 2006).

Fuel price management is very important for airlines as jet fuel costs include an im-

portant component of airline operating costs. In a study conducted by Koopmasn and

Lieshout (2016), they found that fuel costs were 20% -50% of total costs in 2014.

Therefore, jet fuel price risk makes economic sense for airlines. Likewise, Loudon

(2004) suggested that short-term cash ows may be directly related to changes in fuel

prices due to price change inertia.

One of the key factors affecting stock returns is interest rates. Changes in interest

rates can affect the prospects of expected returns. If interest rates fall, a certain in-

crease in stock prices occurs, as businesses will have the opportunity to nd cheaper

loans (Ercan and Ban, 2008). Based on this, it can be said that changes in the interest

rate have a negative impact on stock prices. An increase in interest rates leads to an

increase in the expected rate of return and a decrease in stock prices. Likewise, a rise

in interest rates increases the opportunity cost of holding cash. In this case, investors

turn to other securities that provide a return on interest, rather than holding capital.

This, in turn, can be reected as a decline in stock returns (Gan et al., 2006). Interest

rates are the most important factor affecting competition between stock market and

the bond market (Kalmanbetova, 2010). When interest rates rise, investors sell eq-

uity investment instruments, preferring alternative securities investment instruments.

Investors, in particular, tend to turn to bond markets (Brigham, 2006; Süslü, 2011).

Interest rate risk is an important factor for airlines, as they borrow heavily for nancing

the purchase of aircraft. High leverage ratios are common in the airline industry due

to capital intensity and relatively high cost of equity. In the airline industry, attracting

capital may be more difcult due to high earnings volatility (Loudon, 2004; Tsai, 2008).

In summary, as airlines borrow heavily, interest rates increase, as well as the interest

cost incurred by airlines increases. Increased interest expenses can reduce prot-

ability and therefore stock prices.

There are many studies in the literature on the subject, except for the airline indus-

try. There are several studies examining this relationship in the airline industry (Lou-

don, 2004; Fernando, 2006; Tsai, 2008; Puncreobutr and Sowaros (2016); Yashodha

(2016)). Details of these studies are given below.

Loudon (2004) investigated the exposure to interest rate, exchange rate and fuel

price risks of Qantas and Air New Zealand, using weekly stock price and independent

variables between 1995-2003. According to linear and nonlinear regression model

analysis, Qantas and Air New Zealand airlines were not exposed to interest rate and

currency risk in the short term, but were negatively exposed to fuel price volatility. In

the long term, it was stated that the incidence of all risks increased.

Fernando (2006) examined 15 airlines’ risk exposures and usage of derivatives to

mitigate these risk exposures specically volatility in jet fuel prices. The data was

obtained from nancial reports of airlines. As a result of the analysis, it was stated

that fuel price risk is the most important risk indicator and that protection instruments

should be used more against this risk.

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Tsai (2008) analysed the impact of nancial risk factors, including interest rate ex-

posures, currency uctuations, and fuel price changes on the airline industry. This

study investigated risk exposures in the South African airline industry and used data

on South African Airways (SAA) and Comair to calculate the impact of risk factors

on exposure signicance. According to the linear and non-linear regression model

results, one of the most prominent ndings of the study is that South African Airways

was affected by all risk factors in all time periods, all risk factors signicantly affected

nancial performance to a certain extent when losses rather than prots were ob-

served. Another important nding is that Comair was exposed to fuel price risk in the

short term.

Puncreobutr and Sowaros (2016) aimed to study the risk factors affecting the low-

cost carrier industry and also to provide guidelines to reduce the risk. The research

was conducted by studying the documents, in depth interviews with personnel at the

airports in Thailand. It was found that the risk affecting the low-cost carriers industry

consists of 2 main factors.

Yashodha et al. (2016) examined relationships between the stock price of Cathay

Pacic Airways and China Airlines against key determinants of nancial risks expo-

sure confronting the airline industry, which include interest-rate, exchange rate and

fuel price risk exposures for the period of January 1996 to December 2011. The study

suggests that exchange rate movements have a substantial impact, compared to the

fuel price and interest rate exposures against the stock price of the analysed airline.

In addition, fuel price, interest rate and exchange rate risks are considered among

the most important risks in the ranking of risks in the airline industry according to IATA

(2017) report.

Thorbecke (2020) investigated the impact of the Covid-19 pandemic on the stock

market in the US and analyzed the effects of macroeconomic factors such as oil

price, exchange rate, interest rate and ination on stocks. As a result of the regres-

sion analysis, it was determined that airline stock prices were driven by idiosyncratic

factors rather than macroeconomic factors.

According to Horobet et al. (2022) in their work, this study explores the impact of oil

price volatility on the stock returns of global airlines, with a focus on the long and

short-term effects of oil price risk on the airline industry. The authors use a macroeco-

nomic framework to analyze various risk factors and employ a panel ARDL model and

PMG estimator to estimate long-term and short-term coefcients for the relationship

between variables. The study nds that oil price volatility has a signicant and nega-

tive impact on airlines’ stock returns, and this impact has particularities when the long

versus short-run perspective is considered. The authors suggest that investors and

stakeholders in the airline industry need to be aware of the potential risks associated

with oil price uctuations and consider them when valuing companies in the stock

market. The study also highlights the need for airline companies to rethink their op-

erational and nancial policies to cope with the challenges posed by oil price volatility,

particularly in the post-pandemic world.

Apart from these studies, there are several studies (Samunderu et al., 2023; Wang

and Gao, 2020; Kang et al., 2021; Felix et al., 2023; Atems, 2021) investigating the

effect of oil price volatility on stock returns in airline companies. Most studies have

found that oil prices negatively affect airline stock prices. It is also stated that it is pos-

sible to get rid of the negative effect with the hedging strategy.

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Within the scope of the literature on the airline industry, it has been observed that oil

price, exchange rate and interest rate variables have signicant relationships with

stock prices in the context of macroeconomic factors. Three studies (Loudon (2004);

Tsai (2008) and Yashodha et al. (2016)) related to macroeconomic factors affecting

the stock prices of airlines were reached. Fernando (2006) and Puncreobutr and So-

waros (2016) determined risk factors in airlines. Examining the literature, there are a

few studies examining the relationship of macroeconomic factors with the stock prices

of airlines. Therefore, further research is needed. There are several studies on the

airline sector. In most of these studies, a few (2-3) airline companies were examined

in the research sample. In this study, 14 airline companies were analysed. In addition,

Toda-Yamamato (1995) causality test and Hatemi-J (2012) asymmetric causality test

are used in the study. Finally, another point that distinguishes the study from similar

studies is that a wide period (2009-2018) is analysed by using daily data. Thus, it is

aimed to obtain more reliable results.

2. Research Methodolgy

The subject of this study is the mutual nancial relations of airlines’ stock prices and

macroeconomic factors. In this context, the main purpose of the study is to determine

the macroeconomic factors that affect the stock prices of airlines. Many studies in dif-

ferent sectors have examined the relationship between macroeconomic factors and

stock price and proved the relationship. There are several studies examining this

relationship in the airline industry (Loudon, 2004; Fernando, 2006; Tsai, 2008; Pun-

creobutr and Sowaros (2016); Yashodha (2016)). In most of these studies, research

sample was composed of a few (2-3) airlines. In this study, 14 airlines were examined.

Therefore, it is thought that it will make great contributions to the literature. Another

contribution of this study to the literature is that the Toda-Yamamato (1995) causality

test and the Hatemi-J (2012) asymmetric causality test were used together. In addi-

tion, another aspect that distinguishes the study from similar studies is the considera-

tion of a wide period (2009-2018) using daily data. Thus, it is aimed to obtain better

results.

The study has three main research questions: (i) What is the impact of exchange rate

changes on global airlines’ stock returns? (ii) What is the impact of changes in oil

prices on global airlines’ stock returns? (iii) What is the impact of interest rate changes

on global airlines’ stock returns? However, is there a signicant difference between

symmetrical and asymmetrical macroeconomic effects? The hypothesis of this study

is that macroeconomic factors (exchange rate, oil price and interest rate) have a sig-

nicant and negative effect on stock returns

In this study, factors affecting airline stock prices were examined. 14 airlines whose

nancial data showed continuity in the period 2009-2018 were included in the study.

Stock price of airlines and macroeconomic data included in the sample were obtained

from the Thomson Reuters Datastream. In the analysis, the VAR model will be esti-

mated and dynamic relationships will be analyzed with Toda and Yamamoto (1995)

causality analysis and Hatemi-J (2012) asymmetric causality analysis.

Toda-Yamamoto causality test, as in traditional causality tests (Granger causality test,

etc.), without depending on cointegration condition (Erbaykal and Okuyan, 2007),

causality can be established and causality analysis with VAR model regardless of

whether the series is stationary or not. It is a test that minimizes the risks that can be

Artículos • Abdulkadir Alici

• 99 •

made and that the series can be wrongly evaluated due to integration (Mavrotas and

Kelly, 2001).

Toda and Yamamoto (1995) causality test is carried out through the Vector Autore-

gressive (VAR) model, which includes the level values of the variables. By deter-

mining the optimal lag length (p) of the VAR model and the maximum degree of

integration (dmax), which is the highest stationarity level of the variables, the VAR

(p+dmax) system is estimated by SUR (Seemingly Unrelated Regression) method.

Then, it is decided to determine the causality with the MWALD test whether the co-

efcients of the p lags in the VAR (p+dmax) system are equal to zero as a group.

(Tandoğan and Genç, 2016). The rejection of the Ho hypothesis, which was estab-

lished as the coefcients are equal to zero as a group, shows that there is a causality

relationship. In the causality analysis between the stock prices of the airlines and the

macroeconomic indicators, logaritms of the stock prices of the companies (LASP), the

currency value of the countries against the dollar (LDER), the brent oil price (LBOP)

and the 10-year bond interest rates (LFAIZ) of the countries were used. Binary VAR

systems consisting of LASP and LDER, LBOP and LFAIZ variables are shown in

equations (4.1), (4.2), (4.3), (4.4), (4.5) and (4.6):

p VAR represents the number of lags in the VAR model, and p+d represents the maxi-

mum degree of integration of the variables included in the model. The basic idea of

this approach is to increase the number of lags in the VAR model by the maximum de-

gree of integration of the variables entering the model (Erbaykal and Okuyan, 2007).

Causality tests, which are frequently used in the literature (Granger, 1969; Hsiao,

1981; Sims, 1972; Hacker and Hatemi, 2006; Toda and Yamamoto, 1995) have cau-

sality results by ignoring positive and negative shocks between the series. Hatemi-J

(2012) asymmetric causality test focuses on the relationship between positive and

negative shocks between series. It was rst suggested by Granger and Yoon (2002)

that this relationship may differ from the relationship between variables. On the other

hand, Hatemi-J (2012) developed the Granger and Yoon (2002) method for causality

analysis. In summary, in the Hatemi-J (2012) asymmetric causality test, it is aimed

to observe the different causal effects of positive and negative shocks and to nd the

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 100 •

hidden structure that will allow for the development of foresight for the future (Yılancı

& Bozoklu, 2014; Contuk & Güngör, 2016).

