Exchange Rate Return Volatility Research Articles

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.

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 Interdependence of Tehran Stock Exchange upon the Oil Price and USD Exchange rate return Using Quantile Regression and Time-Frequency Domain Analysis

Compared to GARCH, ARDL, VAR, and similar methods that are commonly used for stock market analysis and portfolio pricing, the quantile regression has proven to be more advantageous. In this study, we combine the quantile regression with wavelet decomposition to analyze different investment horizons in Tehran Stock Exchange. The discrete wavelet decomposition is used to divide the indices time series into short-term (2–16 days), mid-term (16–128 days), and long-term (128–512 days) horizons. The investment horizons are then accurately studied in a bear, normal, and bull market. Since Iran is an oil-exporting country and its economy is highly impacted by fluctuations in the USD exchange rate return, it is of crucial importance to analyze the effects of oil price and free-market USD exchange rate return on the stock market for investment policy-making and portfolio management. The results demonstrate how the exchange rate return volatility and the OPEC basket price fluctuation affect the stock market. The results illustrate strong evidence on the assumption of a long-term strong positive correlation between TSE and the USD exchange rate return increase.

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.

Dependence Structure Between Renminbi Movements and Volatility of Foreign Exchange Rate Returns

This article investigates the linkages of the movements in Renminbi (RMB) to volatility of exchange rate returns of other currencies before and after the yuan devaluation on 11 August 2015. A comparison between the onshore Chinese yuan (CNY) and the offshore Chinese yuan (CNH) is made. Standard regression methods underestimate the tail dependence between yuan and other exchange rate volatility, as financial data are non-normally distributed, especially when extreme event occurs. We apply Gumbel copulas to capture the presence of tail dependence between RMB returns and the volatility of exchange rate returns for 13 selected currencies, and found dependencies not revealed by the standard ARCH models. The tail dependence has increased after the RMB devaluation, suggesting that RMB depreciation is associated with higher downside risks in these currencies. This is most obvious in the currencies of Asian and ASEAN-5 countries that have strong trade and financial linkages with China. The dependence structure has shifted away from the dominance of onshore CNY rates before the devaluation to the growing importance of more volatile offshore CNH rates after the devaluation. Hence, any large depreciation in CNH will lead to a higher volatility in the other exchange rate returns, and the corresponding downside currency risks are higher than those of the CNY.

Modeling Exchange Rate Return Volatility: An Investigation With Garch, Egarch And Igarch Models On Dollar, Euro and Sterling Series

Çalışmada Dolar, Euro ve Sterlin döviz kuru serilerine ilişkin getiri oynaklığı modellemesi yapmak amaçlanmıştır. Bu amaç doğrultusunda 05.01.2000-13.09.2018 dönemindeki günlük verilerin sürekli getirileri hesaplanarak analiz kapsamında incelenmiştir. Oynaklık modellemesi için serinin durağanlığı sağlanmış, Schwarz bilgi kriteri esas alınarak en uygun ARMA başlangıç modeli belirlenmiş ve serilerdeki değişen varyans, otokorelasyon ve doğrusal olmayan unsurların varlığı araştırılmıştır. Analiz sonucunda, EUR serisi için GARCH (2,1), USD serisi için EGARCH (1, 2) ve GBP serisi için IGARCH (1,1) modelleri ile oynaklık modellemesi gerçekleştirilmiştir. Model sonuçlarına göre, EUR serisi için oynaklığa etki eden şokların kalıcı bir etki yaratmadığı tespit edilirken, EUR serisine gelen bir şokun etkisinin ve şoku üzerinden atmasının yaklaşık 8.09 gün sürdüğü belirlenmiştir. USD serisinde meydana gelen pozitif bir şokun, aynı büyüklükteki negatif şoktan daha fazla etki yarattığı ve şokların getiri oynaklığı üzerinde asimetrik bir etkiye sahip olmadığı ortaya çıkarılmıştır. Ayrıca USD serisine gelen bir şokun etkisinin yaklaşık 2.30 gün sürdüğü de belirlenmiştir. GBP serisinde ise şokların kalıcı bir etki yarattığı ve ağırlıklı olarak bir önceki dönemde meydana geldiği tespit edilmiştir.

