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Volatility Timing and Portfolio Construction Using Realized Volatility for the S&P500 Futures Index

  • Dimitrios D. Thomakos
  • Tao Wang

Abstract

Volatility modeling is important for asset pricing, portfolio choice, option pricing, and risk management. Many studies have built increasingly sophisticated statistical models to capture the characteristics of financial markets’ volatility. A lot of earlier work focused on the parametricARCH and GARCH family of models, on stochastic volatility models, on implied volatility from certain option pricing models, or direct indicators of volatility such as ex-post squared or absolute returns. Partial surveys of the voluminous literature on these models are given by Bollerslev et al. (1994), Ghysels et al. (1996), and Campbell et al. (1997).

Keywords

Bias Correction Implied Volatility Kernel Estimator Average Utility Stochastic Volatility Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of PeloponnesePeloponneseGreece
  2. 2.Rimini Center for Economic AnalysisRiminiItaly
  3. 3.Department of EconomicsQueens College and the Graduate Center of the City UniversityNew YorkUSA

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