In the Hatemi-J causality and Toda-Yamamoto causality tests, analyzes are per-

formed by considering the level values of the series. In the Toda-Yamamoto causal-

ity test, symmetrically relationships are analyzed, while in the Hatemi-J causal test,

asymmetric relationships are analyzed. With the Hatemi-J causality analysis method,

it is possible to test whether an increase in one dependent or independent variable

causes an increase/decrease in another variable and/or whether a decrease in any

variable causes a decrease/increase in another variable by separating the positive

and negative shocks of the series (Büberkökü and Şahmaroğlu, 2016).

In the study, independent variables determining stock prices were selected from mac-

roeconomic variables used in the literature. In this context, Brent oil price, interest

rates, and exchange rates were used as independent variables. All variables in the

study were normalized by taking their natural logarithms. Details about the variables

used in the study are listed in Table 1. Stock price of airlines and macroeconomic data

included in the sample were obtained from the Thomson Reuters Datastream.

Table 1. Details About Variables

Variables Symbol Measurement Indicator Measurment Method

Dependent

Variables LASP Airlines Stock Price Reel Stock Price

Inde-

pendent

Variables

LBOP Brent Oil Price Reel Brent Oil Price

LDER Dolar Exchange Rate

Exchange Rate Between the Currency

of the Countries and the US Dollar (Ex:

TL_USD)

LIR Interest Rate 10-Year Bond Interest Rates of

Countries

As a result of the literature review, it is desired to examine the relationship between

the variables in Table 1 above. The hypothesis regarding the related variables is as

follows:

Ho: There is a relationship between macroeconomic indicators (dollar exchange rate,

oil price and interest rate) and stock prices.

H1: There is no relationship between macroeconomic indicators (dollar exchange

rate, oil price and interest rate) and stock prices.

3. RESEARCH FINDINGS

Using the data of this study, the relationship between the stock prices of the airlines

and the macroeconomic factors was investigated using the daily data between 2009

and 2018, and the dynamic relationship between stock price, Dollar exchange rate,

Brent oil price and Interest rate was examined. In the study, the model was estab-

lished by taking the logarithm of all variables. In the analysis, the VAR model will

be estimated and dynamic relationships will be analyzed with Toda and Yamamoto

(1995) causality analysis and Hatemi-J (2012) asymmetric causality analysis.

In order to achieve more accurate causality results in the Toda-Yamamoto causality

test, it is necessary to correctly determine the delay length and maximum integration

level of the variables in the established model. In the study, the integration levels of

Artículos • Abdulkadir Alici

• 101 •

the series were determined using the Augmented Dickey Fuller unit root test. Delay

length of series was determined using SC, HQ, FPE, LR and ACI information criteria.

When the series of all airline companies are examined according to the unit root test

results, it has been determined that the independent variables are not stationary at

the level, and they become stationary when the rst difference of the series is taken2.

In the Toda-Yamamoto causality test, the series are included in the analysis without

the need to make them stationary. This allows series of variables to have more in-

formation and thus obtain better results from analysis (Çil Yavuz, 2006, p. 169). In

Hatemi-J (2012) asymmetric causality analysis, the analysis was performed by taking

the rst differences of the variables. The study rstly gives the results of the Toda-

Yamamoto (1995) causality test. Only signicant relationships are shown in the table.

All Toda-Yamamoto Causality Analyzes are annex.

Table 2. Toda-Yamamoto Causality Analysis

Airlines Direction of Causality x2Stat Var (p+d) Probability

Value

United Airlines LASP ⇏ LDER* 5.454473 2+1 0.0654

Turkish Airlines

LDER ⇏ LASP 23.00338 5+1 0.0003

LBOP LASP 12.72166 5+1 0.0261

LASP ⇏ LIR 12.28137 5+1 0.0311

Singapore

Airlines

LASP ⇏ LDER* 4.613838 2+1 0.0996

LDER ⇏ LASP 14.28926 2+1 0.0008

Qantas Airways

LDER ⇏ LASP* 19.15354 11+2 0.0584

LBOP ⇏ LASP 22.47271 11+2 0.021

Lufthansa

Airlines

LDER ⇏ LASP 12.08459 4+1 0.0167

LASP ⇏ LBOP* 9.412552 4+1 0.0516

LBOP ⇏ LASP 14.71976 4+1 0.0053

LASP ⇏ LIR 10.41648 4+1 0.034

Air China

LDER ⇏ LASP 8.574296 2+1 0.0137

LBOP LASP 11.20648 2+1 0.0037

Aeroot LDER ⇏ LASP 22.90321 10+2 0.0111

Air Canada LBOP LASP 8.734665 2+1 0.0127

2 Related unit root test results are included in the annex-4

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

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Airlines Direction of Causality x2Stat Var (p+d) Probability

Value

Easyjet

LDER ⇏ LASP 11.57768 2+1 0.0031

LIR ⇏ LASP 7.755625 2+1 0.0207

Gol Linhas

Aeras

LDER ⇏ LASP 9.666582 2+1 0.008

JetBlue No signicant result could be reached.

Norwegian

LASP ⇏ LDER 4.622919 2+1 0.0991

LDER ⇏ LASP 5.914692 2+1 0.052

Southwest

LASP ⇏ LBOP 7.848946 2+1 0.0198

LBOP ⇏ LASP* 5.423005 2+1 0.0664

Westjet LBOP ⇏ LASP* 13.0928 8+1 0.0987

Note1: The ⇏ sign indicates the null hypothesis that there is no causality. In the estimated VAR

model, different lag lengths (p) were used, which were determined as the most appropriate accord-

ing to LR, FPE, ACI, SC and HQ criteria. Note 2: The causalities marked with * are those at the 10%

signicance level.

In order to investigate the causality relationship between stock price and macroeco-

nomic (brent oil, interest, dollar exchange rate) variables, a Toda Yamamoto (1995)

causality test was performed for all airlines separately, and the ndings are shown in

Table 3. Analysis comments were made by evaluating all airlines together. A holistic

causality interpretation was made in the context of the variable.

As a result of the analysis of the relationship between the dollar exchange rate varia-

ble (LDER) and the stock price variable (LASP), it was found that the dollar exchange

rate was the cause of the stock prices for 9 airlines (Turkish Airlines, Singapore Air-

lines, Qantas Airways, Lufthansa Airlines, Air China, Aeroot, Easyjet, Gol Linhas

Aeras and Norwegian). It has been observed that there is bidirectional causality for

Norwegian and Singapore Airlines. Accordingly, changes in exchange rate can have

a signicant impact on the stock prices of airlines.

As a result of the analysis of the relationship between the Brent oil price variable

(LBOP) and stock price variable (LASP), it was found that the Brent oil price variable

was the cause of the stock prices for 7 airlines (Turkish Airlines, Qantas Airways,

Lufthansa Airlines, Air China, Air Canada, Westjet, and Southwest). It has been ob-

served that there is a bidirectional causality relationship for Southwest and Lufthansa

Airlines. In this context, changes in brent oil prices could have signicant effects on

airline stock prices.

As a result of the analysis of the relationship between the 10-year bond interest rate

variable (LFAIZ) and the stock price variable (LASP), it has been determined that the

interest rate variable is the cause of the stock price variable in only 1 airline (Easyjet).

Accordingly, we can say that interest rates do not have a signicant impact on airline

stock prices.

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Examining asymmetric causality in cases where symmetric causality cannot be ex-

plained is important in terms of revealing possible relationships between variables.

Hatemi-J (2012) asymmetric causality test ndings are given in Table 4. In Table 4,

only the ndings (variables) with causality are given. All asymmetric causality test

results are included in the annex.

Table 3. Hatemi-J (2012) Asymmetric Causality Test Results

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

United Airlines

LASP- ≠ > LDER- 3.777* 7,089 3,952 2,751

LDER- ≠ > LASP+ 5.525** 6,934 3.88 2,754

LASP- ≠ > LBOP+ 5.089** 6,805 4,001 2,839

LBOP+ ≠ > LASP+ 4.109** 6,847 3,886 2,785

LBOP+ ≠ > LASP- 4.932** 7,149 4,033 2.68

LASP- ≠ > LIR- 3.777* 7,131 4,027 2,728

LIR+ ≠ > LASP+ 4.109** 6,847 3,886 2,785

LIR+ ≠ > LASP- 4.932** 7,149 4,033 2.68

Turkish

Airlines

LASP+ ≠ > LDER+ 5.641** 6,505 3,846 26,330

LASP- ≠ > LDER- 37.128*** 9,482 6,016 4,679

LDER+ ≠ > LASP- 13.245*** 6,607 3,968 2.68

LASP- ≠ > LBOP- 4.509** 4.51 3,842 2,753

LASP+ ≠ > LIR+ 15.025*** 13,804 9,569 7,864

LIR+ ≠ > LASP+ 13.368** 14,051 9,456 7,645

LIR- ≠ > LASP- 19.144*** 9,954 6,128 4,726

LIR- ≠ > LASP+ 7.235* 15,147 9,597 7,736

Singapore

Airlines

LASP- ≠ > LDER- 22.330*** 9,948 43,714 4,562

LDER- ≠ > LASP- 13.059** 13,317 8,061 6,406

LDER- ≠ > LASP+ 3.539* 6,819 3,892 2,758

LASP+ ≠ > LBOP+ 8.135*** 6,914 3,957 2,765

LASP- ≠ > LBOP- 2.931* 6.96 3.86 2,657

LBOP- ≠ > LASP- 3.799** 7,161 3,804 2,596

LIR- ≠ > LASP+ 2.838* 8,253 3,594 2,339

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Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Qantas

