An analysis of the geopolitical and economics influence on tourist arrivals in Russia using a nonlinear autoregressive distributed lag model

This paper examined the theory of purchasing power parity (PPP) for a group of developed and developing countries from January 2003 to May 2016 using both linear and nonlinear panel autoregressive distributed lag (ARDL) models. In addition, the paper extended a time series nonlinear ARDL model to a panel nonlinear ARDL model in testing for the PPP. Further, several panel tests of unit root were carried out to inspect the stationary properties of the variables. Outcome of the tests showed that the variables are a combination of I(1) and I(0). Since we have a combination of I(1) and I(0), linear and nonlinear panel ARDL models were estimated. The linear ARDL models were not valid since they failed to provide evidence for cointegration. However, the extended nonlinear panel ARDL models provided evidence of cointegration indicating that the PPP theory is valid for this group of countries. Unlike previous studies on the PPP, this study made a significant contribution by the provision of useful policy implication on the results found.

PurposeMany tourism academics have investigated the linkages between tourism, power and space; few have specifically addressed the profound links between tourism and geopolitics. In view of the restrictive assumptions of the linear framework used in the earlier studies, and hidden asymmetries present in the time series data. Against this backdrop, the study tries to find out how tourists may respond differently to favourable and unfavourable shocks in geopolitical risk (GPR).Design/methodology/approachIn order to capture this asymmetric nature of the problem, the study employs the non-linear autoregressive distributed lag (NARDL) model to evaluate data from 2001Q1 to 2019Q4.FindingsThe results show that both positive and negative shock to GPR does not produce results of equal magnitude. A positive shock to GPR has a more detrimental effect on foreign tourist arrivals (FTA) than a beneficial effect a negative shock produces. Besides this, the present study also looks at the effect of other macro-economic variables on FTA. An ascend in the real effective exchange rate (REER) i.e. appreciation of the domestic currency has an unfavourable impact on foreign visitor arrivals, while an increase in world gross domestic product amplify it. The results of the study are robust to alternative measures of the control variable.Practical implicationsThe study is significant for policymakers in understanding the short and long-run implications of GPR on FTA in India. The present study can assist policymakers, and destination managers to manage the external and internal risks and minimise the consequences of geopolitical threats on the Indian tourism industry. Consequently, destination managers can utilise the study's findings in calibrating their operations and designing crisis marketing strategies within the geopolitical dynamics of the Indian state.Originality/valueThe study tries to find out how tourists may exhibit distinct reactions to positive and negative disturbances in GPR. The study provides first-hand evidence of how GPR impacts tourism demand. The paper also includes the existing body of literature related to GPR factors and their effect on tourist influx, specifically in the framework of the Indian tourism sector.

This study aims to approach the Fisher effect issue from a different methodological perspective. To this aim, the nonlinear autoregressive distributed lag (ARDL) model, recently developed by Shin et al. (2014), is applied for South Korea between 2000Q4Ƀ2017Q4. This model allows us to decompose one variable (changes in inflation) into two new variables (increases and decreases in inflation) under the manners of nonlinearity and asymmetry. Hence, it enables us to monitor the Fisher effect in terms of increases and decreases separately. We also apply the linear version of the same model since the nonlinear ARDL model is the extended version of linear ARDL model. While the empirical findings of the nonlinear model support asymmetrically partial Fisher effects in the long-run for 1, 3, 5 and 10-years Korean bond rates, the linear model does not. Additionally, the nonlinear model detects lower size partial Fisher effects when the maturity of interest rates gets longer. Another finding of this study is that the nonlinear model may mathematically identify and introduce a different version of the partial Fisher effect based on singular (separate) effects of each decomposed variable in a parametric manner.

Symmetric and asymmetric impact of economic policy uncertainty on food prices in China: A new evidence

Vietnam's exports to Korea and the real exchange rate: Post-crisis evidence from the multiple threshold nonlinear autoregressive distributed lag model

Can art hedge against economic policy uncertainty?: New insights through the NARDL model

This article investigates the symmetrical and asymmetrical linkage between gold prices and the Indian stock market in the presence of structural breaks. The study covers a time frame from April 1993 to December 2019. The study employs both linear autoregressive distributed lag (ARDL) and non-linear ARDL (NLARDL) models. The major findings of the study are as follows: first, both the ARDL and NLARDL models indicate the co-movement of gold prices and the Indian stock market. Second, based on the long-run parameters of both the models, the long-run relationship between the variables could not be established. Third, short-run parameters show that positive shocks in gold prices depress the Indian stock market; however, negative shocks in gold prices have a positive but insignificant relationship with the Indian stock market. The asymmetric reaction of the Indian stock market will yield new insights for investors, fund managers, and policymakers.

The increase in global economic policy uncertainty (EPU), volatility or stock market uncertainty (VIX), and geopolitical risk (GPR) has affected gold prices (GD), crude oil prices (WTI), and stock markets, which present challenges for investors. Sustainable stock investments in emerging markets may minimize and diversify investor risk. We applied the non-linear autoregressive distributed lag (NARDL) model to examine the effects of EPU, VIX, GPR, GD, and WTI on sustainable stocks in seven emerging markets (Thailand, Malaysia, Indonesia, Brazil, South Africa, Taiwan, and South Korea) from January 2012 to June 2023. EPU, VIX, GPR, GD, and WTI showed non-linear cointegration with sustainable stocks in seven emerging markets and possessed different asymmetric effects in the short and long run. Change in EPU increases the return of Thailand’s sustainable stock in the long run. The long-run GPR only affects the return of Indonesian sustainable stock. All sustainable stocks are negatively affected by the VIX and positively affected by GD in the short and long run. Additionally, long-run WTI negatively affects the return of Indonesia’s sustainable stocks. Our findings contribute to rational investment decisions on sustainable stocks, including gold and crude oil prices, to hedge the asymmetric effect of uncertainty.

