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Dynamic Volatility Trading Strategies in the Currency Option Market

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  • Dajiang Guo

Abstract

The conditional volatility of foreign exchange rates can be predicted using GARCH models or implied volatility extracted from currency options. This paper investigates whether these predictions are economically meaningful in trading strategies that are designed only to trade volatility risk. First, this article provides new evidence on the issue of information content of implied volatility and GARCH volatility in forecasting future variance. In an artificial world without transaction costs both delta-neutral and straddle trading stratgies lead to significant positive profits, regardless of which volatility prediction method is used. Specifically, the agent using the Implied Stochastic Volatility Regression method (ISVR) earns larger profits than the agent using the GARCH method. Second, it suggests that the currency options market is informationally efficient. After accounting for transaction costs, which are assumed to equal one percent of option prices, observed profits are not significantly differentfrom zero in most trading strategies. Finally, these strategies offered returns have higher Sharpe ratio and lower correlation with several major asset classes. Consequently, hedge funds and institutional investors who are seeking alternative “marketneutral” investment methods can use volatility trading to improvethe risk-return profile of their portfolio through diversification. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Dajiang Guo, 2000. "Dynamic Volatility Trading Strategies in the Currency Option Market," Review of Derivatives Research, Springer, vol. 4(2), pages 133-154, May.
  • Handle: RePEc:kap:revdev:v:4:y:2000:i:2:p:133-154
    DOI: 10.1023/A:1009638225908
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    References listed on IDEAS

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    Cited by:

    1. Wilkens, Sascha & Roder, Klaus, 2006. "The informational content of option-implied distributions: Evidence from the Eurex index and interest rate futures options market," Global Finance Journal, Elsevier, vol. 17(1), pages 50-74, September.
    2. Lanne, Markku & Ahoniemi, Katja, 2008. "Implied Volatility with Time-Varying Regime Probabilities," MPRA Paper 23721, University Library of Munich, Germany.
    3. Helena Chuliá & Hipòlit Torró, 2008. "The economic value of volatility transmission between the stock and bond markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(11), pages 1066-1094, November.
    4. Joseph Zhi Bin Ling & Albert K. Tsui & Zhaoyong Zhang, 2021. "Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    5. Le, Van & Zurbruegg, Ralf, 2010. "The role of trading volume in volatility forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 533-555, December.
    6. Liu, Dehong & Liang, Yucong & Zhang, Lili & Lung, Peter & Ullah, Rizwan, 2021. "Implied volatility forecast and option trading strategy," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 943-954.
    7. Konstantinidi, Eirini & Skiadopoulos, George & Tzagkaraki, Emilia, 2008. "Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2401-2411, November.
    8. Markopoulou, Chryssa & Skintzi, Vasiliki & Refenes, Apostolos, 2016. "On the predictability of model-free implied correlation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 527-547.
    9. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2020. "High frequency momentum trading with cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 52(C).
    10. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.

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    More about this item

    Keywords

    implied volatility; GARCH model; delta; straddle-hedge; trading strategies; C32;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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