Exchange Rate Return Series Research Articles

Cross-spectral coherence and co-movement between WTI oil price and exchange rate of Thai Baht

This paper investigates the cross-spectral coherence and co-movement between the monthly return series of WTI oil price and exchange rate of Thai Baht against USD from 1986 to 2019. We use a quantile cross spectral (coherency) approach and time frequency wavelets as estimation techniques. Results from the quantile coherency analysis reveal a negative spillover effects of oil price and Thai exchange rates in the short, medium, and long run which connotes that oil market poses systemic risk to the foreign exchange market in Thailand in short, medium and long term. From the wavelets analysis, we first note that there is no co-movements at high frequency (i.e. short term). However, additional results show positive and negative co-movements between oil price and Thailand-US exchange rate in different periods and different frequencies with the effect been stronger during extreme volatility periods. Policy implications are derived at the end of the article.

Pandemic‐induced fear and government policy response as a measure of uncertainty in the foreign exchange market: Evidence from (a)symmetric wild bootstrap likelihood ratio test

AbstractThe paper aims to examine the ability of a global fear index (GFI) based on the COVID‐19 pandemic and government policy responses as a measure of uncertainty in predicting eight Indian rupee‐based exchange rate return series: the Australian dollar, the Canadian dollar, the Swiss franc, the US dollar, the euro, the British pound sterling, the New Zealand dollar, and the Japanese yen. The predictability of the daily Indian rupee‐based exchange rate return series is tested using the recently developed wild bootstrap likelihood ratio test of Kim and Shamsuddin for the period 2 October 2020 to 8 March 2021. Both symmetric and asymmetric tests revealed GFI as an insignificant determinant of the Indian rupee‐based exchange rate return series. However, government policy responses are a significant determinant of the rupee–dollar exchange rate return series.

The Relationship of BIST Sector Indices with Exchange Rate Volatility

Through globalization, the increased integration in financial markets has made the relationship between exchange rate and stocks important. The study aims to model the exchange rate volatility using daily data for the period 04.01.2010-15.10.2020 and investigate the causality relationship between sector returns and exchange rate return volatility. In order to model the volatility of the exchange rate return series, the GARCH model was used to reveal the possible asymmetry feature in the series. As a result of the model applications, GARCH (2,2) was determined as the most suitable model to measure volatility modelling. Then, the Granger causality test was used to see whether there is a relationship between BIST sector return indices and exchange rate return volatility. As a result of the study, one notes that there is a uni-directional causality from the exchange rate return volatility series to the service, technology, and industrial sector indices. There is a bi-directional causality relationship between the financial sector index and the exchange rate return volatility series. It is noteworthy that the causality relationship between the BIST100 index and the exchange rate is towards the volatility of the exchange rate return series from the BIST 100 index, unlike the sector indices. According to this result, it is seen that the changes in the dollar exchange rate affect the decisions of the investors who will invest in the relevant index. The results show that in the case of Turkey, mostly traditional theories are valid.

Using TGARCH, TGARCH-M, EGARCH, EGARCH-M, PGARCH and PGARCH-M models Gaussian and non-Gaussian for modeling (EUR/USD) and (GBP/USD) Exchange Rate

This paper aims to study characteristics of exchange rate volatility of (EUR/USD) and (GBP/USD) using daily closing prices for two time periods, sub-period form (1 January 2015 until 15 may 2020) and full period from(1 January 2010 until 15 may 2020). This is done by studying (TGARCH,EGARCH, PGARCH,TGARCH-M, EGARCH-M, and PGARCH-M) models. The error term assumed five distributional (Gaussian, Student-t, Student-t with fixed df ,(GED) and (GED) with fixed parameter. The results showed that the EGARCH(1,1) has been the best model selected for the return series of ( EUR/USD) exchange rate in the both full and sub period series. The best model selected for the return series of (GBP/USD) exchange rate it was EGARCH(1,1) model in the full period and TGARCH(1,1) model in the sub-period. All four models selected are based on the Student's t distribution to provide the leverage parameter of the EGARCH model of (EUR/USD) in the full time period and sub-period and the EGARCH model of (GBP/USD) in the full time.