Economic policy uncertainty and dollar-pound exchange rate return volatility

The extent to which economic policy uncertainty (EPU) amplifies exchange rate volatility has been an important research question for at least a decade. Previous research has investigated this relationship using monthly data, concluding that EPU imparts an effect on exchange rate volatility either contemporaneously, or with a one month lag. The use of monthly frequency, however, may not provide an accurate causal interpretation, and may even compromise the accuracy of the estimates if the natural cycles of EPU are shorter than a month. To address this econometric concern I construct uncertainty measures at a daily frequency, and estimate a GARCH model using daily USD/British pound returns. The evidence indicates that EPU contributes to exchange rate volatility much more quickly than monthly data can detect. Using a regression technique for separating EPU from non-policy economic uncertainty, I find that non-policy market uncertainty increases volatility more than EPU does.

Modeling exchange rate return volatility of RMB/USD using GARCH family models

ABSTRACT The paper examines volatility of RMB exchange rate return of onshore and offshore markets. The onshore rate covered 4/01/2008–5/09/2016 while offshore spanned 31/12/2008-22/09/2016, the returns were not normally distributed and were integrated of order zero I(0). The Ljung-Box Q statistics depicts the presence of autocorrelation in return series and Ljung-Box Q2 statistics of power transformed for conditional heteroscedasticity for lags of 6, 12 and 20 all indicated the presence of conditional heteroscedascity. The exchange rates volatility was persistent in both markets. However, offshore return was more persistent while leverage effects exist in both markets. Asymmetry power Autoregressive conditional Heteroscedastic (APARCH) model was the best model for forecasting purposes in both markets while Glosten, Jogannathan and Rankle, Generalized Autoregressive conditional Heteroscedastic (GJR-GARCH) model and Integrated Generalized Autoregressive conditional Heteroscedastic (I-GARCH) were the worst models in onshore and offshore return markets respectively. APARCH model should be adopted for future studies.

Toyota group firm invests in Anellotech

This paper studies the effectiveness of FX interventions within an inflation targeting context. I estimate the central bank reaction function, using a friction model à la Rosett. Then, I use the conditional expectation of intervention, as an instrument for actual interventions in a reduced form model of exchange rate daily-returns. Results show evidence of threshold effects in the reaction function, and indicate that intervention had a dampening effect over the daily exchange rate return's volatility, but no influence over the level of exchange rate. The central bank reacted systematically to previous-day exchange rate changes and to deviations from short-term trends.

Foreign exchange market interventions under inflation targeting: The case of Guatemala

This paper studies the effectiveness of FX interventions within an inflation targeting context. I estimate the central bank reaction function, using a friction model à la Rosett. Then, I use the conditional expectation of intervention, as an instrument for actual interventions in a reduced form model of exchange rate daily-returns. Results show evidence of threshold effects in the reaction function, and indicate that intervention had a dampening effect over the daily exchange rate return's volatility, but no influence over the level of exchange rate. The central bank reacted systematically to previous-day exchange rate changes and to deviations from short-term trends.

A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

An accurate estimation of exchange rate return volatility is an important step in financial decision making problems. The main goal of this study is to enhance the ability of GARCH-type family models in forecasting the Euro/Dollar exchange rate volatility. For this purpose, a new neural-network-based hybrid model is developed in which a predefined number of simulated data series generated by the calibrated GARCH-type model along with other explanatory variables is used as input variables. The optimum number of these data series and other parameters of the network are tuned by an efficient particle swarm optimization algorithm. Using two datasets of real Euro/Dollar rates, how the proposed hybrid model could reasonably enhance the results of GARCH-type models and the traditional neural network in terms of different performance measures is demonstrated. We also illustrate how the respective simulated data series as the input variable to the network could contribute to improve the accuracy of volatility forecasting.

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.