Airways

LASP- ≠ > LDER+ 2.786* 8,417 3.56 2,393

LASP+ ≠ > LBOP+ 22.021*** 9,002 5,961 4,582

LASP- ≠ > LBOP- 24.621*** 15,124 9,745 7,806

LASP- ≠ > LBOP+ 8.201*** 7,055 3,784 2,762

LBOP- ≠ > LASP- 7.392* 14,828 9,466 7,269

LBOP+ ≠ > LASP- 5.937** 7,137 3,927 2,642

LASP+ ≠ > LIR+ 3.147* 6,976 35,125 2,728

LIR- ≠ > LASP+ 16.062*** 14,578 9,797 7,717

Lufthansa

Airlines

LASP- ≠ > LDER- 4.633** 6,702 4,042 2,771

LASP- ≠ > LDER+ 6.181** 6,565 3,785 2,687

LASP+ ≠ > LDER- 4.215** 6,541 3,749 2,638

LASP- ≠ > LBOP+ 2.984* 6,337 3,736 2,682

LBOP+ ≠ > LASP- 5.383** 6,944 24,532 2,655

LASP- ≠ > LIR- 3.041* 6,871 4.00 2.80

LIR- ≠ > LASP+ 20.236*** 11,527 7,776 6,163

Air China

LDER+ ≠ > LASP+ 2.749* 6,711 3,769 2,661

LDER+ ≠ > LASP- 3.226* 6,776 3,821 2,704

LASP- ≠ > LBOP- 4.318** 7,038 3,817 2,618

LBOP+ ≠ > LASP+ 3.727* 6,765 3,953 2,762

LBOP- ≠ > LASP- 4.155** 6,944 3,676 2,601

LIR+ ≠ > LASP+ 7.707** 9,567 6,261 4,771

Aeroot

LASP- ≠ > LDER- 5.869* 10,001 5,958 4,491

LASP+ ≠ > LDER- 10.903*** 9.72 6,256 4,563

LDER- ≠ > LASP+ 2.574* 6,997 3,945 2.53

LASP- ≠ > LBOP+ 4.612** 7,157 3,862 2,646

LASP+ ≠ > LBOP- 4.530** 7,036 4,125 2,737

LASP+ ≠ > LIR+ 34.060*** 26,365 16.97 13,778

LASP- ≠ > LIR+ 4.564** 7,634 3,792 2,572

LASP+ ≠ > LIR- 7.258*** 7,199 3,603 2,499

LIR+ ≠ > LASP+ 18.413** 23,567 16,782 13,796

LIR- ≠ > LASP- 3.058* 7,162 3,804 2,623

LIR- ≠ > LASP+ 27.380*** 21,663 16,031 13,538

LIR+ ≠ > LASP- 41.742*** 24,123 16,821 13,878

Artículos • Abdulkadir Alici

• 105 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Air Canada

LASP- ≠ > LBOP- 4.318** 6,623 3,955 2,736

LBOP+ ≠ > LASP+ 3.727* 6,765 3,953 2,762

LBOP- ≠ > LASP- 4.155** 6,944 3,676 2,601

EasyJet

LASP+ ≠ > LDER+ 2.766* 6,892 3,821 2,688

LASP- ≠ > LDER- 13.416*** 6,861 3.95 2,753

LDER- ≠ > LASP+ 5.312** 6,662 3,938 2,747

LASP- ≠ > LBOP- 2.773* 6,663 3,795 2,527

LBOP+ ≠ > LASP+ 10.269*** 6,715 3,622 2,556

LIR- ≠ > LASP+ 2.480* 7,263 3,776 2.42

Gol Linhas

Aeras

LASP- ≠ > LDER- 8.732*** 6,882 3,828 2,641

LASP+ ≠ > LDER- 7.050*** 6.91 3.90 2.66

LDER- ≠ > LASP+ 6.053** 6,351 3,629 2,611

LASP- ≠ > LBOP+ 3.979** 6,681 3,915 2,675

LASP+ ≠ > LIR- 5.293** 7,185 4,083 2,803

LIR+ ≠ > LASP+ 3.738* 7.40 3.98 2.76

Jetblue

LASP- ≠ > LDER- 9.698*** 6,923 3,875 2,713

LDER- ≠ > LASP- 2.736* 6,618 3,762 2,681

LDER- ≠ > LASP+ 14.209*** 6,321 3,773 2,701

LASP+ ≠ > LBOP+ 2.974* 7,338 3,731 2,664

LASP- ≠ > LBOP+ 6.998** 7,509 4,223 2,949

LBOP+ ≠ > LASP+ 5.290** 7,198 3,893 2,765

LBOP- ≠ > LASP+ 4.757** 7,073 4,039 2,781

LASP- ≠ > LIR- 6.329** 7,401 4,053 2,717

LASP- ≠ > LIR+ 13.986*** 10,377 6,033 4.48

LASP+ ≠ > LIR- 8.513** 9,511 6,006 4.63

LIR- ≠ > LASP+ 9.916*** 6,642 3,941 2,702

Norwegian

LASP- ≠ > LDER- 12.904*** 6,205 3,576 2,625

LDER- ≠ > LASP+ 5.777** 7,076 3,868 2.71

LBOP- ≠ > LASP+ 5.598* 9.70 6,214 4,547

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 106 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Southwest

LDER- ≠ > LASP- 10.894*** 7,265 3,892 2.66

LDER- ≠ > LASP+ 7.986*** 6,692 3,756 2,727

LASP- ≠ > LBOP+ 4.616** 7.01 3.77 2.65

LBOP- ≠ > LASP- 4.335** 7,075 4,048 2,776

LIR- ≠ > LASP+ 47.711*** 15,775 11,199 9,275

LIR+ ≠ > LASP- 12.711** 15.78 11,492 9,384

WestJet LASP+ ≠ > LDER+ 2.521* 7,438 3,621 2,391

LBOP+ ≠ > LASP+ 7.184*** 68,616 3,957 2,807

Note: The ⇏ sign indicates the null hypothesis of no causality. *,** and *** values indicate that the

test statistic is signicant at 10%, 5% and 1% signicance levels, respectively. The optimal lag

length was decided according to the HJC information criterion. Bootstrap count is 10,000

Hatemi-J (2012) asymmetric causality test was applied to all airlines in order to in-

vestigate the causality relationship between stock price and macroeconomic (brent

oil price, interest rate, dollar exchange rate) variables, and the ndings are shown in

Table 5.7. Analysis comments were made by evaluating all airlines together. A holistic

causality interpretation was made in the context of the variable.

According to the test results on the dollar exchange rate, a causality relationship be-

tween negative shocks in the dollar rate and positive shocks in the stock price was de-

termined for 8 Airlines (United Arlines, Singapore, Aeroot, Easyjet, Gol Linhas Aeras,

Jetblue, Norwegian and Southwest). It has been determined that there is a causality

relationship from positive shocks in the dollar exchange rate to positive shocks in

the stock price for Air China. It was concluded that there is a causality relationship

from positive shocks in the dollar exchange rate to negative shocks in the stock price

for 2 airlines (Turkish Airline, Air China), while there is a causality relationship from

negative shocks in the dollar exchange rate to negative shocks in the stock price for

3 airlines (Singapore, Jetblue and Southwest). Based on the analysis results, there

was a signicant relationship between the dollar exchange rate and the stock price,

regardless of the positive or negative shocks, for 10 airlines. Based on this, it can be

said that there are signicant relationships between the dollar exchange rate and the

stock price, especially negative changes in the dollar exchange rate positively affect

the stock prices in airlines.

According to the ndings on the Brent oil price variable, a causality relationship was

determined from positive shocks in Brent oil prices to positive shocks in stock price

for 6 Airlines (United Airlines, Air China, Air Canada, Easyjet, Jetblue, and Westjet). A

causality relationship was found from negative shocks in Brent oil prices to negative

shocks in stock prices for 5 Airlines (Singapore, Qantas, Air China, Air Canada, and

Southwest). A causality relationship was determined from negative shocks in Brent

oil prices to positive shocks in stock prices for two airlines (Jetblue and Norwegien).

A causality relationship was found from positive shocks in Brent oil prices to negative

shocks in stock prices for 3 airlines (United Airlines, Lufthansa, and Qantas Airways).

Based on the analysis results, there was a signicant relationship between the oil

price and the stock price, regardless of the positive or negative shocks, for 10 airlines.

Artículos • Abdulkadir Alici

• 107 •

Based on this, it can be said that there are signicant relationships between the oil

price and the stock price, especially positive changes in the oil price positively affect

the stock prices in airlines. Similarly, decreasing oil prices negatively affects the share

prices of airlines.

According to the ndings on the interest rate variable, a causality relationship was

determined from positive shocks in interest rate to positive shocks in stock price for 4

Airlines (United Airlines, Air China, Turkish Airlines, and Aeroot). A causality relation-

ship was found from negative shocks in interest rate to negative shocks in stock price

for two airlines (Turkish Airlines and Aeroot). A causality relationship was found from

negative shocks in interest rate to positive shocks in stock price for 8 airlines (Turkish

Airlines, Singapore, Qantas Airways, Lufthansa, Jetblue, Qatar Airways, easyJet, and

Southwest). A causality relationship was determined from positive shocks in interest

rate to negative shocks in stock price for 3 airlines (United Airlines, Qatar Airways,

and Southwest). Based on the analysis results, there was a signicant relationship

between the interest rate and the stock price, regardless of the positive or negative

shocks, for 10 airlines. Based on this, it can be said that there are signicant relation-

ships between the exchange rate and the stock price, especially negative changes in

the dollar exchange rate positively affect the stock prices in airlines.

According to the results of Toda and Yomamato (1995) causality test, it was found

that there was a signicant relationship between variables of the dollar exchange rate

and oil price and stock prices of airlines. A signicant relationship was not determined

between interest rate and stock price, except for one airline.

According to the Hatemi-J (2012) asymmetric causality test results, signicant rela-

tionships were determined between variables of the dollar rate, oil price, and interest

rate and stock prices of airlines. According to the results of the asymmetric causality

test, in particular, decreasing the dollar rate, increasing Brent oil prices, and decreas-

ing interest rates positively affect the stock prices of airlines.

4. CONCLUSIONS AND RECOMMENDATIONS

The subject of this study is the mutual nancial relations of airlines’ stock prices and

macroeconomic factors. In this context, the main purpose of the study is to determine

the macroeconomic factors that affect the stock prices of airlines.

The change in stock prices and the development of the market vary depending on the

decisions taken by investors. The decisions taken by the investors are determined

according to the performance of the enterprises, the economic situation of the coun-

try, political developments and changes in macroeconomic factors. Determining what

these factors are and revealing their power to inuence stock prices is extremely

important for the formation of prices and investment decisions. In this direction, three

macroeconomic factors that may have a high impact on stock prices of airlines have

been studied. These are the Brent oil price, dollar exchange rate and interest rate.

In application, the relationship between the daily macroeconomic data of 14 airlines

covering the years 2009-2018 and the stock prices of airlines was analyzed with the

Toda Yomamato causality and Hatemi-J asymmetric causality tests, and signicant

relationships were determined regarding the macroeconomic factors determining the

stock prices.

According to the results of Toda and Yomamato (1995) causality test, it was found

that there was a signicant relationship between variables of the dollar exchange rate

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 108 •

and oil price and stock prices of airlines. A signicant relationship was not determined

between interest rate and stock price, except for one airline. According to the results

of Hatemi-J (2012) asymmetric causality test, it was found that there were signicant

relationships between variables of the dollar exchange rate, oil price, and interest

rate, and airline stock prices.

According to the results of both causal analyses, it can be said that there are signi-

cant relationships between the DER variable and the stock price, especially negative

changes in the dollar exchange rate positively affect the stock prices in airlines. It is

understood that uctuations in the exchange rates are reected in the stock price of

airlines. As a result of the analysis, it was found that falls in the dollar exchange rate

in 8 airlines led to an increase in stock prices. The result conrms the theory of the

inverse relationship between the exchange rate and the stock price. Given the impact

of exchange rate changes on the stock price, hedging strategies can be used more

effectively to optimize the costs caused by exchange rates. Countries with high import

rates, especially those that import with foreign currency, are very affected by uctua-

tions in the exchange rate. In this context, most of the airlines buy jet fuels in dollar.

The increase in the dollar exchange rate indirectly increases the cost of jet fuels. In

the opposite case (a decrease in the dollar exchange rate), fuel costs decrease. De-

creasing jet fuel costs will have a positive impact on nancial performance. The result

of the analysis conrms the thesis.

