Modeling Exchange Rate Volatility Research Articles

Modeling Exchange Rate Volatility of ASEAN Member Countries

This study investigates the volatility of exchange rates in nine selected ASEAN member countries, using five forms of the GARCH model. Daily data was sourced from the Bank of Thailand website database, as Baht per foreign currency, over the period from October 2, 2018 to October 7, 2022. This data included Malaysia Ringgit, Singapore Dollar, Brunei Darussalam Dollar, Philippines Peso, Indonesia Rupiah, Myanmar Kyat, Cambodia Riel, Laos Kip, and Vietnam Dong. According to the findings of this study, only eight of the exchange rates were suitable for analysis. In addition, the GARCH ( 1,1) , TGARCH ( 1,1) , and PGARCH ( 1,1) models were determined to be the most applicable, and leverage effects were observed in certain exchange rates. To mitigate the risk associated with trade and investment activities, investors should closely monitor news that is likely to affect the value of exchange rates. In order to design actions that promote exchange rate stability, government agents, on the other hand, must ensure they are current on such news.

Modelling exchange rate volatility under jump process and application analysis

<abstract> <p>Exchange rate is an important part of financial markets. Our analysis finds that the fluctuations of exchange rates have several obvious features, such as spikes, thick tails, fluctuation aggregations and asymmetry. Based on this, we build novel GARCH class model by introducing a jumping process to describe the dynamics of their fluctuations. Our empirical results show that the models with jump factors can better characterize the agglomeration and thick tail characteristics of these return fluctuations than the models without jump factors. In particular, the model with double exponential jumps can fully handle and capture the fluctuation characteristics of the returns. Our findings will be useful for individuals and governments to predict exchange rate fluctuations, provide reference for the effective management of exchange rate risk in China, and further improve the financial risk management mechanism.</p> </abstract>

Modelling exchange rate volatility in turbulent periods: The role of oil prices in Nigeria

The oil price has been increasingly identified as a key fundamental in the dynamics of exchange rates. As a result, we investigate how changes in oil prices affect the dynamics of exchange rates during crisis periods. We hypothesised that the potential of oil prices as an amplifier of exchange rate volatility during crises varies for economic and non-economic crises with divergent origins. Consequently, we classified the crisis sample into two sub-samples: the great recession caused by the 2007 global financial crisis (GFC) is defined as a crisis sample with an economic origin, while the great lockdown caused by the recent COVID-19 outbreaks defined our crisis period with a non-economic origin. We used the GARCH model and its many extensions and noted three findings that strengthened our contributions to the empirical analysis of exchange rate volatility. First, we show that the divergent origins of economic and non-economic crises matter in terms of the extent to which they heighten exchange rate volatility. Second, the persistence of exchange rate volatility during COVID-19 is exacerbated by changes in international oil prices. Finally, our finding of varying dynamics of persistent (transient) exchange rate volatility across different samples of turbulent periods provides investors with evidence-based insights not to generalise their portfolio selection strategy amidst economic crises of different origins.

Shocks and volatility transmission between oil price and Nigeria's exchange rate.

The study examined shock and volatility transmission between oil price and exchange rate markets using daily data covering the period from 23rd October 2009 to 30th November 2020. The contributions of the paper include (i) implementation of VAR-AGARCH model to capture spillover effect of shock and volatility; (ii) examining the nature of shock impact in oil price and exchange rate market; (iii) adopting of two measured of oil price (WTI and Brent); (iv) employing two measures of the exchange rate (USD/Naira and effective/Naira). The study revealed that past own shocks and volatilities significantly contribute to current volatilities in exchange rate and oil price markets. Also, there is bidirectional shock and volatility spillover between the exchange rate (USD/Naira and effective/Naira) and WTI oil price markets. There were bidirectional shock and volatility spillover between USD/Naira and Brent oil price and unidirectional shock and volatility from Brent oil price to effective exchange rate market. We found asymmetric shocks impacting exchange rates and WTI oil price while symmetric shock was observed in Brent oil price. Including innovation in oil price is essential in exchange rate policy formulation and modelling exchange rate shock and volatility.

Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility

Exchange rate data possesses time-series features such as a trend. Based on a convolutional neural network (CNN) deep learning algorithm, which has the advantages of detecting patterns, extracting effective features, finding interdependence of time series data, and its computational efficiency, this paper proposes a convolutional neural network with dropout model-based approach to model and forecast exchange rates. In the meantime, this paper uses the CNN to first model and predict exchange rates and the corresponding results of this model are compared with those of the CNN-WD. The experimental results showed that the CNN-WD is superior to the CNN model in terms of the error value, fitting degree and training time. The dataset used for this research is that of daily exchange rates for the period between December 1, 2003, and October 15, 2021, which is comprised of 6528 daily trading observations. Adjusted closing rates are chosen. First, this paper adopts a CNN to effectively identify patterns and extract relevant data features of the exchange rate dataset, making use of the past 21 days. Dropout regularization is then adopted to help prevent the CNN model from overfitting data by temporarily removing a neuron from the network along with all its incoming and outgoing connections during training if its generated random value is below the set dropout rate. This paper further evaluates the reducibility and identifiability of the CNN-WD. As an application, this paper uses the CNN-WD to forecast the next month’s average tea price in Kenya.

Modeling exchange rate volatility: application of GARCH models with a Normal Tempered Stable distribution

<abstract><p>The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.</p></abstract>

Modelling exchange rate volatility using GARCH models

Modelling exchange rate volatility using GARCH models

An Analyses of Exchange Rate Volatility: A Case of Sane Countries

The sensitivity of exchange rate to an economy has made the study of its volatility essential and that motivated this study in SANE countries. Using GARCH models under different error distributions and monthly data spanning a period of 2016-2020, findings of our study show that exchange rate is volatile in each of the countries sampled. We also found that while some models in each country exhibit mean reversion, others show none mean reversion. The asymmetric parameters of the models show that exchange rate volatility in SANE countries exhibits both positive and negative shocks. Finally, our findings show that other error distributions perform better than the traditional Normal distribution. We therefore recommend that monetary authorities in each of the SANE countries should fine tune their policies on exchange rate to ensure stable and realistic exchange rate regimes which do not hurt the macroeconomic environment. We also suggest that in modelling exchange rate volatility, each country should explore several error distributions so as to avoid biased outcomes.

Modeling exchange rate volatility in the gambia using dynamic conditonal correlaton model

The relationship between different international stock markets is of importance for both financial practitioners and academicians in order to manage risks. Especially after the financial crisis, the pronounced financial contagion draws the public attention to look into such associations. However, measuring and modelling dependence structure becomes complicated when asset returns present non-linear, non-Gaussian and dynamic features. This paper examines the time-varying conditional correlations to the weekly exchange rate returns for the USD, EURO and GBP against the Gambian Dalasi (GMD) during the period 2000 to 2017. We use a dynamic conditional correlation (DCC) multivariate GARCH model. This model can be simplified by estimating univariate GARCH models for each return series, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. DCC-GARCH model was implemented for two different assumptions of the error distribution; assuming Gaussian and Student t-distribution. Empirical results show substantial evidence of significant increase in conditional correlation. It is also clean that, the Student t-distributed errors better forecast the conditional correlation.

Modeling Exchange Rate Volatility in the Presence of Serial Correlations

Modeling Exchange Rate Volatility in the Presence of Serial Correlations

Modelling Exchange Rate Volatility Dynamics: Empirical Evidence From South Africa

In this paper, we extend the literature on modelling exchange rate v olatility in South Africa by estimating a range of models, including some that attempt to account for structural breaks and long memory. We examine the key nominal exchange rates of the South African rand and replicate common findings in the literature; particularly that volatility is ‘persistent’. We investigate whether this ‘persistence’ is due to structural breaks or long memory, and the extent of asymmetric responses of the rand to ‘good news’ and ‘bad news’. Our results show that while long memory is evident in the actual processes, a structural break analysis reveals that this feature is partially explained by unaccounted shifts in volatility regime; the most striking finding is the remarkable fall in the estimates of volatility persistence when considerably more structural breaks than those identified in recent studies are detected and integrated into the generalised autoregressive conditional heteroscedasticity (GARCH) framework. Furthermore, the asymmetric GARCH model results provide evidence of leverage effects, indicating that negative shocks imply a higher next period volatility than positive shocks. The empirical results also shed light on the timing and likely triggers of volatility regime switching.

