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The information content of implied volatility in agricultural commodity markets

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  • GIOT, Pierre

Abstract

In this paper we compare the incremental information content of lagged implied volatility to GARCH models of conditional volatility for a collection of agricultural commodities traded on the New York Board of Trade. We also assess the relevance of the additional information provided by the implied volatility in a risk management framework. It is first shown that past squared returns only marginally improve the information content provided by the lagged implied volatility. Secondly, Value-at-Risk (VaR) models that rely exclusively on lagged implied volatility perform as well as VaR models where the conditional variance is modelled according to GARCH type processes. These results indicate that the implied volatility for options on future contracts in agricultural commodity markets has a high information content regarding conditional variance and VaR forecasts.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2002038.

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Date of creation: 00 Jun 2002
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Handle: RePEc:cor:louvco:2002038

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Related research

Keywords: implied volatility; GARCH; Value-at-Risk; futures; agricultural commodity markets;

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Cited by:
  1. GIOT, Pierre & LAURENT, Sébastien, . "Value-at-Risk for long and short trading positions," CORE Discussion Papers RP -1707, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Pierre Giot and S»bastien Laurent, 2001. "Value-At-Risk For Long And Short Trading Positions," Computing in Economics and Finance 2001 94, Society for Computational Economics.
  3. Baldi, Lucia & Peri, Massimo & Vandone, Daniela, 2011. "Price Discovery in Agricultural Commodities: The Shifting Relationship Between Spot and Future Prices," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114237, European Association of Agricultural Economists.
  4. Luiz Renato Lima & Breno Pinheiro Néri, 2006. "Comparing Value-at-Risk Methodologies," Computing in Economics and Finance 2006 1, Society for Computational Economics.
  5. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute.

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