According to the results of both causal analyses, it was found that there were signi-

cant relationships between the BOP variable and the stock price. In line with the hy-

pothesis, 5 airlines have been identied that the increase or decrease in oil prices is

inversely related to stock prices. But contrary to this conclusion and inconsistent with

the theory, 9 airlines have been identied that the increase or decrease in oil prices

is directly related to stock prices. According to these two results, it was observed

that changes in oil prices affect stock prices differently. According to the rst result,

5 airlines can minimize the increase in fuel costs by implementing effective hedging

strategies or operating fuel-efcient aircraft. According to the second result, the exist-

ence of a linear relationship between oil prices and stock price indicates that 9 airlines

effectively implement hedging strategies and/or control fuel costs. Studies (Carter et

al., 2006; Morrell and Swan, 2006; Treanor et al. 2014 and Hanninen, 2017) on the

efciency of fuel hedging strategy support this. Further studies may elaborate on the

fuel hedging performance of these 9 airlines.

According to the results of the Hatemi-J asymmetric causality test, it was determined

that the interest variable is the cause of stock prices. In line with the hypothesis,

a causality relationship from negative shocks in interest rates to positive shocks in

stock prices was found for 8 airlines. In summary, it can be said that the share price

of airlines increases when interest rates fall. This result is also consistent with the

theory. Reductions in interest rates can also increase liquidity, as well as reduce inter-

est expenses of airlines. At the same time, with a decrease in interest rates, airline

companies can benet from cheap loans. The availability of cheap credit positively af-

fects protability and stock value. Financing aircraft purchase is the biggest nancial

burden for airlines. Airlines borrow heavily during the purchase or leasing of aircraft.

As a result of decreasing interest rates, capital costs will also fall, and as a result,

airlines’ stock prices will increase.

As a result of the analysis, the result that negative changes in the dollar exchange

rate positively affect the stock prices of airline companies supports the studies in the

literature (Tsai, 2008 and Yashodha, 2016). The result that an increase or decrease in

Artículos • Abdulkadir Alici

• 109 •

oil prices has an inverse relationship with stock prices supports the studies in the lit-

erature (Loudon, 2004; Fernando, 2006 and Tsai, 2008). Finally, the nding that there

is a causality relationship from negative shocks in interest rates to positive shocks in

stock prices is similar to the study in the literature (Tsai, 2008).

In summary, this study; provides evidence of the volatility of oil prices, exchange rate

and interest rate changes on global airlines, both at the rm level and collectively.

There are several policy implications for airlines, practitioners, policy makers and

investors to manage the relevant macroeconomic risks. First, as the importance of

oil prices to the airline industry emerges, airline managers, private and institutional

investors should pursue policy uncertainty, assuming oil price uncertainty is a driving

force in stock returns. However, airlines should review their nancial hedging strate-

gies against fuel price risk. In addition, the innovation and adoption of alternative

fuel technology by the aviation and airline industry can be a successful alternative

to oil price risk. Exchange rate changes and interest rate mismatches always lead to

variable earnings. The results on changes in exchange rate and interest rate indicate

that airlines and related governments should focus on policies that will increase the

growth of the aviation industry. In order to better manage these risks, nancial manag-

ers need to more carefully examine the effects of macroeconomic risk increases and

changes in related stock prices. Finally, it is thought that the ndings obtained as a

result of the study will contribute to the determination of the factors affecting the stock

prices of the airline companies, to determine the variables that determine the invest-

ment decisions and stock prices. In addition, it is thought that it will provide positive

contributions to the nancial performance of airline companies by bringing new solu-

tions to airline companies in the context of maximizing stock prices.

The study has some limitations that will affect the research results. The rst of these

is the limitation in the number of samples, although the sample of the research is the

largest compared to other studies. Another limitation is the frequency and duration

of the statistical data used in the research. Another limitation is the limited number

of independent variables. It is thought that conducting more frequent and long-term

studies with more samples (airline operators) and variables in future studies will yield

more accurate results. In addition, the effectiveness of hedging methods related to

changes in exchange rates and interest rates, especially oil prices, can be measured

by quantitative and qualitative study methods.

Funding: This research received no external funding.

Acknowledgments: This study was produced from the PhD Thesis named “Analysis

of Factors Affecting Stock Prices in Airline Businesses”.

Conicts of Interest: The author declares no conict of interest.

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versidad de Sevilla. Es un artículo publicado en acce-

so abierto bajo los términos y condiciones de la licencia

“Creative Commons Atribución-No Comercial-Sin Derivar

4.0 Internacional.

Artículos • Abdulkadir Alici

• 113 •

Annexes

Annex 1: List of Airlines

Airlines Sample

Turkish Airlines Southwest Airlines

United Airlines Easyjet

Air Canada Norwegian

Singapore Airlines GOL Linhas Aeras

Qantas Airlines Westjet

Lufthansa Jetblue

Air China Aeroot

Annex 2: The Relationship Between Macroeconomic Factors

and Stocks (International Literature)

Study Period / Country Method Findings

Aggarwal (1981) USA (1974-1978) Basic Regression

Analysis

Exchange rate ⇏

Stock price

Solnik (1987)

(USA, Japan, Ger-

many, UK, France

Canada etc.)

Multiple linear regres-

sion analysis

Exchange rate ⇏ (+)

Stock price

Kwan and Shin (1999) South Korea Stock Ex-

change (1980-1992)

VEC

Model-Cointegration

Exchange rate ⇏

Stock price

Sadorsky (1999) USA (1974-1976) VAR Model Oil price ⇏ Stock

price

Nasseh and Strauss

(2000)

6 European Countires

(1962-1995)

Regression

Analysis-Johansen

Cointegration

Interest rate ⇏ Stock

price

Papapetrou (2001) Yunanistan

(1989-1996)

Multiple linear regres-

sion analysis – VAR

Model

Oil price ⇏ (-) Stock

price

Wongbangpo and

Sharma (2002)

5 Far Eastern Coun-

tries (1985-1996)

VAR Model-Granger

Cuasality (VECM)

Interest rate and

Exchange rate ⇏

Stock price

Gan et al. (2006) New Zeland

VAR Model –Coin-

tegration- Impulse-

Response

Interest rate and

Exchange rate ⇏

Stock price

Humpe and Macmillan

(2007) Japon VAR

Model-Cointegration

Interest rate ⇏ (-)

Stock price

Malik and Hammou-

deh (2007)

USA, Bayrain, Kuwait

and Saudi Arabia

(1992-2001)

GARCH Model Oil price ⇏ Stock

price

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Study Period / Country Method Findings

Ratanapakorn and

Sharma (2007) USA (1975-1999) VAR-Granger

Causality

Interest rate ⇏ (-)

Stock price

Exchange rate ⇏ (+)

Stock price

Brahmasrene and

Jiranyakul (2008)

Thailand stock market

(1992-2003)

VAR Model- Cointe-

gration and Granger

Causality

Oil price and Ex-

change rate ⇏ (-)

Stock price

Rey (2008) India VAR Model-Granger

Causality

Exchange rate ⇏ (+)

Stock price

Park and Ratti (2008) USA and13 European

Countries (1986-2005) VAR Model Oil price ⇏ Stock

price

Humpe and Macmillan

(2009)

USA ve Japan Stock

Markets (1968-2008) Regression Analysis Interest rate ⇏ (-)

Stock price

Arfaoui et al. (2010)

13 developed and

developing countries

(1986-2008)

Regression ECM

Model

Oil price and Ex-

change rate ⇏ Stock

price

Sohail (2010) Pakistan (1991-2008) VAR model

Interest rate ⇏ (-)

Stock price

Exchange rate ⇏ (+)

Stock price

Rasiah and Rat-

neswory (2010) Malaysia (1980-2006) Multivariate VAR

Model

Exchange rate ⇏ (+)

Stock price

Shubita and Al

Sharkas (2010) USA and Japan VAR Model –VECM Interest rate ⇏ (-)

Stock price

Hsing (2011) Czech stock market

(2001-2009) GARCH Model Interest rate ⇏ Stock

price

Kuwornu and Victor

(2011)

Gana Stock market

(1992-2008)

Box-Jenkins ve EKK

Regression Model

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Oil price ⇏ Stock

price

Kavklar and Festic

(2011)

27 European Coun-

tries (2004-2010)

Model-based recursi-

ve partitioning mod.

Interest rate ⇏ (-)

Stock price

Exchange rate ⇏ (+)

Stock price

Aloui et al. (2012) 25 Developing Coun-

tries (1997-2007) Regression model Oil price ⇏ Stock

price

Khan and Amanullah

(2012)

Pakistan Kara-

chi Stock Market

(34 Companies)

(2000-2009)

Linear Multiple

Regression

Interest rate ⇏ (+)

Stock price

Araori et al. (2012) European Stock

Mrkets VAR-GARCH Oil price ⇏ Stock

price

Scholtnes and Yurtse-

ver (2012)

European Area (38

Industry)

Dynamic VAR and

Linear Multiple

Regression

Oil price ⇏ (-) Stock

price

Li et al. (2012) China (13 Industry

Indexes) (2001-2010)

Panel Cointegration-

Granger Causality

Oil price ⇏ (+) Stock

price

Artículos • Abdulkadir Alici

• 115 •

Study Period / Country Method Findings

Talla (2013) Sweden Linear Multiple

Regression

Exchange rate ⇏ (-)

Stock price

Degiannakis vd.

(2013)

European industrial

sector (1992-2010) Correlation Method Oil price ⇏ Stock

price

Mollick and Assefe

(2013) USA (1999-2011) GARCH

– DCC-GARCH

Oil price ⇏ (-) Stock

price (before 2008)

Oil price ⇏ (+) Stock

price (after 2008)

Abdelbaki (2013) Bahrain (1990-2007) Autoregressive Dis-

trubed Lag Model

Interest rate ⇏ Stock

price

Kabir et al. (2014) Malaysia (1991-2010) VAR- VECM Model

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Zhu et al. (2014) Asya-Pacic(10 Ülke)

(2010-2012)

Conditional and

Unconditional Copula

Model

Oil price ⇏ Stock

price

Narayan and Sharma

(2014)

NYSE USA

(2000-2008) GARCH Oil price ⇏ (-) Stock

price

Khalfaoi et al. (2015) G-7 Countries

(2003-2012) BEKK -GARCH Oil price ⇏ Stock

price

Arshad et al. (2015)

Karaçi (Pakis-

tan) Stock Market

(2007-2013)

Linear regression

analysis

Interest rate ⇏ (-)

Stock price

Utami et al. (2015)

Indonesian cons-

truction industry

(2010-2014)

Panel Data Analysis Interest rate ⇏ (-)

Stock price

Bukulu (2016) Uganda MKB

(2007-2014)

Multiple regression

analysis

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Linck and DEcourt

(2016) Brazil (2000-2010) Stepwise multiple

regression analysis

Interest rate ⇏ Stock

price

Jareno and Negrut

(2016) USA (2008-2014) Quantile Regression

analysis

Interest rate ⇏ (-)

Stock price

Subing (2017) Indonesian-18 Com-

panies (2008-2015) Panel Data Analysis Oil price ⇏ (+) Stock

price

Malik vd. (2018) Pakistan, India and Sri

Lanka (1997-2014) Panel GMM Approach Exchange rate ⇏ (+)

Stock price

Chandrosheker et al.

