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This paper aims to examine the volatility spillover dynamics between stock and foreign exchange market of China considering subprime 2007 financial crisis period.

This study considered daily data from January 2, 2002, to December 31, 2013. The sample period has been further divided into three periods; full sample period (January 2002-December 2013), pre-crisis period (January 2002-October 2007) and post-crisis period (October 2007-December 2013). This study opted Exponential Generalized Autoregressive Heteroskedasticity (EGARCH) model for the purpose of investigating asymmetric volatility spillover.

The results obtained using the EGARCH model imply that volatility spillover dynamics varies from period to period. In full sample period, the results show evidence of significant unidirectional volatility spillover from foreign exchange market to stock market. In pre-crisis period, the results indicate unidirectional volatility spillover from stock market to foreign exchange market. However, in post-crisis period, the results reveal significant bidirectional volatility spillover between stock and foreign exchange market.

The results of the study are important for policy makers because understanding the behavior of the financial markets, i.e. stock and foreign exchange market, would increase the success of policies implemented in a crisis situation. The results would help investors to formulate efficient portfolios.

This study is an important contribution to the existing literature in terms of analyzing volatility spillover between stock and foreign exchange market in an emerging economy, China. Furthermore, this study explored the volatility spillover dynamics between the two markets by considering the pre and post subprime Asian crisis period.

This research began by acknowledging that conventional analysis on the foreign exchange exposure could not adequately reflect firms’ risk management strategies, which firms take actions against uncertainties raised by foreign exchange. In order to conceptualize uncertainty aroused by foreign exchange, the purpose of this paper is to develop an index that could measure corporate profits’ sensitivity to foreign exchange uncertainty and examine its possibility of utilization.

As an alternative to foreign exchange exposure, the present research derived the foreign exchange volatility exposure and analyzed the determinants of foreign currency-denominated debt in terms of foreign exchange volatility exposure. The foreign exchange volatility exposure draws from partially differentiating a firm’s operating profits to the exchange rate volatility.

The major findings are as follows. First, before the Asian financial crisis, South Korean enterprises had similar responses to the exchange volatility exposure as compared with the exchange exposure on procuring foreign-denominated debt. Second, since the global financial crisis (GFC), not only have Korean firms’ response mechanisms to both exposures changed, but also the significance of exchange volatility exposure has been further emphasized. Furthermore, Korean companies have dealt with exchange uncertainties by decreasing foreign-denominated debt as their foreign exchange volatility exposure increased after GFC. In contrast, the influence of conventional exchange exposure on foreign-denominated debt has diminished.

Future research should focus on several points. First, additional research could extend to foreign investors who have divergent perception and consideration in regard to foreign exchange risk management. Second, research on decision making and motivation in foreign currency choice should be conducted in order to deepen academic understanding. Third, research that refines the variables added in the current research should be conducted. Finally, as a way to manage foreign exchange volatility exposure, further investigation based on this study is possible.

The results of this study have several important theoretical and empirical implications for companies’ foreign exchange risk management strategy. First, through foreign exchange volatility exposure, which can usefully take over the role of the existing foreign exchange exposure, the authors can confirm market uncertainty as being relevant to the foreign exchange risk management strategy. Second, through the financial influence that the foreign exchange volatility exposure has on the foreign currency-denominated debt, the authors can observe the Korean firms’ paradigm shifts in their foreign exchange risk management strategies.

This research confirms the importance of foreign exchange volatility exposure in the research works dealing with firms’ exchange risk management, also the possible influence of foreign exchange volatility exposure in the future might be increased as uncertainty is raised from foreign exchange escalating.

The purpose of this paper is to analyze the nature of returns and volatility spillovers between exchange rates and stock price in the IBSA nations (India, Brazil, South Africa).

The study uses VAR framework and the recently proposed Spillover measure of Diebold and Yilmaz to examine the returns and volatility spillover between exchange rates and stock prices of IBSA nations. In addition, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.

The results of multivariate GARCH model suggests the integration between stock and foreign exchange markets and indicates the existence of bi‐directional volatility spillover between stock and foreign exchange markets in the IBSA countries. Spillover results using the Diebold Yilmaz model suggest the bi‐directional contribution between stock and foreign exchange market, in terms of both returns and volatility spillovers. Overall, results confirm the presence of returns and volatility spillovers within the IBSA nations and, in particular, the stock markets play a relatively more important role than foreign exchange markets in the first and second moment interactions and spillovers.

