Exchange Rate Returns Research Articles

Impact of currency futures on spot rate volatility in Indian foreign exchange market

The work aimed going to ascertain the efficacy of the currency futures on cash-market or Spot exchange rates volatility with respect of USD/INR, EURO/INR, GBP/INR & JPY/INR. The daily closing of have been taken from RBI website. Also, the time series data is checked for stationarity using ADF test. Existence of ARCH effect examined in spot rate residual of the return series data and checked with Autoregressive conditional heteroskedasticity L.M. test by using GARCH (1,1) model. Findings of GARCH model shows that large volatility is persistent pre-introduction of currency futures and finding shows presence of time-varying conditional volatility of returns of currencies spot prices at pre-post and total period of currency futures introduction in currency exchange market. The variances in volatility of the exchange rate returns of pre and post introduction of currency futures time periods being analyzed by using statistical tools. Findings shows to be importantly varied and volatility has been came down in the post futures era also series were found more stable in terms of volatility.

Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model

This paper proposes a dynamic factor model to accurately characterize the dynamic dependence and risk spillovers between the crude oil and exchange rate returns of oil-trading countries from 2000–2020, conditional on the common factors. To this end, we first identify the common factors related to returns of crude oil prices and exchange rates from 14 typical oil-trading countries. Then, we use the AR-GARCH model to filter the respective idiosyncratic factors and conduct a comparative study of conditional dynamic dependence between the crude oil and exchange rate returns of oil importers and exporters. Finally, we combine the dynamic factor copula model with the CoVaR method to measure the conditional risk spillover effect between crude oil and exchange rate markets. The empirical study indicates that the classical factor analysis can be used to precisely identify the common factors related to both financial markets, with the similar trend of macro-economic indicators. Furthermore, the factor copula model can capture the dynamic structure between crude oil and exchange rate markets more accurately than the traditional Copula–GARCH model. Specifically, the idiosyncratic factors related to each return series still have a significant impact on the dependence between the crude oil and exchange rate returns of oil importers, while the common factors have played an important role in the relationship of oil exporters’ exchange rates with crude oil prices. Finally, the crude oil market has enjoyed a relative risk premium to the exchange rate markets of oil-trading countries. However, there is almost no conditional risk spillover from the corresponding exchange rates to the crude oil prices. Finally, we discuss the implications for investors, policymakers and respective exchange rate regulators from oil-trading countries with further insights from the macro-economic perspective.

Systematic variations in exchange rate returns

This paper explores the latent factor structure of systematic variations in exchange rate returns among 43 currencies from 2001 to 2017. The exchange rate return variation is decomposed into a world factor, group factor and country-specific factor by employing a dynamic factor model. We find that the world factor is the main driving force in exchange rate return variation in industrialized and European emerging market economies (EME). Based on the Lasso family methods, this study finds that the relationship between the systematic variations in exchange rate returns and economic fundamentals has changed over the observation period.

Post-Covid-19 Volatility of Exchange Rate Returns: A Case Study of Pakistan

The purpose of the study is to forecast the volatility for returns of the exchange rate of Pakistan concerning US dollars along with the impact of covid-19 so that we can find out the feasibility of holding this asset along with the risk and returns associated with it. For this purpose daily data has been taken from the State bank of Pakistan on a period from February 01, 2001, to June 30, 2021, where covid-19 is used as a dummy variable. Furthermore, in methodology, we applied GARCH models after finding the presence of the ARCH effect which is at ARCH (6) in the series. It is found in all GARCH models that the past volatility of the exchange rate returns has a statistically significant influence on the current volatility of the exchange rate means there is time-varying and time-correlated volatility associated with exchange rate returns. According to GARCH-M, GARCH-M (variance) (1,1) and GARCH-M (SD) (1,1) results it is concluded that average returns of exchange rate are small but significant and there is no risk factor associated with exchange rate returns but the past square residual terms have a significant impact on risk volatility. Furthermore, Both T-GARCH and E- GARCH depicts that the impact of covid-19, which is bad news, although has a significant impact but its magnitude has a lesser influence on exchange rate volatility than the good news.

Climate risks and realized volatility of major commodity currency exchange rates

We find that climate-related risks forecast the intraday data-based realized volatility of exchange rate returns of eight major fossil fuel exporters (Australia, Brazil, Canada, Malaysia, Mexico, Norway, Russia, and South Africa). We study several metrics capturing risks associated with climate change, derived from data directly on variables such as, for example, abnormal patterns of temperature. We control for various other moments (realized skewness, realized kurtosis, realized upside and downside variance, realized upside and downside tail risk, and realized jumps) and estimate our forecasting models using random forests, a machine learning technique tailored to analyze models with many predictors.