Interaction between Stock and Foreign Exchange Markets Under the Floating Regime: Evidence from Thailand

This paper explores the nature of the linkage between foreign exchange market and the Thai stock market using monthly data of exchange rate and stock prices during July 1997 to June 2010. The results from cointegration test in a bivariate framework show no long-run relationship between stock prices and nominal effective exchange rate. The noncausality test is thus employed to investigate the existence and direction of causality. It is found that there exists positive bidirectional causality between nominal effective exchange rate and stock prices. This evidence supports the flow-oriented theory, which indicates a positive linkage between stock prices and exchange rate. In addition, the results from a bivariate constant conditional correlation autoregressive conditional heteroskedastic model and the standard Granger causality test in multivariate framework indicate the existence of bidirectional causality between stock return and exchange rate return. Additionally, exchange rate return volatility does not cause stock return to decrease.

Out-of-sample Forecasting Performance of Won/Dollar Exchange Rate Return Volatility Model

Out-of-sample Forecasting Performance of Won/Dollar Exchange Rate Return Volatility Model-Parameter Instability, Volatility Break, Exchange Rate Change, ICSS Algorithm, White Reality Check Test, Hansen SPA Test, GARCH, EWMA, Long Memory Process

Out-of-Sample Forecasting Performance of Won/Dollar Exchange Rate Return Volatility Model ((------ )

English Abstract: We compare the out-of-sample forecasting performance of volatility models using daily exchange rate for the KRW/USD during the period from 1992 to 2008. For various forecasting horizons, historical volatility models with a long memory tend to make more accurate forecasts. Especially, we carefully observe the difference between the EWMA and the GARCH (1,1) model. Our empirical finding that the GARCH model puts too much weight on recent observations relative to those in the past is consistent with prior evidence showing that asset market volatility has a long memory, such as Ding and Granger (1996). The forecasting model with the lowest MSFE and VaR forecast error among the models we consider is the EWMA model in which the forecast volatility for the coming period is a weighted average of recent squared return with exponentially declining weights. In terms of forecast accuracy, it clearly dominates the widely accepted GARCH and rolling window GARCH models. We also present a multiple comparison of the out-of-sample forecasting performance of volatility using the stationary bootstrap of Politis and Romano (1994). We find that the White's reality check for the GARCH (1,1) expanding window model and the FIGARCH (1,1) expanding window model clearly reject the null hypothesis and there exists a better model than the two benchmark models. On the other hand, when the EWMA model is the benchmark, the White's for all forecasting horizons are very high, which indicates the null hypothesis may not be rejected. The Hansen's report the same results. The GARCH (1,1) expanding window model and the FIGARCH (1,1) expanding window model are dominated by the best competing model in most of the forecasting horizons. In contrast, the RiskMetrics model seems to be the most preferred. We also consider combining the forecasts generated by averaging the six raw forecasts and a trimmed set of forecasts which calculate the mean of the four forecasts after disregarding the highest and lowest forecasts from the six models. This experiment confirms that the forecast combinations always outperform forecasts from a single model. Korean Abstract: 1992-2008년간의 일별 대미달러 원화환율시계열에 비모수적 조정과정을 통한 Inclan and Tiao 통계량을 이용하여 구조적 변화를 검정하였다. 원화의 대미달러 일별 환율의 변화는 통계적으로 유의미한 구조적 변화를 겪지 않은 것으로 나타났다. 벤치마크 모형에 의해 생성된 환율수익률 변동성의 표본외 예측력을 비교모형에 의한 예측치와 비교함으로써 환율의 변동성을 예측하기 위한 최적모형을 구축하고자 하였다. 벤치마크 모형으로는 선행연구에서 사용된 바 있는 확장창 GARCH(1,1)모형 (expanding window GARCH(1,1))과 조건부 이분산성의 장기기억프로세스적인 특성을 보다 잘 포착할 수 있는 FIGARCH(1,d,1)모형 등을 사용하였으며, 환율수익률 변동성의 예측력에 있어서 벤치마크 모형과 비교할 모형으로는 환율수익률 비조건부 분산 시계열의 구조적 변화를 고려할 수 있는 rolling window GARCH(1,1)모형을 이용하였다. 본 연구에서는 다양한 변동성 예측모형 간의 예측력 비교를 위하여 2가지 손실함수(loss function)를 사용하였는데, 먼저 Starica and Granger (2005)와 Starica et al. (2005)의 누적MSFE (aggregated version of mean squared forecasting error)를 사용하였으며, 두 번째 손실함수로서 Gonzales-Rivera et al. (2004)의 VaR 손실함수를 사용하였다. 표본외 예측력 분석결과, 역사적 변동성 모형 중에서 변동성의 장기기억특성을 고려하여 과거 관찰치에 대한 가중치가 GARCH 타입의 변동성 예측모형보다 서서히 감소하도록 설계된 EWMA (exponentially weighted moving average)모형의 변동성 예측가능성이 통계적으로 유의미하게 높은 것으로 나타나 대미달러 원화환율수익률의 변동성이 분산시계열의 구조적 변화를 고려하는 단기기억모형의 특성보다는 장기기억특성을 가지고 있음을 확인할 수 있었다.