The market participants may consider the relationship between the exchange rate and stock index to predict the future movement of each other effectively. Multinational companies interested in exchange rate forecasting may consider the stock market as an important attribute. There is an interesting implication for portfolio managers too because of the spillover stock and foreign exchange markets. This knowledge would help to create a fund which performs well. Moreover, the paper can help regulators and policy makers in IBSA nations to understand the structure of the market in a better way and then design their policies.

The study contributes to the literature by extending the existing studies on the spillover between stock price and exchange rate by investigating the issue for three emerging economies, India, Brazil and South Africa. Unlike most studies in the literature which focus on multivariate GARCH model, this is the first study which explores the issue of returns and volatility spillover between the stock prices and the exchange rates using spillover measure of Diebold and Yilmaz and much longer and recent daily data. Moreover, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

This paper aims to explore the empirical determinants of exchange-rate volatility (ERV) in selected Asian economies, namely, Bangladesh, China, India, Indonesia, Malaysia and Pakistan. Specifically, it examines how the volatility of foreign reserves, government spending, industrial production, gold prices and terms of trade affect monthly ERV during the examined period.

The authors carry out the empirical analysis by using monthly data for the period January 1997–March 2019. First, the volatility of the underlying variables is measured based on the conditional variances obtained by estimating the univariate (generalized) autoregressive conditional heteroskedasticity [(G)ARCH] model for each variable during the study period. Next, the autoregressive conditional heteroscedasticity (ARCH)-Lagrange multiplier test is applied to ensure that there are no remaining ARCH effects in the residuals. Finally, the multivariate autoregressive-moving average-GARCH (1, 1) models are estimated to examine whether and how the volatility of the underlying variables affects ERV.

The results reveal that the current period volatility of exchange rates is significantly affected by ERV in the previous period in all selected countries. The results also indicate that the volatilities of the underlying macroeconomic variables are quite differently related to ERV in examined Asian countries. Foreign-reserve volatility (VFXRES) has negative and significant impacts on ERV in Bangladesh, China and Malaysia. Government-spending volatility is negatively related to ERV in India, whereas it is positively related to ERV in all other examined countries. The results also suggest that although terms-of-trade volatility reduces ERV in both Bangladesh and Pakistan, it amplifies ERV in the remaining examined countries. However, gold-price volatility (VGOLDP) significantly, positively contributes to ERV in Bangladesh, Indonesia and Malaysia. On the contrary, the higher volatility in industrial production (VIPI) results in lower ERV in Indonesia and Pakistan, whereas it increases ERV in China, India and Malaysia.

The findings have several important policy implications. First, the findings suggest that both Bangladesh and Malaysia should keep an adequate level of foreign reserves to stabilize their foreign exchange rates. Second, as government-spending volatility has a vital role in determining ERV, it is necessary to bring sustainability and continuity in government expenditures. Bangladesh and Pakistan can stabilize their foreign exchange rates by making exports more competitive, viable and accessible.

This paper significantly contributes to the existing literature by exploring how the behavior of unexpected variations in the factors determining exchange rates affects ERV in selected Asia countries. Most of the published studies have examined the determinants of exchange rates by considering the macroeconomic variables at their levels. Departing from the existing studies, this paper significantly relates the volatility (second moment) of exchange rate determinants to the behavior of ERV. Further, this paper provides firsthand empirical evidence on this issue for the selected Asian economies.

The purpose of this paper is to study the dynamic relationship between foreign exchange and stock returns. Specifically, the authors examine the impact of the 2008 financial crises on the relation between foreign exchange and stock returns in the MENA region.

The authors examine the long-run relation between these two variables using VECM and the authors study the volatility behavior of these two variables using the Dynamic VECH–generalized autoregressive conditional heteroskedasticity (GARCH) model. The sample covers the MENA region over the period 2004–2015.

The results indicate a regime shift in three countries: Egypt, Tunisia and Morocco. In addition, the results assert asymmetric relation between stock returns and changes in exchange rates during pre-crisis and post-crisis periods. Modeling the volatility of the foreign exchange and stock return and their covariance using VECH–GARCH suggests that the persistence in volatility is more prominent in the crisis/post-crisis period as compared with the pre-crisis period. Finally, the authors also find more significant results for the persistence parameter in the covariance between stock return and foreign exchange in the crisis/post-crisis period as compared with the pre-crisis period.

To the best of the authors’ knowledge, the studies by Wong and Li (2010) and Caporale et al. (2014) are the only two that have examined the interaction between stock prices and foreign exchange during the recent financial crisis of 2008. To the authors’ knowledge, none of the previous literature examined the impact of financial 2008 crisis on the relation between foreign exchange and stock prices in the MENA.