Heterogeneity dependence between oil prices and exchange rate: Evidence from a parametric test of Granger causality in quantiles

This paper re-examines the nexus between crude oil price and exchange rate by investigating their heterogeneity dependence structure within the framework of Granger causality in quantiles for a sample of developed and emerging economies (namely UK, Canada, Brazil, Russia, Mexico, Norway, India, Japan, South Africa, South Korea and European Union (EU)). The results indicate no distinct causality between the crude oil price changes and the real exchange rate returns for all countries besides Russia at the median of the conditional distribution. Besides, the crude oil price changes influence the exchange rate returns in all countries, except Norway and EU, particularly around the tails of the conditional distributions of exchange rate returns. This suggests that the oil price changes influence the real exchange rate returns when the real exchange rate returns are either in extreme appreciation or depreciation. Moreover, the crude oil price movement can be explained by the exchange rate returns for most oil importers only when the crude oil market is bearish or bullish. By contrast, the real exchange rate can permanently affect the crude oil price for most oil-importing countries irrespective of the crude oil market's state. Finally, our findings provide an essential reference for managing the extreme risk dependence between the exchange rate market and the crude oil market.

Analysis of Exchange Rate Volatility in Peru in the Presence of Structural Breaks

The presence of structural breaks in the analysis of volatility in the financial field has been ignored in research done in Latin America. This paper fills this gap by analyzing the behavior of the dollar exchange rate in Peru by evaluating the impact of structural breaks in volatility forecasting. The return behavior was analyzed for the period 05/01/2010 to 09/30/2021. Econometric analysis was used, which consisted of: (1) use of the modified Iterative Cumulative Sum of Squares (ICSS) algorithm to determine the structural breakpoints; (2) estimation of GARCH Models for the subsamples originated by the identified breakpoints; and (3) comparison of alternative models with the GARCH(1,1) expanding window model for horizons of 1, 20, 60 and 120 days. The ICSS algorithm identified 8 breaks in volatility behavior. The models were compared based on out-of-sample forecast performance. It was determined that the GARCH model that considers structural breaks is only effective for a one-day horizon. Finally, the GARCH(1,1) 0.25 rolling window model provides a better strategy for forecasting the volatility of exchange rate returns in Peru for longer horizons.

The dynamic moderating function of the exchange rate market on the oil-stock nexus

Oil price movements have an important impact on the stock market, and this impact could be dynamically moderated by the exchange rate, which could not be effectively depicted by VAR or GARCH methods widely used in previous studies. This paper adopts a more flexible nonlinear model to investigates this dynamic moderating effect of the exchange rate market on the oil-stock nexus for 45 major countries from November 30, 2005 to November 22, 2019. We also compare the differences in this moderating effect between oil-importing and oil-exporting countries and confirm the presence of a wealth transfer effect. Specifically, the empirical results show that (1) In the stage where domestic currency depreciation or appreciation degree is not serious (the growth rate is less than 0.1), an increase in crude oil prices has a positive impact on stock market returns, and this positive impact is weakened when the growth rate of the exchange rate return approaches zero. (2) As the local currency continues to appreciate (the growth rate is greater than about 0.22), the increase in crude oil prices may negatively influence stock market returns to an increasingly greater extent among crude oil importers. (3) The increase in crude oil prices may have a short-term positive impact on stock market returns in oil-exporting countries due to the wealth transfer effect when the domestic currency appreciates at a faster rate. Finally, we discuss the policy implications of our findings to help investors avoid risks due to fluctuations in international oil prices.

ON MULTIPLE STRUCTURAL BREAKS IN DISTRIBUTION: AN EMPIRICAL CHARACTERISTIC FUNCTION APPROACH

We estimate and test for multiple structural breaks in distribution via an empirical characteristic function approach. By minimizing the sum of squared generalized residuals, we can consistently estimate the break fractions. We propose a sup-F type test for structural breaks in distribution as well as an information criterion and a sequential testing procedure to determine the number of breaks. We further construct a class of derivative tests to gauge possible sources of structural breaks, which is asymptotically more powerful than the smoothed nonparametric tests for structural breaks. Simulation studies show that our method performs well in determining the appropriate number of breaks and estimating the unknown breaks. Furthermore, the proposed tests have reasonable size and excellent power in finite samples. In an application to exchange rate returns, our tests are able to detect structural breaks in distribution and locate the break dates. Our tests also indicate that the documented breaks appear to occur in variance and higher-order moments, but not so often in mean.