Dynamic exchange rate dependences: The effect of the U.S.-China trade war

Since March 2018 the United States and China have been locked in a trade confrontation featured by huge retaliatory tariffs. This paper investigates the impact of the U.S.-China trade war on the dynamic dependences of Chinese Yuan (CNY) and the currencies of its major trade partners merging a generalized autoregressive score-driving (GAS) model and the copula approach. The GAS framework is used to capture the marginal distributions of each exchange rate return series and then to estimate the dynamic copula correlation between CNY and major trading currencies. We document intra-regional currency contagions that the dependences of KRW-CNY and SGD-CNY have increased and remained at high levels, whereas the dependence of JPY-CNY is reduced after the breakout of the trade war. The dependences of AUD-CNY and EUR-CNY also increase in the post-war period. Appreciations in the USD against target currency and the downside risk of the global economy caused by the trade war are possible factors driving changes in exchange rates and dependences between CNY and currencies of major trade partners.

Modelling Nigeria Naria Exchange Rate against Some Selected Country’s Currencies Volatility: Application of GARCH Model

This paper examines the exchange rate volatility with GARCH-type model of the daily exchange rate return series from January 2012 – August 2016 for Naira/Chinese Yuan, Naira/India Rupees, Naira/Spain Euro, Naira/UK Pounds and Naira/US Dollar returns. The studies compare estimates of variants of GARCH (1, 1), EGARCH (1, 1), TGARCH (1,1) and GJR-GARCH (1,1) models. The result from all models indict presence of volatility in the five currencies and equally indicate that most of the asymmetric models rejected the existence of a leverage effect except for models with volatility break. For GARCH (1, 1), GJR-GARCH (1, 1,) EGARCH (1,1) and TGARCH (1, 1), it was observed that India have the best exchange rate with the highest log-likelihood (Log L) and the lowest AIC and BIC followed by USA, China, Spain and United Kingdom respectively. The four models was later compared for the exchange rates of the five countries under consideration i.e. China, India, Spain, UK and USA to select the best fitted model for each country and it was discovered that GJR-GARCH (1,1) is the best fitted model for all the countries followed by GARCH (1,1), TGARCH (1,1) and EGARCH (1,1) in that order.

MODELING AND FORECASTING EXCHANGE RATE VOLATILITY IN EEC COUNTRIES

This main objective of this paper is to examine the properties of the GARCHmodel and its usefulness in modeling and forecasting the volatility of exchange ratemovements in selected EEC countries (Romania, Hungary and Serbia). The dailyreturns of exchange rates on Hungarian forint (HUF), Romanian lei (ROL) andSerbian dinar (RSD), all against the US dollar are analyzed during the period 03.January 2000 to 15. April 2013 in respect. In order to measure the involved risk,symmetric and asymmetric GARCH models are applied. The accuracy of exchangerate volatility forecast is evaluated through reference to the most commonly usedcriteria. These include a Mincer-Zarnowitz regression based test, Mean AbsoluteError (MAE), Root Mean Square Error (RMSE) and Diebold and Mariano test (DMtest). The results of Mincer-Zarnowitz regression test for selected exchange ratereturn series showed a clear lack of explanotory power and sub-optimality of theTGARCH model. The results of the Mean Absolute Error (MAE) and the Root MeanSquare Error (RMSE) for the forecasted volatility showed that symmetric modelbetter predict conditional variance of the exchange rate returns, but estimating resultsindicating that the parameters of forecasts are not satisfactory, i.e. models have littlepredictive power. Results for Diebold-Mariano test results for Diebold-Mariano testshowed that symmetric model outperforming TGRACH forecast in case of Hungarianforint and Serbian dinar sample series, and that only in case of Romania lei TGARCHoutperforming the GARCH forecast.

Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.

