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Stochastic Volatility Models For Financial Time Series Analysis

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  • FELICIA RAMONA BIRĂU

    (UNIVERSITY OF CRAIOVA, FACULTY OF ECONOMICS AND BUSINES ADMINISTRATION)

Abstract

This article highlights a comprehensive and approachable perspective to stochastic volatility models for financial time series analysis. Financial time series represent a distinctive category in the economic field, with highly dynamic characteristics, especially in times of financial crisis. Beyond its highly empirical behavior, modeling volatility of financial asset returns aims to improve forecast accuracy. The stochastic volatility models analyzed in this article include the autoregressive conditional heteroscedastic model (ARCH), the generalized autoregressive conditional heteroscedastic (GARCH) model and the exponential generalized autoregressive conditional heteroscedastic (EGARCH) model.

Suggested Citation

  • Felicia Ramona Birău, 2012. "Stochastic Volatility Models For Financial Time Series Analysis," Anale. Seria Stiinte Economice. Timisoara, Faculty of Economics, Tibiscus University in Timisoara, vol. 0, pages 472-475, November.
    • Handle: RePEc:tdt:annals:v:xviii/supplement:y:2012:p:472-475
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    References listed on IDEAS

    as
    1. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091.
    2. Nakajima, Jouchi & Omori, Yasuhiro, 2012. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3690-3704.
    3. Harvey, Andrew C & Shephard, Neil, 1996. "Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 429-434, October.
    4. Ronald J. Mahieu & Peter C. Schotman, 1998. "An empirical application of stochastic volatility models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(4), pages 333-360.
    5. Bollerslev, Tim & Engle, Robert F, 1993. "Common Persistence in Conditional Variances," Econometrica, Econometric Society, vol. 61(1), pages 167-186, January.
    6. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
    7. Strozzi, Fernanda & Zaldı́var, José-Manuel & Zbilut, Joseph P, 2002. "Application of nonlinear time series analysis techniques to high-frequency currency exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 520-538.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    stochastic volatility class of models; high-frequency data; stationary time series; autoregressive conditional heteroscedastic model; financial asset returns;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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