Robust international portfolio optimization with worst‐case mean‐CVaR

We propose a robust international portfolio optimization model in a worst-case mean-CVaR framework. In our model, we assume that the distributions, the first- and second-order moments of returns of assets and exchange rates are ambiguous. To control the conservatism of our robust model, we incorporate a new support set constructed by intervals of deviations from the no-arbitrage condition in currency markets into the ambiguity set of distribution. Our model can be reformulated as an equivalent semi-definite programming problem, which is computationally tractable. We conduct empirical experiments by the weekly rolling window strategy during the total period, the 1997 Asian Financial Crisis period, and the recent stable period. Using various performance measures, we investigate the out-of-sample performance of our model with comparison to those of other four benchmark models. The experimental results demonstrate that our model has the best performance in terms of return and various risk adjusted return measures during all of the three periods. They suggest that investors can obtain significant benefits when employing the robust portfolio strategy and the new support set, and considering the ambiguity of the first- and second-order moments during both volatile and stable periods.

Robust International Portfolio Optimization with Worst-Case Mean-LPM

This paper proposes a robust international portfolio optimization model with the consideration of worst-case lower partial moment (LPM) and worst-case mean return. In our model, we assume that the distributions and the first- and second-order moments of distributions of returns of assets and exchange rates are all ambiguous. The proposed model can be reformulated into an equivalent semidefinite programming (SDP) problem, which is computationally tractable. For investigation of the performance of our model, we also give two benchmark models. The first benchmark model is a scenario-based model which uses historical observations of returns to approximate the future distributions. The second benchmark model only considers the ambiguity of distributions but does not consider the ambiguity of the first- and second-order moments of distributions. We conduct empirical experiments in a rolling forward way to evaluate the out-of-sample performances of our proposed model, the two benchmark models, and an equally weighted model using the return measures and various risk-adjusted return measures. The result shows that our model has the best performance. It verifies that investors can obtain benefits when employing the robust model and considering the ambiguity of the first- and second-order moments of distributions.

Exchange rate return predictability in times of geopolitical risk

We develop the hypothesis that geopolitical risk predicts exchange rate returns. Using data on 17 countries, we demonstrate that the information content embedded in geopolitical risk is economically useful and can improve the forecast accuracy of exchange rate returns. We show that geopolitical risk predicts 10 out of 17 (59%) exchange rate returns in in-sample tests while in out-of-sample tests predictability is found for 88% of currencies. Buy and sell signals generated from our model lead to higher returns compared to the historical average model. Our model delivers excess profits relative to the benchmark model in 11 out of 17 (65%) currencies.

Technical trading rules' profitability and dynamic risk premiums of cryptocurrency exchange rates

PurposeThe study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.Design/methodology/approachThe study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.FindingsThe findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.Practical implicationsThe empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.Originality/valueThe study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

Multivariate Hyper-Rotated GARCH-BEKK

Abstract For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal of Econometrics 179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters.

Exchange Rate, COVID‐19, and Stock Returns in Africa: Insights from Time‐Frequency Domain

We examine the co‐movement between exchange rate (EXR) returns and stock (STK) returns in Africa amid COVID‐19 in a time and frequency domain. Therefore, we employ the bi‐ and partial wavelet and the wavelet multiple correlation techniques using daily data from 13 February 2013 to 6 May 2021. Our findings divulge that COVID‐19’s effect does not increase the intensity of the relationship between EXR and STK returns in Africa but causes a significant difference in the lead‐lag relationship between the two assets. We find a strong likelihood for a high market integration between African markets in the long run, regardless of market conditions. Under general market conditions, South African (Namibian) equities have the lead/lag potential in the short and long run (intermediate term). Namibian stocks are the first to respond to shocks before all other remaining variables in the intermediate term, while in the long term, the South African EXR market is the last variable to experience shocks. Owing to the recently intensified alliances between African markets, investors should be wary of the specific African equities they include in their portfolios in the periods ahead. Policymakers are required to have an in‐depth understanding of the nature of the co‐movement between the variables to ensure timely and operative policy responses are rolled out to minimise adverse fluctuations in stocks and the local currencies.

Emerging Market Bond Flows and Exchange Rate Returns

Emerging Market Bond Flows and Exchange Rate Returns

Time-series dynamics of Baltic trade flows: Structural breaks, regime shifts, and exchange-rate volatility