This paper examined the volatility models for exchange rate return, including Random Walk model, AR model, GARCH model and extensive GARCH model, with Normal and Student-t distribution assumption as well as nonparametric specification test of these models. We fit these models to Bangladesh foreign exchange rate index from January 1999 to December 31, 2012. The return series of Bangladesh foreign exchange rate are leptokurtic, significant skewness, deviation from normality as well as the returns series are volatility clustering as well. We found that student t distribution into GARCH model improves the better performance to forecast the volatility for Bangladesh foreign exchange market. The traditional likelihood comparison showed that the importance of GARCH model in modeling of Bangladesh foreign market, but the modern nonparametric specification test found that RW, AR and the model with GARCH effect are still grossly mis-specified. All these imply that there is still a long way before we reach the adequate specification for Bangladesh exchange rate dynamics.

This paper aims to investigate the dynamic linkage between stock prices and exchange rate changes for the Gulf Arab countries (Kuwait, Qatar, Saudi Arabia and United Arab Emirates [UAE]).

The author uses the Markov-switching autoregression to detect regime-shift behavior in the stock returns of the Gulf Arab countries and Markov-switching vector autoregressive (MS-VAR) model to capture the dynamic interrelatedness between exchange and stock returns over the period 2000–2018.

This study’s analysis finds evidence to support the persistence of two distinct regimes for all markets, namely, a low-volatility regime and a high-volatility regime. The low-volatility regime illustrates more persistence than the high-volatility regime. Specifically, exchange rate changes do not have an influence on the stock market returns of the Gulf Arab countries, regardless of the regimes. On the other hand, stock market returns have a substantial impact on exchange markets for all countries, except Saudi Arabia, and it is more noticeable during the regime of high volatility.

The findings shed light on the interconnectedness between two of the most important financial markets in the complex international financial environment. They are thus of particular interest for economic policymakers and portfolio investors.

The author distinguishes this study from previous studies in several ways. First, while previous empirical studies of the dynamic linkage between stock prices and foreign exchange markets are primarily devoted to developed markets or emerging markets, this study’s interest is concentrated on four Gulf Arab financial markets (Kuwait, Qatar, Saudi Arabia and UAE). Second, unlike most investigations in the literature that only estimate this link for the whole period, this study attempts to estimate during the good and bad period by using a two-regime MS-VAR model. To the best of the author’s knowledge, this is the first study of the Gulf Arab countries on the stock and foreign exchange markets to apply this model.

The purpose of this paper is to analyze volatility transmission between the Japanese stock and foreign exchange markets.

In contrast to the existing literature, industry‐level stock data are applied to a trivariate Baba, Engle, Kraft and Kroner‐generalised autoregressive conditional heteroscedasticity (BEKK‐GARCH) model that also includes comparable US industrial stocks returns as a control variable.

Using daily data over the study period 1994‐2007, it was found that news shocks in the Japanese currency market account for volatility transmission in eight of the ten industrial sectors considered. Evidence was also found of significant asymmetric effects in five of these industries.

While the BEKK‐GARCH model enables analysis of volatility transmission between the stock and foreign markets against a background of conditional correlation and asymmetries, the model requires the estimation of a large number of parameters, which can be problematic for a limited dataset.

The paper's findings have important implications for understanding international volatility transmission involving the stock and foreign exchange markets. This in turn can provide insight into investor behaviour.

This study aims to extend the literature by extensively investigating the impact of foreign exchange and interest rate changes on the returns and volatility of bank stocks in Saudi Arabia, which is the largest dual banking industry.

This study employs the generalized autoregressive conditional heteroscedasticity (GARCH) model on stock returns of four fully Islamic Saudi banks and eight conventional Saudi banks.

The results showed that the foreign exchange rate return has a positive impact on Saudi conventional bank returns, while it has an adverse impact on Saudi Islamic bank returns. Moreover, a higher interest rate return has a positive impact on Saudi bank stock returns implying that the assets side is more sensitive to changes in interest rates than the liability side. Finally, higher foreign exchange and interest rates volatility increases the volatility of Saudi bank returns, where the former has the largest significant impact. Therefore, Saudi regulators should pay more attention to the risk management of their banks because this could threaten the stability of their financial system.

To the best knowledge of the author, this is the first study that tries to extensively analyze the joint impact of foreign exchange and interest rates on bank stock returns and volatility in Saudi Arabia by applying the GARCH model. The study uses a long data set from 2010 to 2019 that includes all Saudi banks and employs four measures of interest rates to increase the robustness of the results.