An empirical analysis of stochastic behavior of Sri Lanka exchange rate changes

The main purpose of this study was to explore the main characteristics of stochastic behaviour of Sri Lankan exchange rate aginst to US dollar,(LKR/US$). This study used daily spot exchange rate time series collected from Central Bank, Sri Lanka web site. The study covers the time period from 2008 to 2010, which represents 722 trading days. The sample period was divided into two. One period was from January 1, 2008 to May 19, 2009 and the other period from May 30, 2009 to December 31, 2010. Graphical techniques, Kernel density function, autocorrelation function, and GARCH models were used to analyse the behaviour of the exchange rate in this study. The results show that basic statistical properties of Sri Lankan exchange rate series was a nonlinear, asymetric shape ,nonstationary series with stochastic trend, I(1). The change in the logarithm of the daily exchange rate (Exchange rate return) series has fatter tails, serial dependence, volatility clustering and ARCH effects in both sample periods. During the period I, the exchange rate was depreciating, distribution was positively skewed , larger volatility (SD=3.4) , non normal and nonstationary. During the period II, exchange rate was appreciating, high persistent and skewed negatively. The changes of log exchange rate behave as normal with an autoregressive conditional heteroscedasticity process for innovations. The characteristics of the exchange rate changes indicates the presence of heterogeneity among market participants as well as changing parameters over time. Standard deviation of this distribution dominates the mean value. Variance was also time varying. The results of this study has important implications for exchange rate determination, balance of payments, risk modeling and management, forecasting, market efficiency, statistical inference in empirical work and for the economy, as whole.

Oil Price and Exchange Rate in Malaysia: A Time-Frequency Analysis

The study analyzed the Granger-causal relationship in the time-frequency framework between return series of real oil price (ROP) and real effective exchange rate (REER) for Malaysia. In doing so, study relied on time-frequency framework of the Granger-causality, which is based on continuous wavelet approach. We found that the causal and reverse causal relations between oil price and real exchange rate vary across scale and period viz., during late 1989, in the time scale of 8~10 months, both variables were in phase and ROP was leading and both variables were out of phase and ROP was leading (a) in 1990-1991, in the time scale of 12~16 months, (b) in 1997 -1998 in the time scale of 10~16 months, (c) in 2001-2003, in time scale of 9~15 months, and (d) in 2005 and early 2006, in the time scale of 2~7 months. Further, evidence shows that during 1989-1998, in 32~48 months scales, variable were in phase and ROP was lagging and throughout the study period, in 60~64 months scale, variables were in phase and ROP was leading. Hence, our evidence show that there is evidence of both cyclical and anti-cyclical relationship between ROP and REER at shorter time scales however, throughout study for higher scales REER was lagging and receiving cyclical effects of ROP shocks. Findings obtained in the study have implications for central monetary authority of Malaysia in the formulations of appropriate monetary and exchange rate policies and for traders in the formulations of effective risk management.

Testing the Presence of Structural Break in the Euro Exchange Rate Variance

We investigate the presence of structural breaks in the euro exchange rate conditional variance from the GARCH models and how does it influence the Value at Risk metrics. We use daily exchange rate for the euro/dollar, euro/£, euro/CHF, euro/yen for the 01:1999 - 09:2013 in order to test the influence of structural breaks on the GARCH models. The structural break in the series mean and variance are identified using the PELT algorithm, the structural breaks dates are captured using dummy variables in the GARCH models, the selection of models is done using the informational criterion [Akaike, Schwarz, Log-likelihood]. We find evidence of structural breaks in the unconditional variance of euro exchange rate return series over the 1999-2013 period, we test the performance of the structural GARCH(1,1) models versus the GARCH(1,1) and find that structural GARCH models outperforms when forecasting exchange rate return volatility in real time.

Two EGARCH Models and One Fat Tail

We compare two EGARCH models which belong to a new class of models in which the dynamics are driven by the score of the conditional distribution of the observations. Models of this kind are called dynamic conditional score (DCS) models and their form facilitates the development of a comprehensive and relatively straightforward theory for the asymptotic distribution of the maximum likelihood estimator. The EGB2 distribution is light-tailed, but with higher kurtosis than the normal. Hence it is complementary to the fat-tailed t. The EGB2EGARCH model gives a good …t to many exchange rate return series, prompting an investigation into the misleading conclusions liable to be drawn from tail index estimates.

Volatility Modeling of Emerging Foreign Exchange Market: A Case of Bangladesh

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.

Volatility Modeling of Emerging Foreign Exchange Market: A Case of Bangladesh

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.

