Estimation of stochastic volatility with LRD

I Casas

doi:10.1016/j.matcom.2008.01.040

Estimation of stochastic volatility with LRD

I Casas

Research output: Contribution to journal › Article › peer-review

Abstract

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. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	335-340
Journal	Mathematics and Computers in Simulation
Volume	78
Issue number	2-3
DOIs	https://doi.org/10.1016/j.matcom.2008.01.040
Publication status	Published - 2008

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10.1016/j.matcom.2008.01.040

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title = "Estimation of stochastic volatility with LRD",

abstract = "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. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.",

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AB - 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. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.

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DO - 10.1016/j.matcom.2008.01.040

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VL - 78

SP - 335

EP - 340

JO - Mathematics and Computers in Simulation

JF - Mathematics and Computers in Simulation

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ER -

Estimation of stochastic volatility with LRD

Abstract

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