(2018)

India and Brazil

(2000-2016) Panel Data Analysis Exchange rate ⇏ (+)

Stock price

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

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Annex 3: The Relationship Between Macroeconomic Factors

and Stocks (National Literature)

Study Period / Country Method Findings

Durukan (1999) BIST100 (1986-1998)

Percentage change

model and natural

logaritma

Interest rate ⇏ Stock

price

Albeni and Demir

(2005)

ISE Financial Industry

(1991-2000)

Multiple regression

analysis

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Yılmaz et al. (2006) ISE (1990-2003) EKK, VEC Model

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Gençtürk (2009) ISE (1992-1996) Multiple linear regres-

sion analysis

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Süslü (2010) 11 Developing Counti-

res (1999-2006) Panel data analysis

Interest rate ⇏ Stock

price

Exchange rate ⇏

Stock price

Oil price ⇏ Stock

price

İşçan (2010) BIST100 (2001-2009) VAR Model Oil price Stock price

Sayılgan and Süslü

(2010)

Developing Countires

(1999-2006)

Balanced panel data

analysis

Exchange rate ⇏

Stock price

Büyükşalvarcı and

Abdioğlu (2010) BIST100 (2001-2010) VAR Model – Toda-

Yomamato Causality

Exchange rate ⇏

Stock price

Karacaer and

Kapusuzoğlu (2010) BIST100 (2003-2010) VAR – Causality,

Cointegration

Exchange rate ⇏

Stock price

Soytaş and Oran

(2011)

BIST100 Electric

Industry VAR - EGARCH Oil price ⇏ Stock

price

Ayaydın and Dağlı

(2012)

22 Developing

Countries Panel data analysis Exchange rate ⇏ (+)

Stock price

Öztürk et al. (2013)

BIST100 (Manufac-

turing, Chemistry and

Petroleum)

VAR Model Oil price ⇏ Stock

price

Şener et al. (2013) BIST100 (2002-2012) VAR Model Oil price ⇏ Stock

price

Yıldırım et al. (2014) BIST Industrial

(1991-2013) VAR Model Oil price ⇏ (+) Stock

price

Kılıç et al. (2014) BIST Industrial

(1994-2013)

Gregory Hansen

Dynamic EKK

Oil price ⇏ (+) Stock

price

Abdioğlu and

Değirmenci (2014) BIST100 (2005-2013) VAR Model Oil price ⇏ Stock

price

Artículos • Abdulkadir Alici

• 117 •

Study Period / Country Method Findings

Doğru (2015) European, MIST and

BRICS (2002-2014) VAR - EGARCH Oil price ⇏ Stock

price

Güngör and Kaygın

(2015)

BIST Manufacturing

(2005-2011)

Dynamic Panel Data

Analysis

Exchange rate ⇏ (+)

Stock price

Oil price ⇏ (+) Stock

price

Poyraz and Tepel

(2015) BIST100 (1995-2011) Multiple linear regres-

sion analysis

Exchange rate ⇏ (-)

Stock price

Interest rate ⇏ (-)

Stock price

Altınbaş et al. (2015) BIST100 (2003-2012) VAR Model Exchange rate ⇏ (-)

Stock price

Coşkun and Ümit

(2016) BIST100 (2005-2015) VAR Causality Exchange rate ⇏

Stock price

Kendirli and Çankaya

(2016)

BIST Bank

(2009-2015) VAR Model Exchange rate ⇏ (-)

Stock price

Sancar et al. (2017) BIST100 (2000-2016) DOLS and FMOLS

Analysis

Exchange rate ⇏ (-)

Stock price

Sadaghzadeh and

Elmas (2018)

BIST Commercial

(2000-2017) Panel Data Analysis

Exchange rate ⇏ (-)

Stock price

Interest rate ⇏ (-)

Stock price

Demir (2019) BIST100 (2003-2017) ARDL Approach

Exchange rate ⇏ (+)

Stock price

Oil price ⇏ (-) Stock

price

Annex 4: Causality Analysis Unit Root Test Results

Airlines Variables

ADF-Test

Statistics(Level)

ADF-Test Statistics(1.

difference)

Constant Constant

and Trend Constant Constant

and Trend

United Airlines

LASP -1.21397

(0,6706)

-2.50774

(0.3243)

-48.5567

(0.0001)

-48.5474

(0.0000)

LIR -2.21632

(0.2006)

-2.30433

(0.4309)

-54.133

0.0001

-54.1228

0.0000

LDER -1.68376

(0.4395)

-2.47218

(0.3421)

-51.9482

(0.0001)

-51.9384

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

(0.4110)

-54.026

(0.0001)

-54.058

(0.0000)

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 118 •

Airlines Variables

ADF-Test

Statistics(Level)

ADF-Test Statistics(1.

difference)

Constant Constant

and Trend Constant Constant

and Trend

Turkish Airlines

LASP -0.95011

(0.7725)

-1.79606

(0.7067)

-55.3598

(0.0001)

-55.3518

(0.0000)

LIR -1.47155

(0.5482)

-0.98859

(0.9439)

-60.1277

(0.0001)

-60.1784

(0.0000)

LDER 1.059713

(0.9972)

-2.2711

(0.4492)

-28.7112

(0.0000)

-28.7814

(0.0000)

LBOP -1.53399

(0.5164)

-1.95498

(0.625)

-59.9069

(0.0001)

-59.9124

(0.0000)

Singapore Airlines

LASP -2.08937

(0.2491)

-2.99029

(0.1351)

-52.9474

(0.0001)

-52.9479

(0.0000)

LIR -2.70664

(0.073)

-2.71232

(0.2316)

-60.3848

(0.0001)

-60.3734

(0.0000)

LDER -1.91305

(0.3265)

-1.9671

(0.6184)

-53.1502

(0.0001)

-53.1664

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

(0.4110)

-54.026

(0.0001)

-54.058

(0.0000)

Qantas Airways

LASP -0.40951

(0.9052)

-1.61603

(0.7868)

-50.4062

(0.0001)

-50.4361

(0.0000)

LIR -1.08131

(0.7253)

-3.28599

(0.0687)

-51.8308

(0.0001)

-51.8281

(0.0000)

LDER -2.29739

(0.1729)

-2.64155

(0.2617)

-14.8604

(0.0000)

-14.865

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

(0.4110)

-54.026

(0.0001)

-54.026

(0.0001)

Lufthansa Airlines

LASP -1.56446

(0.50079

-2.20526

(0.4859)

-49.5213

(0.0001)

-49.5132

(0.0000)

LIR -1.68779

(0.4374)

-3.25412

(0.0743)

-20.2008

(0.0000)

-20.1969

(0.0000)

LDER -1.68343

(0.4396)

-2.47297

(0.3417)

-51.9482

(0.0001)

-51.9383

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

(0.411)

-54.026

(0.0001)

-54.058

(0.0000)

Air China

LASP -2.16457

(0.2196)

-2.13241

(0.5268)

-47.3102

(0.0001)

-47.3106

(0.0000)

LIR -2.49796

(0.116)

-2.5784

(0.2904)

-36.3309

(0.0001)

-36.36

(0.0000)

LDER -0.88008

(0.795)

-0.7243

(0.9704)

-51.2119

(0.0001)

-51.2987

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

(0.4110)

-54.026

(0.0001)

-54.058

(0.0000)

Artículos • Abdulkadir Alici

• 119 •

Airlines Variables

ADF-Test

Statistics(Level)

ADF-Test Statistics(1.

difference)

Constant Constant

and Trend Constant Constant

and Trend

Aeroot

LASP -1.59872

(0.4831)

-1.58365

(0.7995)

-48.566

(0.0001)

-48.5646

(0.0000)

LIR -3.7175

(0.0039)

-3.67119

(0.0244)

-24.1436

(0.0000)

-24.1479

(0.0000)

LDER -0.43884

(0.9001)

-1.76154

(0.7232)

-51.7562

(0.0001)

-51.7512

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

(0.4110)

-54.026

(0.0001)

-54.058

(0.0000)

Air Canada

LASP -2.16457

(0.2196(

-2.13241

(0.5268)

-47.3102

(0.0001)

-47.3106

(0.0000)

LIR -1.83955

(0.3615)

-2.15779

(0.5125)

-51.8502

(0.0001)

-51.8422

(0.0000)

LDER -3.338

(0.0134)

-3.61609

80.0286)

-21.3296

(0.0000)

-21.327

(0.0000)

LBOP -1.54241

(0.5120)

-2.34089

80.4110)

-54.026

(0.0001)

-54.058

(0.0000)

Easyjet

LASP -1.65538

(0.454)

-1.01484

(0.9403)

-49.7491

(0.0001)

-49.7702

(0.0000)

LIR -1.57127

(0.4972)

-3.13876

(0.0975)

-52.5233

(0.0001)

-52.5135

(0.0000)

LDER -1.68343

(0.4396)

-2.47297

(0.3417)

-51.9482

(0.0001)

-51.9383

(0.0000)

LBOP -1.68343

(0.4396)

-2.47297

(0.3417)

-51.9482

(0.0001)

-51.9383

(0.0000)

Gol Linhas Aeras

LASP -1.37094

(0.598)

-1.21986

(0.9054)

-49.8904

(0.0001)

-49.8891

(0.0000)

LIR -0.69097

(0.8471)

-0.90032

(0.9544)

-34.939

(0.0000)

-34.9486

(0.0000)

LDER -0.01738

(0.9559)

-2.96526

(0.1423)

-55.3418

(0.0001)

-55.3772

(0.0000)

LBOP -1.54238

(0.512)

-2.34086

(0.411)

-54.0266

(0.0001)

-54.0586

(0.0000)

JetBlue

LASP -1.00447

(0.7538)

-2.57575

(0.2916)

-50.6404

(0.0001)

-50.6315

(0.0000)

LIR -2.21632

(0.2006)

-2.30433

(0.4309)

-54.133

(0.0001)

-54.1228

(0.0000)

LDER -1.68376

(0.4395)

-2.47218

(0.3421)

-51.9482

(0.0001)

-51.9384

(0.0000)

LBOP -1.68376

(0.4395)

-1.68376

(0.4395)

-51.9482

(0.0001)

-51.9384

(0.0000)

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 120 •

Airlines Variables

ADF-Test

Statistics(Level)

ADF-Test Statistics(1.

difference)

Constant Constant

and Trend Constant Constant

and Trend

Norwegian

LASP -2.64129

(0.0848)

-2.17174

(0.5047)

-50.2849

(0.0001)

-50.3258

(0.0000)

LIR -7.574

(0.0000)

-7.57427

(0.0000)

-19.3165

(0.0000)

-19.3129

(0.0000)

LDER -0.72958

(0.8374)

-2.81907

(0.1904)

-52.5566

(0.0001)

-52.5745

(0.0000)

LBOP -1.54238

(0.512)

-2.34086

(0.411)

-54.0266

(0.0001)

-54.0586

(0.0000)

Southwest

LASP -0.79693

(0.8193)

-1.66027

(0.7685)

-54.7848

(0.0001)

-54.7747

(0.0000)

LIR -2.21632

(0.2006)

-2.30433

(0.4309)

-54.133

(0.0001)

-54.1228

(0.0000)

LDER -1.68376

(0.4395)

-2.47218

(0.3421)

-51.9482

(0.0001)

-51.9384

(0.0000)

LBOP -1.54241

(0.512)

-2.34089

(0.411)

-54.026

(0.0001)

-54.058

(0.0000)

Westjet

LASP -1.6517

(0.4559)

-1.57375

(0.8033)

-50.2391

(0.0001)

-50.2393

(0.0000)

LIR -1.83955

(0.3615)

-2.15779

(0.5125)

-51.8502

(0.0001)

-51.8422

(0.0000)

LDER -3.338

(0.01349

-3.61609

(0.0286)

-21.3296

(0.0000)

-21.327

(0.0000)

LBOP -1.54238

(0.512)

-2.34086

(0.411)

-54.0266

(0.0001)

-54.0586

(0.0000)

Annex 5: Toda-Yomamato Causality Test (All)

Airlines Direction of Causality Var(p+d) Prob.

