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Modelling and Forecasting the Volatility of Thin Emerging Stock Markets: the Case of Bulgaria

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  • Patev Plamen
  • Kanaryan Nigokhos
  • Lyroudi Katerina

Abstract

Modern Portfolio Theory associates the stock market risk with the volatility of return. Volatility is measured by the variance of the returns' distribution. However, the investment community does not accept this measure, since it weights equally deviations of the average returns, whereas most investors determine the risk on the basis of small or negative returns. In the last few years the measure Value at Risk (VaR) has been established and adopted widely by practitioners.The issue of modelling and forecasting thin emerging stock markets' risk is still open. The subject of this present paper is the risk of the Bulgarian stock market. The aim of this research is to give the investment community a model for assessing and forecasting the Bulgarian stock market risk.The result of this research shows that the SOFIX index has basic characteristics that are observed in most of the emerging stock markets, namely: high risk, significant autocorrelation, non-normality and volatility clustering. Three models have been applied to assess and estimate the Bulgarian stock market risk: RiskMetrics, EWMA with t-distributed innovations and EWMA with GED distributed innovations. The results revealed that the EWMA with t-distributed innovations and the EWMA with GED distributed innovations evaluate the risk of the Bulgarian stock market adequately.

Suggested Citation

  • Patev Plamen & Kanaryan Nigokhos & Lyroudi Katerina, 2009. "Modelling and Forecasting the Volatility of Thin Emerging Stock Markets: the Case of Bulgaria," Comparative Economic Research, Sciendo, vol. 12(4), pages 47-60, January.
    • Handle: RePEc:vrs:coecre:v:12:y:2009:i:4:p:47-60:n:4
    DOI: 10.2478/v10103-009-0021-8
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    Cited by:

    1. Bogdan ZUGRAVU & Dumitru Cristian OANEA & Victoria Gabriela ANGHELACHE, 2013. "Analysis Based on the Risk Metrics Model," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 145-154, May.
    2. Naseem Al Rahahleh & Robert Kao, 2018. "Forecasting Volatility: Evidence from the Saudi Stock Market," JRFM, MDPI, vol. 11(4), pages 1-18, November.
    3. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    4. Dumitru Cristian OANEA & Victoria Gabriela ANGHELACHE & Bogdan ZUGRAVU, 2013. "Econometric Model for Risk Forecasting," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 123-127, May.
    5. Gabriela Anghelache & Dumitru-Cristian Oanea, 2014. "Main Romanian Commercial Banks’ Systemic Risk during Financial Crisis: a CoVar Approach," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 6(2), pages 069-080, December.
    6. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].

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