European exchange rate volatility dynamics: an empirical investigation

Malik, Ali

doi:10.1016/j.jempfin.2003.09.004

Cited by 19 publications

(7 citation statements)

References 46 publications

(81 reference statements)

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“…For instance, Beltratti and Morana (1999) and Martens (2001) present improved daily volatility forecasts after fitting the GARCH model to half-hour intraday returns. Similar findings appear in Pong, Shackleton, Taylor, and Xu (2004), Malik (2005), and Chortareas, Jiang, and Nankervis (2011). According to Antoniou, Holmes, and Priestley (1998) and Rahman, Lee, and Ang (2002), the property of intraday return exhibits the same stylized facts as daily return such as excess kurtosis, fat tail, skewness, and persistency.…”

Section: Introductionsupporting

confidence: 65%

An empirical study on the role of trading volume and data frequency in volatility forecasting

Liu

Choo

2020

Journal of Forecasting

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This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed-Data Sampling (GARCH-MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and(3) high-frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH-MIDAS in volatility forecasting.

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Section: Introductionsupporting

confidence: 65%

An empirical study on the role of trading volume and data frequency in volatility forecasting

Liu

Choo

2020

Journal of Forecasting

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“…Bubák et al (2011) report the presence of significant volatility spillovers among the Central European (Czech, Hungarian and Polish) foreign exchange markets. Further to these, Malik (2005) finds that the euro was considerably more volatile compared to the British pound, while Nikkinen et al (2006) point out that the volatility of the euro significantly affected the expected volatility of the British pound and the Swiss franc. Equally, Inagaki (2007), Antonakakis (2008) and Kitamura (2010) find the presence of volatility spillovers running from the euro to the British pound.…”

Section: Literature Reviewmentioning

confidence: 99%

The behaviour of asset return and volatility spillovers in Turkey: A tale of two crises

Bajo‐Rubio

Berke

McMillan

2017

Research in International Business and Finance

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This paper examines return and volatility spillovers between the Turkish stock market with international stock, exchange rate and commodity markets. Our aim is not only to examine spillover behaviour with a large emerging market but also to examine crossasset spillovers and how they vary across two periods of financial market crisis; the dotcom crash and the liquidity-induced financial crisis. This is to be compared with existing work that typically focuses on industrialised countries or single asset markets only. Using the spillover index methodology we uncover an interesting distinction between these two periods of markets stress. Over the dotcom period spillovers are largely between the same asset class, notably two exchange rate series and two international stock markets series. However, in the period including the financial crisis, spillovers both increase and cross asset types and suggest a much greater degree of market interdependence. Understanding this changing nature in spillovers is key for investors, regulators and academics involved in theoretical model development.

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“…Based on the findings of the study, even though MAE considers both models equal on forecasting performance, we recommend GARCH (1,1) for volatility forecasting of Indian rupee as it is also consistent with the principle of parsimony. Other things remaining equal, parsimony is a key consideration in modelling as discussed in studies such as [52] [53] and [54]. GARCH (1,1) was also found to be best suited for models exchange rate volatility by many previous studies including those by [44] [55] [56] [57] [58] and [59].…”

Section: Discussionmentioning

confidence: 99%

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

Talwar¹,

Bhat²

2018

TEL

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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.

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European exchange rate volatility dynamics: an empirical investigation

Cited by 19 publications

References 46 publications

An empirical study on the role of trading volume and data frequency in volatility forecasting

An empirical study on the role of trading volume and data frequency in volatility forecasting

The behaviour of asset return and volatility spillovers in Turkey: A tale of two crises

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

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