Do structural breaks in exchange rate volatility matter? Evidence from Asia-Pacific currencies

Using the U.S. dollar exchange rate return series of three major Asia-Pacific currencies, this paper investigates the empirical relevance of structural breaks in exchange rate volatilities. We find significant evidence of structural breaks in the unconditional variances of all three exchange rate returns, implying unstable GARCH processes for these exchange rates. Various methods of accommodating structural breaks were considered when forecasting daily exchange rate volatility using GARCH models. In sharp contrast to previous evidence from currencies of developed countries, accommodating structural breaks, however, did not improve out-of-sample forecasts of exchange rate volatility, i.e., a simple GARCH(1,1) with expanding window model performed best in forecasting exchange rate volatilities in these emerging markets.

Efficiency tests of foreign exchange markets for four Asian Countries

This paper uses the traditional variance ratio test of Lo and MacKinlay (1988, 1989), the non-parametric-based variance ratio test of Wright (2000) and the multiple-variance ratio test of Chow and Denning (1993), to re-examine the validity of the weak form efficient market hypothesis for foreign exchange markets in four floating-rate markets in neighboring Asian economies (Japan, South Korea, Taiwan and the Philippines). The results show that the random walk patterns of the exchange rate return series cannot be rejected, with the one exception of Taiwan, where inefficiency is shown to be most prominent. We therefore conclude that the foreign exchange markets of Japan, South Korea and the Philippines are weak form efficient, while the foreign exchange market of Taiwan is inefficient.

Long‐term Dependence in Asian Foreign Exchange Markets

We investigate the long‐term dependency behavior of Asian foreign exchange markets by using rescaled range analysis. Emerging markets in Korea, Taiwan, India, and Thailand, show evidences of long memory in the exchange rate return series, while the exchange rate return persistence is not found in more developed and mature markets in Japan, Australia, Hong Kong, and Singapore. Our results suggest that the return‐generating processes and presence of long memory depends on the degree of market development. In addition, the findings suggest that Asian financial crisis affects long‐term dependences of Korean won and Thai baht in which their economies and currency were hard hit by the crisis.

The effects of structural breaks and long memory on currency hedging

AbstractEmpirical evidence suggests that unconditional variance of exchange rate return series is subject to occasional structural breaks that may induce spurious phenomenon of high persistence and long memory of volatility processes. In this study, we investigate the effects of such breaks on estimated risk‐minimizing hedge strategies (ratios) and their performance in currency markets. Using bivariate GARCH (BGARCH) and fractionally integrated GARCH models, we estimate the hedge ratios for six foreign currencies in the full sample with and without controlling for breaks and each subsample of different unconditional variance regimes identified by a modified version of the Inclan C, and Tiao GC (1994) algorithm. Our findings suggest that daily currency risk can be better hedged with currency futures when controlling for unconditional variance breaks in the BGARCH model. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:607–632, 2010

Integration and Efficiency of Stock and Foreign Exchange Markets in India

This article attempts to examine the integration and efficiency of Indian stock and foreign exchange markets. The study employed Time series ordinary least square regression, Unit Root test, Grangers causality test, Vector Auto Regression techniques on monthly data of stock return and exchange rate return for the period spanning from February 1995 to March 2005.The major finding of this study are as follows. Both the stock indices return (Rsensex and Rnifty) are near normal whereas exchange rate return is not normal and more peak. The stock return and exchange rate return are positively related. The policy implication of this above result of the positive relation between stock return and exchange rate return for the foreign investors in India should be further studied. From the Granger's causality test, it is found that there is no causality for the return series of stock indices and exchange rate except return Nifty and return exchange rate. Weak form of market efficiency hypothesis is also corroborated for stock and foreign exchange markets.

Structural breaks and GARCH models of exchange rate volatility

AbstractWe investigate the empirical relevance of structural breaks for GARCH models of exchange rate volatility using both in‐sample and out‐of‐sample tests. We find significant evidence of structural breaks in the unconditional variance of seven of eight US dollar exchange rate return series over the 1980–2005 period—implying unstable GARCH processes for these exchange rates—and GARCH(1,1) parameter estimates often vary substantially across the subsamples defined by the structural breaks. We also find that it almost always pays to allow for structural breaks when forecasting exchange rate return volatility in real time. Combining forecasts from different models that accommodate structural breaks in volatility in various ways appears to offer a reliable method for improving volatility forecast accuracy given the uncertainty surrounding the timing and size of the structural breaks. Copyright © 2008 John Wiley & Sons, Ltd.