Unravelling the Cipher of Indian Rupee’s Volatility: Testing the Forecasting Efficacy of the Rolling Symmetric and Asymmetric GARCH Models

Modelling exchange rate volatility is crucially important because of its diverse implications on the profitability of corporations and decisions of policy makers. This paper empirically investigates exchange rate volatility of India’s currency by applying rolling symmetric and asymmetric GARCH models to the USDINR and EURINR daily exchange rates for a period spanning April 1, 2006 through January 31, 2018, resulting in total observations of 2861. To estimate GARCH (1,1) and EGARCH (1,1) models, the data window is rolled over five years with nearly 1200 observations and one month is used as forecast period for each window. Both, in-sample criteria like the log likelihood criteria, Akaike information criterion (AIC), the Bayesian information criterion (SIC) and Hannan Quinn criterion (HQC) as well as the out-of-sample criteria like Mean Squared Error (MSE) and Mean Absolute Error (MAE) have been used to test model fit and forecast accuracy of the models. To test the robustness of the findings, Diebold-Mariano test is used to compare the predictive accuracy of both the models. Further, the forecasting accuracy of the two models has also been tested by splitting the sample period into periods of tranquility and volatility in Indian exchange rate. Results show that GARCH (1,1) model with generalized error distribution is adequate to capture the mean and volatility process of USDINR and EURINR exchange rate returns.

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Policy makers need accurate forecasts about future values of exchange rates. This is due to the fact that exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. This paper applies univariate nonlinear time series analysis to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of exchange rate in Tanzania. To capture the symmetry effect in exchange rate data, the paper applies both ARCH and GARCH models. Also, the paper employs exponential GARCH (EGARCH) model to capture the asymmetry in volatility clustering and the leverage effect in exchange rate. The paper reveals that exchange rate series exhibits the empirical regularities such as clustering volatility, nonstationarity, non-normality and serial correlation that justify the application of the ARCH methodology. The results also suggest that exchange rate behavior is generally influenced by previous information about exchange rate. This also implies that previous day’s volatility in exchange rate can affect current volatility of exchange rate. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since exchange rate volatility (exchange-rate risk) may increase transaction costs and reduce the gains to international trade, knowledge of exchange rate volatility estimation and forecasting is important for asset pricing and risk management.

Modelling exchange rate volatility with random level shifts

ABSTRACTRecent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward biased. In this article, we directly estimate a random level shift model to the logarithm of the absolute returns of five exchange rates series, in order to assess whether random level shifts (RLSs) can explain this long memory property. Our results show that there are few level shifts for the five series, but once they are taken into account the long memory property of the series disappears. We also provide out-of-sample forecasting comparisons, which show that, in most cases, the RLS model outperforms popular models in forecasting volatility. We further support our results using a variety of robustness checks.

Volatility transmission in African foreign exchange markets

Purpose – The concept of volatility transmission and co-movement has witnessed a resurgence in the international finance literature in recent years after the black swan events which gave evidence of financial market linkages. The purpose of this paper is to examine the dynamic sources of volatility transmission in the foreign exchange market in recent financial market integration in Africa. Design/methodology/approach – A conceptual framework was adapted from the extant literature and was used as the basis of modeling exchange rate volatility transmission. This paper adopts a quantitative research approach and opts for augmented DCC model to empirically unearth the sources of exchange rate volatility transmission. Findings – The key findings of the study are that, the African market is more prone to shock from outside than in the region. Macroeconomic news surprises influence volatility transmission and co-movements. Robust support is found for trade balance, interest rate and gross domestic product. These findings clearly demonstrate the low level of financial development and challenges that sometimes exist in exchange rate-policy implementation by policy makers. Research limitations/implications – Interested academics and practitioners working in the area might incorporate bilateral investment into the model of exchange rate correlation in future research. Originality/value – Unilaterally considering exchange rate volatility transmission and subsequent augmentation of the DCC model, this study makes a modest contribution to the examination of exchange rate correlations in Africa. This study makes an important contribution in not only addressing this imbalance, but more importantly improving the relative literature on exchange rate volatility transmission.