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.

Time Series Properties of ARCH Processes with Persistent Covariates

We consider ARCH processes with persistent covariates and provide asymptotic theories that explain how such covariates affect various characteristics of volatility. Specifically, we propose and study a volatility model, named ARCH-NNH model, that is an ARCH(1) process with a nonlinear function of a persistent, integrated or nearly integrated, explanatory variable. Statistical properties of time series given by this model are investigated for various volatility functions. It is shown that our model generates time series that have two prominent characteristics: high degree of volatility persistence and leptokurtosis. Due to persistent covariates, the time series generated by our model has the long memory property in volatility that is commonly observed in high frequency speculative returns. On the other hand, the sample kurtosis of the time series generated by our model either diverges or has a well-defined limiting distribution with support truncated on the left by the kurtosis of the innovation, which successfully explains the empirical finding of leptokurtosis in financial time series. We present two empirical applications of our model. It is shown that the default premium (the yield spread between Baa and Aaa corporate bonds) predicts stock return volatility, and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that our model generally performs better than GARCH(1,1) and FIGARCH at relatively lower frequencies.

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.

Global Asset Allocation in Fixed Income Markets

Many global investors are faced with the problem of choosing an appropriate currency allocation of their assets in the capital markets. This paper addresses the asset allocation problem under the assumption that the investment universe is comprised of unhedged risk-free bonds in different countries. In general, the total return arising from holding an unhedged bond portfolio is comprised of two components. One component of the return arises from the bond price changes resulting from yield curve movements and the other component arises from exchange rate fluctuations. In this paper, bond price changes are assumed to be governed by a one factor interest rate term structure model. The return arising from exchange rate changes is extracted by modelling the evolution of exchange rates as a jump stochastic process. The jump process is assumed to occur in the volatility of exchange rate returns. This model is consistent with the empirical evidence that the volatility of currency returns exhibits GARCH behaviour. Using the models that describe the evolution of interest rates and exchange rates, the optimal portfolio allocation problem is solved in a mean-variance setting by Monte Carlo simulation. The out-of-sample performance of the portfolios selected is also presented and is compared against those obtained using other existing methods.

Closed-end country funds and the role of exchange rates in pricing and in determination of premiums and discounts

This paper investigates the relationships between fund returns and exchange rates and between premiums/discounts and exchange rates for European closed-end country funds over the period 1992 through 2003. It focuses on the effects of the adoption of the Euro and its exchange rate implications for the pricing behavior of Euroland country funds. Significant effects are found: fund returns are most affected by exchange rate returns while premiums/discounts are most affected by volatility of exchange rate returns. In general, a depreciation of the local currency (a strengthening of the U.S. dollar) decreases fund returns more after the adoption of the Euro than before. However, introduction of the Euro reduced volatility impacts on premiums/discounts. Thus, it appears that U.S. investors, in a portfolio context, gained from weakened volatility effects but lost from intensified effects of exchange rate changes.