Aim/purpose – In the decades since their reintegration with the West, the small open economies of Estonia, Latvia, and Lithuania have seen their trade flows grow substan- tially. While the mix of trade partners has evolved over time, the region has been affect- ed by various political and economic shocks. This study examines the bilateral trade balances between the Baltic countries and nine partners to investigate whether there have been structural breaks due to political or economic events. Because these events may have been “priced into” exchange rates or increased these rates’ volatility, connections between these variables and trade balances are also considered. Design/methodology/approach – Monthly data beginning in 1994 are taken from the International Monetary Fund’s Direction of Trade Statistics [DOTS]. Trade partners include the Nordic countries of Finland, Sweden, and Norway, as well as Poland, Russia, and the United States and country groupings such as the CIS, Advanced Economies, and the World. Ratios of the export and import values are used to create bilateral trade bal- ances. The Bai–Perron (1998) structural break test is then used to identify “break points” that can classify time periods into regimes. Baltic nominal and real effective exchange rates, both in log changes and as a GARCH-based volatility measure, show whether regimes correspond to competitiveness or risk. Correlations are calculated to show links between bilateral trade balances and real exchange rates. Findings – Each trade balance has at least one structural break; many have more. In fewer than half of the cases do these correspond to specific events such as EU accession or the Global Financial Crisis. Trade with Russia has decreased, particularly for Estonia and Latvia. But many partners with historical ties, such as Estonia-Finland, Latvia- -Sweden, and Lithuania-Poland have more breaks than do other partners (such as Estonia- -Poland). Structural breaks in real exchange-rate returns and volatility do not match those of trade balances, and correlations between returns and trade balances are low. Research implications/limitations – These findings open the door to future research on the macroeconomic and cultural/historical factors behind these trade linkages and any changes in regimes. However, no structural determinants have yet been estimated. Originality/value/contribution – This study isolates changes in trade regimes, which can be further explained by specific events or particular dates. It also shows that variance has changed as well as the mean, but this differs by country and by the partner. Keywords: Trade flows, Baltics, time series, structural breaks. JEL Classification: F14, F4, C1.

Exchange Rate Volatility in the Covid-19 Period: An Analysis Using the Markov-Switching ARCH Model

Most analyses of exchange rate volatility in the economic literature are conducted by means of autoregressive conditional heteroskedasticity (ARCH) or generalized ARCH (GARCH) models. According to Humilton and Susmel such models often predict higher volatility than their actual volatility rates, and their predictive performance is considerably low. Diebold and Lamoureux and Lastapes attributed this to the structural change in the ARCH process. Moreover, Hamilton and Susmel developed the Markov-switching ARCH (MS-ARCH or SWARCH) model to overcome the reliability problem of parameter estimates that do not allow for a regime change. This method presents a nonlinear structure enabling regime changes. Therefore, the MS-ARCH method was preferred in the study. Considering the continuing massive impact of COVID-19 on the global financial system, its influence on exchange rates must also be explored. This question was addressed in the analysis. In this direction, the effect of volatility was estimated with the MS-ARCH model using the return values of USD/TRY exchange rate in the trading days between March 2020 and October 2021, the month March 2020 when the first COVID-19 case appeared in Turkey. Two volatility regimes, namely, low volatility and high volatility, were employed in the study. The findings demonstrate that the COVID-19 pandemic, along with various economic and political events in Turkey and the world, affects exchange rate volatility and that these volatility periods are permanent. It also depicts that the USD/TRY return series has high volatility and a strong regime dependency. From these results, it may be concluded that the forecasting of information on exchange rate volatility is important for asset pricing and risk management because exchange rate volatility can increase transaction costs and reduce gains in international trade. The article contributes to the existing body of literature by explaining volatility modeling in the light of the recent daily exchange rate returns during the COVID-19 pandemic.

An empirical study of international oil price and RMB exchange rate return spillover effect

Using the spillover index method proposed by Diebold and Yilmaz(2012),we examine the return spillover effect between oil market and three major markets of the RMB exchange rate (CNY, NDF, CNH market ) .We find that there exists a return spillover effect between oil market and three RMB exchange rate markets, and oil price is the absolute contributor of information overflow. Furthermore, we explore the impact of “811” exchange rate reform on the relationship between oil and exchange rate market. We find after the reform, the spillover from oil market to exchange rate market decreases while the spillover in the opposite direction increases. We also employ the rolling window technique and find return spillovers between the oil and exchange rate markets are time-varying.

Value-At-Risk Analysis Using ARIMAX-GARCHX Approach For Estimating Risk Of Bank Central Asia Stock Returns

Before buying a stock, an investor must estimate the risk which will be received. VaR is one of the methods that can be used to measure the level of risk. Most stock returns have a high fluctuation, so the variant is heteroscedastic, which is thought to be caused by exogenous variables. The time series model used to model data that is not only influenced by the previous period but is also influenced by exogenous variables is ARIMAX. In contrast, the GARCHX model is used to obtain a more optimal stock return data model with heteroscedasticity cases and is influenced by exogenous variables. This study uses the ARIMAX-GARCHX model to calculate the VaR of the stock returns of PT Bank Central Asia Tbk. The exogenous variables used are the exchange rate return of IDR/USD and the return of the JCI in the period January 3, 2017, to March 31, 2021. The best model chosen is the ARIMAX(2,0,1,1)-GARCHX(1,1,1). VaR calculation is carried out with the concept of moving windows with time intervals of 250, 375, and 500 transaction days. The results obtained at the 95% confidence level, the maximum loss obtained by an investor is 1,4%.