Modeling exchange rate volatility in CEEC countries: Impact of global financial and European sovereign debt crisis

The aim of this study is to envisage the impact of global financial (GFC) and European sovereign debt crisis (ESDC) on foreign exchange markets of emerg- ing countries in Central and Eastern Europe CEEC countries (Czech Republic, Hungary, Romania, poland and Serbia). The daily returns of exchange rates on Czech Republic koruna (CZK), Hungarian forint (HuF), Romanian lea (RoL), polish zloty (pLZ) and Serbian dinar (RSD), all against the Euro are analyzed during the period from 3rd January 2000 to15th April 2013, in respect. To examine the impact of global financial crisis and European sovereign debt crisis, dummy variables were adopted. overall results imply that global financial crisis has no impact on exchange rate returns in selected CEEC countries, while European sovereign debt crisis inf luencing in depreciation of polish zloty by 8% and Roma- nian lea by 6%. obtained results by our calculation, imply that global financial crisis increased enhanced volatility on exchange rate returns of Czech koruna, Romanian lea and polish zloty. Moreover, results of empirical analysis imply that this impact has the strongest inf luence in volatility on exchange rate returns of polish zloty.

Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan

What drives volatility in foreign exchange market in Pakistan? This paper undertakes an analysis of modelling exchange rate volatility in Pakistan by potential macroeconomic fundamentals well-known in the economic literature. For this monthly data on Pak Rupee exchange rates in the terms of major currencies (US Dollar, British Pound, Canadian Dollar and Japanese Yen) and macroeconomics fundamentals is taken from April, 1982 to November, 2011. The results show that the PKR-USD exchange rate volatility is influenced by real output volatility, foreign exchange reserves volatility, inflation volatility and productivity volatility. The PKR-GBP exchange rate volatility is influenced by foreign exchange reserves volatility and terms of trade volatility. The PKR-CAD exchange rate volatility is influenced by terms of trade volatility. The findings of this paper reveal that exchange rate volatility in Pakistan results from real shocks than nominal shocks

Modeling moneyness volatility in measuring exchange rate volatility

PurposeThe purpose of this paper is to introduce a model to measure foreign exchange (FX) rate volatility accurately. The FX rate volatility forecasting is a crucial endeavour in financial markets and has gained the attention of researchers and practitioners over the last several decades. The implied volatility (IV) measure is widely believed to be the best measure of exchange rate volatility. Despite its widespread usage, the IV approach suffers from an obvious chicken‐egg problem: obtaining an unbiased IV requires the options to be priced correctly and calculating option prices accurately requires an unbiased IV.Design/methodology/approachThe authors contribute to the literature by developing a new model for FX rate volatility – the “moneyness volatility (MV)”. This approach is based on measuring the variability of forward‐looking “moneyness” rather than use of options price. To assess volatility forecasting performance of MV against IV, the in‐sample and out‐of‐sample tests are involved using the F‐test, Granger‐Newbold test and Diebold‐Mariano framework.FindingsThe MV model outperforms the IV in FX rate volatility forecasting ability in both in‐sample and out‐of‐sample tests. The F‐test, Granger‐Newbold test and Diebold‐Mariano test results consistently reveal that MV outperforms IV in estimating as well as forecasting exchange rate volatility for six major currency options. Furthermore, in Mincer‐Zarnowitz regressions, MV outperforms IV and time‐series models in predicting future volatility.Originality/valueThe authors’ pioneering approach in modeling exchange rate volatility has far‐reaching implications for academicians, professional traders, and financial risk analysts and managers.

Exchange Rate Regimes and Volatility: Comparison of the Snake and Visegrad

Exchange rate stability was defined as one of the prerequisites for monetary integration in Europe. In this paper, we analyze recent developments in the volatility of exchange rates of the Central European countries (the Visegrad Group) and a selected group of European Union countries (the Snake) participating in the former European Monetary System. We compare volatilities in the currencies of both groups under specific exchange rate regimes using two different approaches to modeling exchange rate volatility: squared returns parametric model and GARCH. Both methods provide identical results for the currencies of the Visegrad group: an increase in volatility after a floating exchange rate regime was introduced. The case of the Snake countries exhibits mixed results for two currencies and a concurring result for the others: a decrease in volatility. In one case we are left with an insignificant coefficient. We consider the results as robust and suitable for policy making decisions.

Modelling exchange rate volatility

Two types of statistical models are empirically applied to test the pattern of volatility in the exchange rate markets. One considers the autoregressive models and tests the random walk hypothesis. The other considers the conditional variance process and tests the hypothesis of chaotic dynamics. Empirical results mostly support the random walk hypothesis and also the existence of Lorenz-type chaos.