Who knows more about future currency volatility?

Charoenwong, Charlie; Jenwittayaroje, Nattawut; Low, Buen Sin

doi:10.1002/fut.20351

Cited by 16 publications

(16 citation statements)

References 36 publications

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“…In line with the literature, comparing raw implied volatility forecasts to lagged realized volatility forecasts leads to mixed results (see Andersen, Frederiksen, & Staal, ; Blair, Poon, & Taylor (); Jiang & Tian, ; Martens & Zein, ; Pong, Shackleton, Taylor, & Xu, ). Finally, we find that the historical GJR‐GARCH model underperforms the realized and implied volatility alternatives in almost all cases (similar results are reported by Fleming, ; Jorion, ; Andersen, Bollerslev, Diebold, & Labys, ; Covrig & Low, ; Giot, ; Andersen, Frederiksen, & Staal, ; and Charoenwong, Jenwittayaroje, & Low, , among others).…”

Section: Introductionsupporting

confidence: 85%

An International Comparison of Implied, Realized, and GARCH Volatility Forecasts

Kourtis

Markellos

Symeonidis

2016

Journal of Futures Markets

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We compare the predictive ability and economic value of implied, realized, and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the most accurate predictions, whereas an implied volatility model that corrects for the volatility risk premium is superior at the monthly horizon. Widely used GARCH models have inferior performance in almost all cases considered. All methods perform significantly worse over the 2008-09 crisis period. Finally, implied volatility offers significant improvements against historical methods for international portfolio diversification.

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Section: Introductionsupporting

confidence: 85%

An International Comparison of Implied, Realized, and GARCH Volatility Forecasts

Kourtis

Markellos

Symeonidis

2016

Journal of Futures Markets

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“…They report that, forecasts based on intra day data sometimes outperform option implied forecasts. Finally, Charoenwong et al (2009) assess the predictive power of implied volatility extracted from options traded on different venues namely the Philadelphia Stock Exchange, the Chicago Mercantile Exchange, and the over-the-counter (OTC) market. Their study concludes that, irrespective of the trading venue, implied volatility performs better than time-series forecasts.…”

Section: Literaturementioning

confidence: 99%

The importance of the volatility risk premium for volatility forecasting

Prokopczuk

Simen

2014

Journal of Banking & Finance

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In this paper, we study the role of the volatility risk premium for the forecasting performance of implied volatility. We introduce a non-parametric and parsimonious approach to adjust the model-free implied volatility for the volatility risk premium and implement this methodology using more than 20 years of options and futures data on three major energy markets. Using regression models and statistical loss functions, we find compelling evidence to suggest that the risk premium adjusted implied volatility significantly outperforms other models, including its unadjusted counterpart. Our main finding holds for different choices of volatility estimators and competing time-series models, underlying the robustness of our results.

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“…2 The use of 5-minute intraday data appears to have established as a standard for the analysis of currency markets. 3 In fact, Pong et al (2004) and Charoenwong et al (2009) employ an identical estimator as in (4). As proposed in Andersen and Bollerslev (1997), we use log returns, and we dismiss any data between Friday and Sunday 2100 GMT.…”

Section: Static Hedging and Model-free Variance Forecastingmentioning

confidence: 99%

“…Early work on the subject includes Scott (1992), who introduces the notion of a volatility risk premium, Jorion (1995) and Bates (1996a). Covrig and Low (2003), Christoffersen and Mazzotta (2005) and Charoenwong, Jenwittayaroje and Low (2009) use OTC options to study the accuracy of implied volatility forecasts. While their evidence is ambiguous as to whether or not implied volatility is a biased predictor of future realized volatility, they agree that implied volatility subsumes the information contained in competing time-series models.…”

Section: Introductionmentioning

confidence: 99%

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Variance risk premiums in foreign exchange markets

Ammann

Buesser

2013

Journal of Empirical Finance

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Based on the theory of static replication of variance swaps we assess the sign and magnitude of variance risk premiums in foreign exchange markets. We find significantly negative risk premiums when realized variance is computed from intraday data with low frequency. As a likely consequence of microstructure effects however, the evidence is ambiguous when realized variance is based on high-frequency data. Common to all estimates, variance risk premiums are highly time-varying and inversely related to the risk-neutral expectation of future variance. When we test whether variance risk premiums can be attributed to classic risk factors or fear of jump risk, we find that conditional premiums remain significantly negative. However, we observe a strong relationship between the size of log variance risk premiums and the VIX, the TED spread and the general shape of the implied volatility function of the corresponding currency pair. Overall, we conclude that there is a separately priced variance risk factor which commands a highly time-varying premium.

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Who knows more about future currency volatility?

Cited by 16 publications

References 36 publications

An International Comparison of Implied, Realized, and GARCH Volatility Forecasts

An International Comparison of Implied, Realized, and GARCH Volatility Forecasts

The importance of the volatility risk premium for volatility forecasting

Variance risk premiums in foreign exchange markets

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