Econometric Modeling of Exchange Rate Volatility and Jumps

Erdemlioglu, Deniz; Laurent, Sébastien; Neely, Christopher J.

doi:10.2139/ssrn.2038581

Cited by 14 publications

(8 citation statements)

References 84 publications

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“…According to Brooks [10], maximization of the LLF necessitates minimization of ( ) for this characteristic, fat tailed distribution, the Generalized Error distribution (GED) is widely applied (Erdemlioglu, et al [3]; Palm [30]; Pagan [31]; Bollerslev, et al [32] and Brooks [10]). …”

Section: The Arch-garch Estimationmentioning

confidence: 99%

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

Epaphra¹

2017

JMF

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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.

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Section: The Arch-garch Estimationmentioning

confidence: 99%

“…Thus, policymakers are interested in measuring exchange volatility to learn about market expectations and uncertainty about policy. For example, understanding and estimating exchange volatility is important for asset pricing, portfolio allocation, and risk management (Erdemlioglu et al [3]). …”

Section: Introductionmentioning

confidence: 99%

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

Epaphra¹

2017

JMF

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“…According to Engle (2001), Dowd (2002), Žiković (2008), Erdemlioglu et al (2012) and Duffie and Pan (1997), ARMA models represent the best methodology in measuring exchange rate volatility or respective returns. In addition they provide the option of the largest loss expected calculation under a certain probability level during a given time period (depending on the exposure) for those entities that are exposed to a certain position and,consequently,are directly affected by exchange rate risk (Marcucci 2005).…”

Section: Modelling Institutional Factors In Time Series Analysismentioning

confidence: 99%

Empirical Analysis on the USD/all Exchange Rate Volatility in Albanian Market: Preliminary Results

Todri¹,

Liddo²

2015

Eur. j. econ. bus. stud.

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“…Because of this uncertainty, researchers and policymakers have looked into the nature and scope of their impact on trade volume. As a result, understanding volatility is critical, as currency rate risk can increase transaction costs while reducing returns from foreign trade and this understanding can boost activities such as asset pricing and circumventing risk [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility

Wanjiru¹,

Waititu²,

Wanjoya³

2022

IJDSA

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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 modelbased 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.

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Econometric Modeling of Exchange Rate Volatility and Jumps

Cited by 14 publications

References 84 publications

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

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

Empirical Analysis on the USD/all Exchange Rate Volatility in Albanian Market: Preliminary Results

Deep Convolution Neural Network with Dropout in Modeling Exchange Rate Volatility

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