United Airlines

LASP ⇏ LDER* 5.454473 2+1 0.0654

LDER ⇏ LASP 1.614422 2+1 0.4461

LASP ⇏ LBOP 2.769781 2+1 0.2504

LBOP ⇏ LASP 1.598040 2+1 0.4498

LASP ⇏ LIR 0.596795 2+1 0.7420

LIR ⇏ LASP 0.744436 2+1 0.6892

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Airlines Direction of Causality Var(p+d) Prob.

Turkish Airlines

LASP ⇏ LDER 3.439227 5+1 0.6326

LDER ⇏ LASP 23.00338 5+1 0.0003

LASP ⇏ LBOP 3.273417 5+1 0.6579

LBOP ⇏ LASP 12.72166 5+1 0.0261

LASP ⇏ LIR 12.28137 5+1 0.0311

LIR ⇏ LASP 6.981030 5+1 0.2221

Singapore Airlines

LASP ⇏ LDER* 4.613838 2+1 0.0996

LDER ⇏ LASP 14.28926 2+1 0.0008

LASP ⇏ LBOP 1.979957 2+1 0.3716

LBOP ⇏ LASP 0.920073 2+1 0.6313

LASP ⇏ LIR 0.099344 2+1 0.9515

LIR ⇏ LASP 1.564555 2+1 0.4574

Qantas Airways

LASP ⇏ LDER 9.586351 11+2 0.5679

LDER ⇏ LASP 19.15354 11+2 0.0584

LASP ⇏ LBOP 10.48858 11+2 0.4870

LBOP ⇏ LASP 22.47271 11+2 0.0210

LASP ⇏ LIR 9.957297 11+2 0.5342

LIR ⇏ LASP 14.72919 11+2 0.1952

Lufthansa Airlines

LASP ⇏ LDER 3.839486 4+1 0.4282

LDER ⇏ LASP 12.08459 4+1 0.0167

LASP ⇏ LBOP 9.412552 4+1 0.0516

LBOP ⇏ LASP 14.71976 4+1 0.0053

LASP ⇏ LIR 10.41648 4+1 0.0340

LIR ⇏ LASP 7.243989 4+1 0.1235

Air China

LASP ⇏ LDER 0.713048 2+1 0.7001

LDER ⇏ LASP 8.574296 2+1 0.0137

LASP ⇏ LBOP 1.456549 2+1 0.4827

LBOP ⇏ LASP 11.20648 2+1 0.0037

LASP ⇏ LIR 2.112953 2+1 0.3477

LIR ⇏ LASP 0.233940 2+1 0.8896

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 122 •

Airlines Direction of Causality Var(p+d) Prob.

Aeroot

LASP ⇏ LDER 7.214308 10+2 0.7051

LDER ⇏ LASP 22.90321 10+2 0.0111

LASP ⇏ LBOP 12.07843 10+2 0.2798

LBOP ⇏ LASP 15.54836 10+2 0.1133

LASP ⇏ LIR 12.57227 10+2 0.2486

LIR ⇏ LASP 9.418824 10+2 0.4929

Air Canada

LASP ⇏ LDER 0.475302 2+1 0.7885

LDER ⇏ LASP 1.7763 2+1 0.4114

LASP ⇏ LBOP 1.236906 2+1 0.5388

LBOP ⇏ LASP 8.734665 2+1 0.0127

LASP ⇏ LIR 0.400624 2+1 0.8185

LIR ⇏ LASP 0.190176 2+1 0.9093

Easyjet

LASP ⇏ LDER 4.017438 2+1 0.1342

LDER ⇏ LASP 11.57768 2+1 0.0031

LASP ⇏ LBOP 0.523379 2+1 0.7698

LBOP ⇏ LASP 2.419486 2+1 0.2983

LASP ⇏ LIR 0.307617 2+1 0.8574

LIR ⇏ LASP 7.755625 2+1 0.0207

Gol Linhas Aeras

LASP ⇏ LDER 0.930101 2+1 0.6281

LDER ⇏ LASP 9.666582 2+1 0.008

LASP ⇏ LBOP 1.90757 2+1 0.3853

LBOP ⇏ LASP 0.524288 2+1 0.7694

LASP ⇏ LIR 0.986156 2+1 0.6107

LIR ⇏ LASP 1.387106 2+1 0.4998

JetBlue

LASP ⇏ LDER 4.138988 2+1 0.1262

LDER ⇏ LASP 0.238096 2+1 0.295

LASP ⇏ LBOP 1.459149 2+1 0.4821

LBOP ⇏ LASP 1.668923 2+1 0.8878

LASP ⇏ LIR 2.402695 2+1 0.3008

LIR ⇏ LASP 2.441567 2+1 0.295

Artículos • Abdulkadir Alici

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Airlines Direction of Causality Var(p+d) Prob.

Norwegian

LASP ⇏ LDER* 4.622919 2+1 0.0991

LDER ⇏ LASP 5.914692 2+1 0.052

LASP ⇏ LBOP 0.691067 2+1 0.7078

LBOP ⇏ LASP 1.673395 2+1 0.4331

LASP ⇏ LIR 0.384805 2+1 0.825

LIR ⇏ LASP 0.373598 2+1 0.8296

Southwest

LASP ⇏ LDER 3.047839 2+1 0.2179

LDER ⇏ LASP 1.577901 2+1 0.4543

LASP ⇏ LBOP 7.848946 2+1 0.0198

LBOP ⇏ LASP* 5.423005 2+1 0.0664

LASP ⇏ LIR 0.781822 2+1 0.6764

LIR ⇏ LASP 2.90079 2+1 0.2345

Westjet

LASP ⇏ LDER 5.316777 8+1 0.7232

LDER ⇏ LASP 5.195281 8+1 0.7365

LASP ⇏ LBOP 8.260785 8+1 0.4084

LBOP ⇏ LASP* 13.0928 8+1 0.1087

LASP ⇏ LIR 6.810396 8+1 0.5572

LIR ⇏ LASP 3.557818 8+1 0.8947

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 124 •

Annex 6: Hatemi-J (2012) Asymmetric Causality Analysis Results (All)

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

United Airlines

LASP+ ≠ > LDER+ 1.504 6.73 3.921 2.736

LASP- ≠ > LDER- 3.777* 7.089 3.952 2.751

LASP- ≠ > LDER+ 0.139 6.469 3.83 2.631

LASP+ ≠ > LDER- 0.020 6.585 3.854 2.699

LDER+ ≠ > LASP+ 0.252 6.874 3.884 2.729

LDER- ≠ > LASP- 1.628 6.848 3.822 2.564

LDER- ≠ > LASP+ 5.525** 6.934 3.88 2.754

LDER+ ≠ > LASP- 1.171 6.763 3.845 2.713

LASP+ ≠ > LBOP+ 1.801 6.378 3.786 2.543

LASP- ≠ > LBOP- 1.191 6.323 4.112 2.738

LASP- ≠ > LBOP+ 5.089** 6.805 4.001 2.839

LASP+ ≠ > LBOP- 0.135 7.351 3.664 2.708

LBOP+ ≠ > LASP+ 4.109** 6.847 3.886 2.785

LBOP- ≠ > LASP- 1.349 6.838 3.837 2.656

LBOP- ≠ > LASP+ 1.196 6.958 3.868 2.745

LBOP+ ≠ > LASP- 4.932** 7.149 4.033 2.68

LASP+ ≠ > LIR+ 1.504 6.969 3.894 2.707

LASP- ≠ > LIR- 3.777* 7.131 4.027 2.728

LASP- ≠ > LIR+ 0.139 6.727 3.845 2.625

LASP+ ≠ > LIR- 1.000 6.73 4.007 2.647

LIR+ ≠ > LASP+ 4.109** 6.847 3.886 2.785

LIR- ≠ > LASP- 1.349 6.838 3.837 2.656

LIR- ≠ > LASP+ 1.196 6.958 3.868 2.745

LIR+ ≠ > LASP- 4.932** 7.149 4.033 2.68

Artículos • Abdulkadir Alici

• 125 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Turkish Airlines

LASP+ ≠ > LDER+ 5.641** 6.505 3.846 2.72

LASP- ≠ > LDER- 37.128*** 9.482 6.016 4.679

LASP- ≠ > LDER+ 0.772 6.371 3.765 2.677

LASP+ ≠ > LDER- 0.087 6.261 3.734 2.642

LDER+ ≠ > LASP+ 1.796 6.131 3.843 2.609

LDER- ≠ > LASP- 0.002 9.725 6.053 4.61

LDER- ≠ > LASP+ 0.135 6.518 3.814 2.794

LDER+ ≠ > LASP- 13.245*** 6.607 3.968 2.68

LASP+ ≠ > LBOP+ 1.289 6.719 3.803 2.655

LASP- ≠ > LBOP- 4.509** 6.92 3.842 2.753

LASP- ≠ > LBOP+ 0.271 7.035 4.138 2.887

LASP+ ≠ > LBOP- 1.235 6.83 3.902 2.776

LBOP+ ≠ > LASP+ 0.158 6.762 3.927 2.818

LBOP- ≠ > LASP- 0.495 6.577 3.827 2.657

LBOP- ≠ > LASP+ 0.054 6.811 3.932 2.719

LBOP+ ≠ > LASP- 0.547 6.253 3.758 2.738

LASP+ ≠ > LIR+ 15.025*** 13.804 9.569 7.864

LASP- ≠ > LIR- 1.159 10.819 5.98 4.494

LASP- ≠ > LIR+ 1.278 7.474 3.821 2.76

LASP+ ≠ > LIR- 0.000 6.343 3.732 2.624

LIR+ ≠ > LASP+ 13.368** 14.051 9.456 7.645

LIR- ≠ > LASP- 19.144*** 9.954 6.128 4.726

LIR- ≠ > LASP+ 7.235* 15.147 9.597 7.736

LIR+ ≠ > LASP- 6.980 13.879 9.568 7.682

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 126 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Singapore Airlines

