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Delta-neutral volatility trading with intra-day prices: an application to options on the DAX

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  • Schmitt, Christian
  • Kaehler, Jürgen
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    Abstract

    This paper evaluates the profitability of applying four different volatility forecasting models to the trading of straddles on the German stock market index DAX. Special care has been taken to use simultaneous intra-day prices and realistic transaction costs. Furthermore, straddle positions were evaluated on a daily basis to preserve delta neutrality. The four models applied in this paper are: historical volatility, two ARCH models, and an autoregressive model for the volatility index. VDAX. The ARCH models perform best in generating profits for market makers. Forecasts based on historical volatility also produce statistically and economically significant profits over the two-year simulation period of 1993 and 1994. In general, a filter1rule with a small filter of0.5 per cent produces the best results for both the ARCH models and historical volatility. However, the VDAX-AR model generates much lower and usually insignificant profits, and for some filter rules this model even has cumulative losses for market makers. For non-market-makers and non-members of exchange, however, larger transaction\costs imply that no significant profits can be gained with any model of volatility forecasts. --

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    Bibliographic Info

    Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 96-25.

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    Date of creation: 1996
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    Handle: RePEc:zbw:zewdip:9625

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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
    2. Robert F. Engle & Alex Kane & Jaesun Noh, 1993. "Index-Option Pricing with Stochastic Volatility and the Value of Accurate Variance Forecasts," NBER Working Papers 4519, National Bureau of Economic Research, Inc.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    4. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    5. Bera, Anil K & Higgins, Matthew L, 1993. " ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-66, December.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Robert F. Engle & Che-Hsiung Hong & Alex Kane, 1990. "Valuation of Variance Forecast with Simulated Option Markets," NBER Working Papers 3350, National Bureau of Economic Research, Inc.
    9. Hull, John C & White, Alan D, 1987. " The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
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    Cited by:
    1. Krishnamurti, Chandrasekhar & Hoque, Ariful, 2011. "Efficiency of European emissions markets: Lessons and implications," Energy Policy, Elsevier, vol. 39(10), pages 6575-6582, October.

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