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Stochastic Volatility and GARCH: a Comparison Based on UK Stock Data

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  • Chiara Pederzoli

Abstract

This paper compares two types of volatility models for returns, ARCH-type and stochastic volatility (SV) models, both from a theoretical and an empirical point of view. In particular a GARCH(1,1) model, an EGARCH(1,1) model and a log-normal AR(1) stochastic volatility model are considered. The three models are estimated on UK stock data: a series of the British equity index FTSE100 is used to estimate the relevant parameters. Diagnostic tests are implemented to evaluate how well the models fit the data. The models are used to obtain daily volatility forecasts and these volatilities are used to estimate the “VaR” on a simple one-unit position on FTSE100. The VaR accuracy is tested by means of a backtest. While the results do not lead to a straightforward preference between GARCH(1,1) and SV, the EGARCH shows the best performance.

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  • Chiara Pederzoli, 2006. "Stochastic Volatility and GARCH: a Comparison Based on UK Stock Data," The European Journal of Finance, Taylor & Francis Journals, vol. 12(1), pages 41-59.
    • Handle: RePEc:taf:eurjfi:v:12:y:2006:i:1:p:41-59
    DOI: 10.1080/13518470500039121
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    References listed on IDEAS

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    Cited by:

    1. Chuan-Hsiang Han & Wei-Han Liu & Tzu-Ying Chen, 2014. "VaR/CVaR ESTIMATION UNDER STOCHASTIC VOLATILITY MODELS," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-35.
    2. Valeria V. Lakshina, 2014. "The Fluke Of Stochastic Volatility Versus Garch Inevitability : Which Model Creates Better Forecasts?," HSE Working papers WP BRP 37/FE/2014, National Research University Higher School of Economics.
    3. Saman, Corina, 2010. "Macroeconomic Uncertainty and Investment – Empirical Analysis for Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 155-164, July.
    4. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    5. Ngo Thai Hung, 2021. "Volatility Behaviour of the Foreign Exchange Rate and Transmission Among Central and Eastern European Countries: Evidence from the EGARCH Model," Global Business Review, International Management Institute, vol. 22(1), pages 36-56, February.
    6. M. Berument & Yeliz Yalcin & Julide Yildirim, 2011. "The inflation and inflation uncertainty relationship for Turkey: a dynamic framework," Empirical Economics, Springer, vol. 41(2), pages 293-309, October.
    7. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    8. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    9. Rajesh Mohnot, 2011. "Forecasting Forex Volatility In Turbulent Times," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 5(1), pages 27-38.

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