LASP+ ≠ > LDER+ 1.955 6.699 3.805 2.726

LASP- ≠ > LDER- 22.330*** 9.948 6.09 4.562

LASP- ≠ > LDER+ 0.558 6.906 3.748 2.665

LASP+ ≠ > LDER- 0.131 7.219 3.861 2.716

LDER+ ≠ > LASP+ 0.000 6.755 3.824 2.813

LDER- ≠ > LASP- 13.059** 13.317 8.061 6.406

LDER- ≠ > LASP+ 3.539* 6.819 3.892 2.758

LDER+ ≠ > LASP- 1.314 7.57 3.918 2.636

LASP+ ≠ > LBOP+ 8.135*** 6.914 3.957 2.765

LASP- ≠ > LBOP- 2.931* 6.96 3.86 2.657

LASP- ≠ > LBOP+ 0.736 6.666 3.891 2.741

LASP+ ≠ > LBOP- 0.825 6.85 3.835 2.705

LBOP+ ≠ > LASP+ 1.830 6.595 3.797 2.657

LBOP- ≠ > LASP- 3.799** 7.161 3.804 2.596

LBOP- ≠ > LASP+ 0.401 6.918 3.827 2.672

LBOP+ ≠ > LASP- 0.162 7.218 4.016 2.695

LASP+ ≠ > LIR+ 0.952 9.153 3.733 2.47

LASP- ≠ > LIR- 0.815 8.329 3.594 2.336

LASP- ≠ > LIR+ 1.669 8.891 3.751 2.533

LASP+ ≠ > LIR- 0.019 7.786 3.585 2.485

LIR+ ≠ > LASP+ 0.974 8.595 3.869 2.47

LIR- ≠ > LASP- 1.903 8.222 3.49 2.294

LIR- ≠ > LASP+ 2.838* 8.253 3.594 2.339

LIR+ ≠ > LASP- 0.234 8.43 3.397 2.275

Artículos • Abdulkadir Alici

• 127 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Qantas Airways

LASP+ ≠ > LDER+ 0.034 8.023 3.545 2.382

LASP- ≠ > LDER- 1.299 11.699 3.319 2.033

LASP- ≠ > LDER+ 2.786* 8.417 3.56 2.393

LASP+ ≠ > LDER- 0.315 9.035 3.773 2.514

LDER+ ≠ > LASP+ 0.059 8.769 3.605 2.532

LDER- ≠ > LASP- 0.027 11.871 3.225 2.116

LDER- ≠ > LASP+ 0.185 12.695 3.257 2.08

LDER+ ≠ > LASP- 0.246 13.115 3.629 2.204

LASP+ ≠ > LBOP+ 22.021*** 9.002 5.961 4.582

LASP- ≠ > LBOP- 24.621*** 15.124 9.745 7.806

LASP- ≠ > LBOP+ 8.201*** 7.055 3.784 2.762

LASP+ ≠ > LBOP- 2.008 6.892 3.868 2.698

LBOP+ ≠ > LASP+ 1.935 9.505 6.034 4.632

LBOP- ≠ > LASP- 7.392* 14.828 9.466 7.269

LBOP- ≠ > LASP+ 0.033 7.368 3.871 2.743

LBOP+ ≠ > LASP- 5.937** 7.137 3.927 2.642

LASP+ ≠ > LIR+ 3.147* 6.976 3.96 2.728

LASP- ≠ > LIR- 0.732 7.25 3.993 2.728

LASP- ≠ > LIR+ 0.178 6.853 3.998 2.792

LASP+ ≠ > LIR- 0.007 6.652 3.895 2.756

LIR+ ≠ > LASP+ 0.632 6.784 3.749 2.66

LIR- ≠ > LASP- 0.000 6.941 3.849 2.654

LIR- ≠ > LASP+ 16.062*** 14.578 9.797 7.717

LIR+ ≠ > LASP- 4.388 14.178 9.79 7.783

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 128 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Lufthansa Airlines

LASP+ ≠ > LDER+ 0.322 6.779 3.731 2.607

LASP- ≠ > LDER- 4.633** 6.702 4.042 2.771

LASP- ≠ > LDER+ 6.181** 6.565 3.785 2.687

LASP+ ≠ > LDER- 4.215** 6.541 3.749 2.638

LDER+ ≠ > LASP+ 0.935 6.541 3.875 2.685

LDER- ≠ > LASP- 0.181 6.864 3.92 2.778

LDER- ≠ > LASP+ 1.715 6.597 3.796 2.688

LDER+ ≠ > LASP- 1.856 6.718 3.821 2.605

LASP+ ≠ > LBOP+ 0.151 6.755 3.901 2.704

LASP- ≠ > LBOP- 0.930 6.923 3.946 2.766

LASP- ≠ > LBOP+ 2.984* 6.337 3.736 2.682

LASP+ ≠ > LBOP- 1.161 6.88 3.893 2.674

LBOP+ ≠ > LASP+ 0.278 6.708 3.825 2.732

LBOP- ≠ > LASP- 1.472 6.622 3.913 2.688

LBOP- ≠ > LASP+ 0.000 6.696 3.832 2.737

LBOP+ ≠ > LASP- 5.383** 6.944 3.67 2.655

LASP+ ≠ > LIR+ 1.272 6.892 3.71 2.635

LASP- ≠ > LIR- 3.041* 6.871 4 2.8

LASP- ≠ > LIR+ 0.361 6.548 3.871 2.726

LASP+ ≠ > LIR- 1.210 6.517 3.811 2.717

LIR+ ≠ > LASP+ 0.244 6.844 3.854 2.723

LIR- ≠ > LASP- 0.001 6.923 3.993 2.761

LIR- ≠ > LASP+ 20.236*** 11.527 7.776 6.163

LIR+ ≠ > LASP- 3.904 11.731 7.912 6.392

Artículos • Abdulkadir Alici

• 129 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Air China

LASP+ ≠ > LDER+ 0.310 6.937 3.906 2.708

LASP- ≠ > LDER- 0.200 6.857 3.775 2.665

LASP- ≠ > LDER+ 0.347 7.238 3.786 2.696

LASP+ ≠ > LDER- 0.099 6.518 3.79 2.672

LDER+ ≠ > LASP+ 2.749* 6.711 3.769 2.661

LDER- ≠ > LASP- 1.316 7.076 3.856 2.732

LDER- ≠ > LASP+ 0.833 6.741 3.946 2.699

LDER+ ≠ > LASP- 3.226* 6.776 3.821 2.704

LASP+ ≠ > LBOP+ 0.065 6.356 3.721 2.543

LASP- ≠ > LBOP- 4.318** 7.038 3.817 2.618

LASP- ≠ > LBOP+ 0.001 6.925 3.709 2.556

LASP+ ≠ > LBOP- 1.080 6.897 3.767 2.559

LBOP+ ≠ > LASP+ 3.727* 6.765 3.953 2.762

LBOP- ≠ > LASP- 4.155** 6.944 3.676 2.601

LBOP- ≠ > LASP+ 1.401 6.604 3.75 2.538

LBOP+ ≠ > LASP- 0.774 6.558 3.801 2.633

LASP+ ≠ > LIR+ 0.076 9.516 6.162 4.658

LASP- ≠ > LIR- 0.737 9.154 5.981 4.584

LASP- ≠ > LIR+ 0.647 9.239 5.935 4.631

LASP+ ≠ > LIR- 0.365 9.536 5.82 4.412

LIR+ ≠ > LASP+ 7.707** 9.567 6.261 4.771

LIR- ≠ > LASP- 1.011 9.833 6.323 4.635

LIR- ≠ > LASP+ 2.384 11.152 7.817 6.115

LIR+ ≠ > LASP- 2.559 11.05 7.816 6.356

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 130 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Aeroot

LASP+ ≠ > LDER+ 0.167 6.933 3.729 2.598

LASP- ≠ > LDER- 5.869* 10.001 5.958 4.491

LASP- ≠ > LDER+ 2.727 10.641 6.116 4.634

LASP+ ≠ > LDER- 10.903*** 9.72 6.256 4.563

LDER+ ≠ > LASP+ 0.737 7.235 3.725 2.706

LDER- ≠ > LASP- 2.363 9.986 6.229 4.647

LDER- ≠ > LASP+ 2.574* 6.997 3.945 2.53

LDER+ ≠ > LASP- 0.115 6.463 3.903 2.684

LASP+ ≠ > LBOP+ 1.866 6.801 3.924 2.785

LASP- ≠ > LBOP- 3.331 9.752 5.997 4.58

LASP- ≠ > LBOP+ 4.612** 7.157 3.862 2.646

LASP+ ≠ > LBOP- 4.530** 7.036 4.125 2.737

LBOP+ ≠ > LASP+ 0.431 6.906 3.695 2.658

LBOP- ≠ > LASP- 0.928 10.025 6.073 4.603

LBOP- ≠ > LASP+ 0.865 9.255 6.047 4.662

LBOP+ ≠ > LASP- 0.091 9.285 6.07 4.548

LASP+ ≠ > LIR+ 34.060*** 26.365 16.97 13.778

LASP- ≠ > LIR- 0.039 7.115 3.692 2.591

LASP- ≠ > LIR+ 4.564** 7.634 3.792 2.572

LASP+ ≠ > LIR- 7.258*** 7.199 3.603 2.499

LIR+ ≠ > LASP+ 18.413** 23.567 16.782 13.796

LIR- ≠ > LASP- 3.058* 7.162 3.804 2.623

LIR- ≠ > LASP+ 27.380*** 21.663 16.031 13.538

LIR+ ≠ > LASP- 41.742*** 24.123 16.821 13.878

Artículos • Abdulkadir Alici

• 131 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Air Canada

LASP+ ≠ > LDER+ 0.127 8.021 4.016 2.825

LASP- ≠ > LDER- 0.055 8.023 3.45 2.423

LASP- ≠ > LDER+ 1.234 8.658 3.9 2.6

LASP+ ≠ > LDER- 0.001 6.696 3.463 2.411

LDER+ ≠ > LASP+ 0.032 7.443 3.666 2.5

LDER- ≠ > LASP- 0.973 7.338 3.604 2.497

LDER- ≠ > LASP+ 0.166 7.97 3.697 2.548

LDER+ ≠ > LASP- 0.576 7.094 3.504 2.443

LASP+ ≠ > LBOP+ 0.065 6.379 3.929 2.637

LASP- ≠ > LBOP- 4.318** 6.623 3.955 2.736

LASP- ≠ > LBOP+ 0.001 6.746 3.742 2.7

LASP+ ≠ > LBOP- 1.080 6.472 3.576 2.631

LBOP+ ≠ > LASP+ 3.727* 6.765 3.953 2.762

LBOP- ≠ > LASP- 4.155** 6.944 3.676 2.601

LBOP- ≠ > LASP+ 1.401 6.604 3.75 2.538

LBOP+ ≠ > LASP- 0.774 6.558 3.801 2.633

LASP+ ≠ > LIR+ 0.240 6.09 3.71 2.748

LASP- ≠ > LIR- 1.360 6.236 4.021 2.597

LASP- ≠ > LIR+ 0.919 5.978 3.869 2.558

LASP+ ≠ > LIR- 0.513 7.333 3.858 2.692

LIR+ ≠ > LASP+ 1.127 6.996 3.813 2.639

LIR- ≠ > LASP- 0.067 6.284 3.771 2.701

LIR- ≠ > LASP+ 0.418 7.126 4.021 2.812

LIR+ ≠ > LASP- 0.015 7.161 3.888 2.735

LASP+ ≠ > LDER+ 2.766* 6.892 3.821 2.688

LASP- ≠ > LDER- 13.416*** 6.861 3.95 2.753

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 132 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

