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Estimation of stochastic volatility with LRD

Listed author(s):
  • Casas, Isabel

Understanding the behaviour of market prices is not simple. Stock market prices tend to have complicated distributions with strong skewness and fat tails. One important step in forecasting tomorrow’s price is to estimate the volatility, i.e. how much tomorrow’s price is expected to differ from today’s price. In this paper the volatility is assumed to be a lognormal random process and in addition, it may display long-range dependence (LRD). The aim is to obtain the estimates of the mean, standard deviation and LRD parameter of the volatility process of the S&P 500.

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File URL: http://www.sciencedirect.com/science/article/pii/S0378475408000451
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Article provided by Elsevier in its journal Mathematics and Computers in Simulation (MATCOM).

Volume (Year): 78 (2008)
Issue (Month): 2 ()
Pages: 335-340

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Handle: RePEc:eee:matcom:v:78:y:2008:i:2:p:335-340
DOI: 10.1016/j.matcom.2008.01.040
Contact details of provider: Web page: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/

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  1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
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  3. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
  4. Deo, Rohit S. & Hurvich, Clifford M., 2001. "On The Log Periodogram Regression Estimator Of The Memory Parameter In Long Memory Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 17(04), pages 686-710, August.
  5. Fabienne Comte & Eric Renault, 1998. "Long memory in continuous-time stochastic volatility models," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 291-323.
  6. Gao, Jiti & Anh, Vo & Heyde, Chris, 2002. "Statistical estimation of nonstationary Gaussian processes with long-range dependence and intermittency," Stochastic Processes and their Applications, Elsevier, vol. 99(2), pages 295-321, June.
  7. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
  8. Comte, F. & Renault, E., 1996. "Long memory continuous time models," Journal of Econometrics, Elsevier, vol. 73(1), pages 101-149, July.
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