Investing in green bonds has been recognized as a strategy that not only promotes environmental performance but also offers attractive investment returns. However, macroeconomic factors, such as geopolitical risk (GPR), climate policy uncertainty (CPU), and global economic policy uncertainty (GEPU), play a substantial role in shaping green bond returns. Using the returns of US green bonds as a proxy for the returns of green bonds, we argue in this paper that these heterogenous uncertainty measures might have asymmetric impacts on the returns of green bonds (GB). By employing monthly data from January 2016 to August 2022, we apply the nonlinear autoregressive distributed lags model (NARDL) to examine the asymmetric impact of these heterogenous uncertainty measures on the returns of GB. The NARDL findings reveal evidence of short‐run asymmetric impacts of GPR, GEPU, and CPU on the returns of green bonds. In the long run, there is an asymmetric impact of GPR and CPU on the returns of GB. However, there is a symmetric impact of CPU on the returns of GB in the long run. Specifically, in the short run, we found that a positive CPU shock causes a decline in the returns of green bonds. A similar magnitude of negative CPU shock causes an increase in the returns of green bonds. Moreover, a positive shock to GPR increases the return of green bonds, while a comparable negative shock to GPR reduces the return of GB. Furthermore, only negative shocks to GEPU have an impact on GB returns in the short run. Specifically, a negative GEPU shock reduces GB returns. In the long term, the returns of GB are positively impacted by negative shocks in GPR and both positive and negative shocks in GEPU, whereas positive shocks in GPR affect the returns of GB negatively.

The asymmetric effects of oil price changes on China’s exports: New evidence from a nonlinear autoregressive distributed lag model

PurposeThis study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.Design/methodology/approachThis study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.FindingsThe real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.Originality/valueThis study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

Purpose The primary aim of this paper is to examine the role of asymmetry in the relationship between FDI shocks and real GDP across seven selected Asian countries, contributing to a deeper understanding of how FDI influences economic growth in the region. Design/methodology/approach This study employs two advanced methodologies: the nonlinear autoregressive distributed lag (NARDL) model and the generalized impulse response function (GIRF). The NARDL model is used to explore both the long- and short-term asymmetric effects of FDI on real GDP, while the GIRF is employed to analyze the responses of GDP to both positive and negative FDI shocks. Findings The results reveal that FDI asymmetrically affects real GDP in the long run in five of the countries under consideration: Indonesia, Malaysia, the Philippines, Singapore and Thailand. Short-term asymmetry is also confirmed for Malaysia, Thailand and Japan. Additionally, the GIRF analysis demonstrates that the responses of GDP to positive and negative FDI shocks differ significantly and are not mirror images. Practical implications The findings have important policy implications, suggesting that policymakers should consider the asymmetrical effects of FDI when designing economic strategies and responses to foreign investment shocks. These insights can enhance empirical modeling and contribute to more effective economic planning in the region. Originality/value This paper is unique in its explicit focus on asymmetry in the relationship between FDI shocks and real GDP, applying advanced econometric techniques to provide a more nuanced understanding of this dynamic. The study’s results offer valuable contributions to both policy analysis and empirical modeling in the context of Asian economies.

The complexity of financial development and economic growth nexus in Syria: A nonlinear modelling approach with artificial neural networks and NARDL model

PurposePrevious studies have mostly estimated there to be a symmetric effect in the Foreign direct investment (FDI) inflow regarding the economic growth of Central, East and Southeast European (CESEE) countries. However, for the CESEE countries, as well as for the majority of countries around the world, there has been no study that has estimated the symmetric and asymmetric effect of outward FDI on economic growth. The main objective of this study is to estimate whether the relation between outward FDI and economic growth in CESEE countries is symmetric or asymmetric.Design/methodology/approachThis study includes a sample based on eight CESEE countries. The authors used the linear and non-linear autoregressive distributed lag (ARDL) model and annual data for the period from 1990 to 2020.FindingsIn the long run, in the linear ARDL model, a significant symmetrical effect due to OFDI on the economic growth of Romania and Slovenia was found, while in the non-linear ARDL model, a significant asymmetric effect of OFDI on the economic growth of Bulgaria, Poland, Romania, Russia, Slovenia and Slovakia was found. In six out of the eight countries, asymmetry was found while symmetry was found in the other two. Poorer symmetry results can be ascribed to the lack of linear model neglecting the asymmetric behaviour of the positive and negative change decomposition as part of the OFDI movement, which leads to the wrong conclusion.Originality/valueThis is the first study to evaluate the asymmetric effect of outward FDI on the economic growth of eight CESEE countries.

Fluctuations in the oil price profoundly impact many other prices in the economy, as oil is used to produce numerous goods and services. While literature is ample regarding the linear relationship between crude oil prices and food prices, academic discussion on the presence of non-linear relationships is relatively evolving. This paper strives to explore the existence of both linear and non-linear relationships between crude oil price and food price in Bangladesh by employing the autoregressive distributed lag (ARDL) model and the non-linear autoregressive distributed lag (NARDL) model, respectively. The ARDL model indicates that the crude oil price has a linear and positive impact on food price inflation in Bangladesh. The NARDL model finds no asymmetric relationship between the two variables in the long run. As a result, in the long run, the response of food price inflation in Bangladesh is the same whether oil prices increase or decrease. However, the NARDL model reveals that the change in oil prices asymmetrically impacts food price inflation only in the short run. These findings are essential for further study, and the results can be used for policymaking to ensure food security in Bangladesh.