EasyJet

LASP- ≠ > LDER+ 1.415 7.012 3.843 2.741

LASP+ ≠ > LDER- 0.384 6.993 3.787 2.685

LDER+ ≠ > LASP+ 0.153 6.644 3.738 2.623

LDER- ≠ > LASP- 0.008 7.262 3.691 2.649

LDER- ≠ > LASP+ 5.312** 6.662 3.938 2.747

LDER+ ≠ > LASP- 0.453 6.896 3.892 2.664

LASP+ ≠ > LBOP+ 0.010 6.882 3.785 2.601

LASP- ≠ > LBOP- 2.773* 6.663 3.795 2.527

LASP- ≠ > LBOP+ 0.243 6.725 3.742 2.603

LASP+ ≠ > LBOP- 0.765 6.646 3.789 2.536

LBOP+ ≠ > LASP+ 10.269*** 6.715 3.622 2.556

LBOP- ≠ > LASP- 2.224 7.467 3.802 2.652

LBOP- ≠ > LASP+ 0.021 6.718 3.889 2.676

LBOP+ ≠ > LASP- 0.001 7.702 3.892 2.77

LASP+ ≠ > LIR+ 0.230 6.892 3.69 2.628

LASP- ≠ > LIR- 1.191 6.555 3.681 2.624

LASP- ≠ > LIR+ 1.869 6.693 3.813 2.703

LASP+ ≠ > LIR- 0.028 6.954 3.861 2.677

LIR+ ≠ > LASP+ 1.725 6.295 3.788 2.639

LIR- ≠ > LASP- 1.292 6.591 3.859 2.677

LIR- ≠ > LASP+ 2.480* 7.263 3.776 2.42

LIR+ ≠ > LASP- 0.026 6.67 3.819 2.682

LASP+ ≠ > LDER+ 1.230 6.609 3.793 2.65

LASP- ≠ > LDER- 8.732*** 6.882 3.828 2.641

Artículos • Abdulkadir Alici

• 133 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Gol Linhas Aeras

LASP- ≠ > LDER+ 0.264 6.886 3.757 2.617

LASP+ ≠ > LDER- 7.050*** 6.91 3.9 2.66

LDER+ ≠ > LASP+ 1.133 6.974 3.822 2.665

LDER- ≠ > LASP- 0.247 7.005 3.886 2.699

LDER- ≠ > LASP+ 6.053** 6.351 3.629 2.611

LDER+ ≠ > LASP- 0.004 6.943 3.836 2.729

LASP+ ≠ > LBOP+ 0.249 7.051 4.031 2.743

LASP- ≠ > LBOP- 1.025 6.508 3.683 2.633

LASP- ≠ > LBOP+ 3.979** 6.681 3.915 2.675

LASP+ ≠ > LBOP- 0.050 6.769 3.795 2.746

LBOP+ ≠ > LASP+ 0.282 6.75 3.939 2.7

LBOP- ≠ > LASP- 0.624 7.088 3.741 2.542

LBOP- ≠ > LASP+ 0.420 7.097 4.011 2.82

LBOP+ ≠ > LASP- 0.573 7.126 4.015 2.672

LASP+ ≠ > LIR+ 0.440 6.559 3.864 2.705

LASP- ≠ > LIR- 0.014 6.786 3.817 2.658

LASP- ≠ > LIR+ 0.148 7.018 3.711 2.633

LASP+ ≠ > LIR- 5.293** 7.185 4.083 2.803

LIR+ ≠ > LASP+ 3.738* 7.4 3.98 2.761

LIR- ≠ > LASP- 0.323 7.103 3.673 2.592

LIR- ≠ > LASP+ 2.047 6.885 3.746 2.643

LIR+ ≠ > LASP- 0.011 6.689 3.79 2.721

LASP+ ≠ > LDER+ 1.058 7.034 3.974 2.745

LASP- ≠ > LDER- 9.698*** 6.923 3.875 2.713

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 134 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Jetblue

LASP- ≠ > LDER+ 1.126 6.718 3.797 2.73

LASP+ ≠ > LDER- 0.394 6.468 3.979 2.857

LDER+ ≠ > LASP+ 0.396 6.469 3.803 2.636

LDER- ≠ > LASP- 2.736* 6.618 3.762 2.681

LDER- ≠ > LASP+ 14.209*** 6.321 3.773 2.701

LDER+ ≠ > LASP- 0.998 6.408 3.791 2.627

LASP+ ≠ > LBOP+ 2.974* 7.338 3.731 2.664

LASP- ≠ > LBOP- 1.353 6.889 3.897 2.774

LASP- ≠ > LBOP+ 6.998** 7.509 4.223 2.949

LASP+ ≠ > LBOP- 0.184 6.872 3.903 2.746

LBOP+ ≠ > LASP+ 5.290** 7.198 3.893 2.765

LBOP- ≠ > LASP- 0.885 6.877 3.757 2.523

LBOP- ≠ > LASP+ 4.757** 7.073 4.039 2.781

LBOP+ ≠ > LASP- 0.546 6.831 3.717 2.626

LASP+ ≠ > LIR+ 2.241 6.956 3.866 2.735

LASP- ≠ > LIR- 6.329** 7.401 4.053 2.717

LASP- ≠ > LIR+ 13.986*** 10.377 6.033 4.48

LASP+ ≠ > LIR- 8.513** 9.511 6.006 4.63

LIR+ ≠ > LASP+ 0.219 6.588 3.947 2.738

LIR- ≠ > LASP- 0.605 6.358 3.851 2.66

LIR- ≠ > LASP+ 9.916*** 6.642 3.941 2.702

LIR+ ≠ > LASP- 0.470 6.821 3.864 2.647

LASP+ ≠ > LDER+ 0.678 6.803 4.035 2.79

LASP- ≠ > LDER- 12.904*** 6.205 3.576 2.625

Artículos • Abdulkadir Alici

• 135 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Norwegian

LASP- ≠ > LDER+ 0.078 6.95 3.862 2.713

LASP+ ≠ > LDER- 0.000 6.37 3.762 2.647

LDER+ ≠ > LASP+ 0.265 7.092 3.889 2.737

LDER- ≠ > LASP- 0.653 6.599 3.92 2.668

LDER- ≠ > LASP+ 5.777** 7.076 3.868 2.71

LDER+ ≠ > LASP- 1.102 7.022 3.87 2.76

LASP+ ≠ > LBOP+ 1.880 9.348 5.934 4.65

LASP- ≠ > LBOP- 0.572 6.313 3.798 2.611

LASP- ≠ > LBOP+ 1.229 6.546 3.982 2.765

LASP+ ≠ > LBOP- 0.791 7.043 3.896 2.739

LBOP+ ≠ > LASP+ 2.573 9.575 6.015 4.566

LBOP- ≠ > LASP- 1.026 6.731 3.882 2.746

LBOP- ≠ > LASP+ 5.598* 9.7 6.214 4.547

LBOP+ ≠ > LASP- 2.480 9.616 6.06 4.627

LASP+ ≠ > LIR+ 0.006 11.589 4.046 3.232

LASP- ≠ > LIR- 0.455 10.053 4.231 2.328

LASP- ≠ > LIR+ 0.514 8.522 4.024 2.306

LASP+ ≠ > LIR- 0.200 11.562 4.437 2.412

LIR+ ≠ > LASP+ 0.409 10.726 3.94 2.146

LIR- ≠ > LASP- 0.374 10.032 4.102 2.395

LIR- ≠ > LASP+ 0.346 11.095 4.003 2.361

LIR+ ≠ > LASP- 0.421 11.647 4.162 2.412

LASP+ ≠ > LDER+ 0.022 6.899 3.973 2.763

LASP- ≠ > LDER- 1.249 6.228 3.56 2.54

Anduli • Revista Andaluza de Ciencias Sociales Nº 25 - 2024

• 136 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Southwest

LASP- ≠ > LDER+ 0.511 6.238 3.69 2.608

LASP+ ≠ > LDER- 1.899 7.371 3.83 2.659

LDER+ ≠ > LASP+ 0.525 6.748 3.844 2.69

LDER- ≠ > LASP- 10.894*** 7.265 3.892 2.66

LDER- ≠ > LASP+ 7.986*** 6.692 3.756 2.727

LDER+ ≠ > LASP- 0.332 6.697 3.827 2.704

LASP+ ≠ > LBOP+ 0.241 6.826 3.714 2.623

LASP- ≠ > LBOP- 1.159 7.188 3.756 2.657

LASP- ≠ > LBOP+ 4.616** 7.01 3.77 2.645

LASP+ ≠ > LBOP- 0.014 6.869 3.845 2.793

LBOP+ ≠ > LASP+ 0.886 7.579 4.062 2.788

LBOP- ≠ > LASP- 4.335** 7.075 4.048 2.776

LBOP- ≠ > LASP+ 1.843 7.557 4.073 2.74

LBOP+ ≠ > LASP- 2.259 6.754 3.657 2.663

LASP+ ≠ > LIR+ 1.342 7.045 3.925 2.714

LASP- ≠ > LIR- 1.334 6.904 3.659 2.645

LASP- ≠ > LIR+ 0.143 6.712 3.807 2.652

LASP+ ≠ > LIR- 0.643 7.03 3.756 2.605

LIR+ ≠ > LASP+ 0.031 7.05 3.948 2.716

LIR- ≠ > LASP- 0.096 6.843 3.658 2.567

LIR- ≠ > LASP+ 47.711*** 15.775 11.199 9.275

LIR+ ≠ > LASP- 12.711** 15.78 11.492 9.384

LASP+ ≠ > LDER+ 2.521* 7.438 3.621 2.391

LASP- ≠ > LDER- 0.018 7.28 3.593 2.473

Artículos • Abdulkadir Alici

• 137 •

Hatemi-J (2012) Asymmetric Causality Analysis

Airlines Direction of Causality Wald Stat. Critical Bootstrap Value

1% 5% 10%

Westjet

LASP- ≠ > LDER+ 0.050 8.213 3.671 2.525

LASP+ ≠ > LDER- 0.017 8.411 3.61 2.552

LDER+ ≠ > LASP+ 1.431 8.542 3.604 2.5

LDER- ≠ > LASP- 1.829 6.908 3.604 2.488

LDER- ≠ > LASP+ 0.374 7.537 3.761 2.513

LDER+ ≠ > LASP- 0.067 7.946 3.606 2.457

LASP+ ≠ > LBOP+ 0.011 6.656 4.043 2.717

LASP- ≠ > LBOP- 0.034 7.096 4.014 2.888

LASP- ≠ > LBOP+ 0.311 6.566 3.736 2.62

LASP+ ≠ > LBOP- 0.019 6.87 3.819 2.725

LBOP+ ≠ > LASP+ 7.184*** 6.8616 3.957 2.807

LBOP- ≠ > LASP- 0.001 6.472 3.763 2.617

LBOP- ≠ > LASP+ 0.052 6.86 3.894 2.727

LBOP+ ≠ > LASP- 1.801 6.956 3.633 2.612

LASP+ ≠ > LIR+ 0.536 7.116 3.911 2.749

LASP- ≠ > LIR- 0.283 6.845 4.021 2.753

LASP- ≠ > LIR+ 0.368 6.849 3.896 2.748

LASP+ ≠ > LIR- 0.368 6.991 3.815 2.756

LIR+ ≠ > LASP+ 2.352 6.89 3.809 2.653

LIR- ≠ > LASP- 1.277 6.52 3.745 2.56

LIR- ≠ > LASP+ 0.491 6.672 3.74 2.595

LIR+ ≠ > LASP- 0.060 6.851 3.783 2.696