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Markov processes and the distribution of volatility: a comparison of discrete and continuous specifications

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Markov processes and the distribution of volatility: a comparison of discrete and continuous specifications. / Taylor, S J.
In: Philosophical Transactions A: Mathematical, Physical and Engineering Sciences , Vol. 357, No. 1758, 01.08.1999, p. 2059-2070.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Taylor, SJ 1999, 'Markov processes and the distribution of volatility: a comparison of discrete and continuous specifications', Philosophical Transactions A: Mathematical, Physical and Engineering Sciences , vol. 357, no. 1758, pp. 2059-2070. https://doi.org/10.1098/rsta.1999.0417

APA

Taylor, S. J. (1999). Markov processes and the distribution of volatility: a comparison of discrete and continuous specifications. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences , 357(1758), 2059-2070. https://doi.org/10.1098/rsta.1999.0417

Vancouver

Taylor SJ. Markov processes and the distribution of volatility: a comparison of discrete and continuous specifications. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences . 1999 Aug 1;357(1758):2059-2070. doi: 10.1098/rsta.1999.0417

Author

Taylor, S J. / Markov processes and the distribution of volatility : a comparison of discrete and continuous specifications. In: Philosophical Transactions A: Mathematical, Physical and Engineering Sciences . 1999 ; Vol. 357, No. 1758. pp. 2059-2070.

Bibtex

@article{58c08dc115664a32bd9b58c40749bf00,
title = "Markov processes and the distribution of volatility: a comparison of discrete and continuous specifications",
abstract = "Two mixtures of normal distributions, created by persistent changes in volatility, are compared as models for asset returns. A Markov chain with two states for volatility is contrasted with an autoregressive Gaussian process for the logarithm of volatility. The conditional variances of asset returns are shown to have a bimodal distribution for the former process when volatility is persistent that contrasts with a unimodal distribution for the latter process. A test procedure based upon this contrast shows that a log–normal distribution for sterling/dollar volatility is far more credible than only two volatility states.",
keywords = "conditional state probabilities, foreign exchange volatility distributions stochastic volatility, Leptokurtic return distributions , Markov chain , mixture distributions, stochastic volatility",
author = "Taylor, {S J}",
year = "1999",
month = aug,
day = "1",
doi = "10.1098/rsta.1999.0417",
language = "English",
volume = "357",
pages = "2059--2070",
journal = "Philosophical Transactions A: Mathematical, Physical and Engineering Sciences ",
issn = "1364-503X",
publisher = "Royal Society of London",
number = "1758",

}

RIS

TY - JOUR

T1 - Markov processes and the distribution of volatility

T2 - a comparison of discrete and continuous specifications

AU - Taylor, S J

PY - 1999/8/1

Y1 - 1999/8/1

N2 - Two mixtures of normal distributions, created by persistent changes in volatility, are compared as models for asset returns. A Markov chain with two states for volatility is contrasted with an autoregressive Gaussian process for the logarithm of volatility. The conditional variances of asset returns are shown to have a bimodal distribution for the former process when volatility is persistent that contrasts with a unimodal distribution for the latter process. A test procedure based upon this contrast shows that a log–normal distribution for sterling/dollar volatility is far more credible than only two volatility states.

AB - Two mixtures of normal distributions, created by persistent changes in volatility, are compared as models for asset returns. A Markov chain with two states for volatility is contrasted with an autoregressive Gaussian process for the logarithm of volatility. The conditional variances of asset returns are shown to have a bimodal distribution for the former process when volatility is persistent that contrasts with a unimodal distribution for the latter process. A test procedure based upon this contrast shows that a log–normal distribution for sterling/dollar volatility is far more credible than only two volatility states.

KW - conditional state probabilities

KW - foreign exchange volatility distributions stochastic volatility

KW - Leptokurtic return distributions

KW - Markov chain

KW - mixture distributions

KW - stochastic volatility

U2 - 10.1098/rsta.1999.0417

DO - 10.1098/rsta.1999.0417

M3 - Journal article

VL - 357

SP - 2059

EP - 2070

JO - Philosophical Transactions A: Mathematical, Physical and Engineering Sciences

JF - Philosophical Transactions A: Mathematical, Physical and Engineering Sciences

SN - 1364-503X

IS - 1758

ER -