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The new approaches in econometric research of financial markets. Distributed volatility

Author

Listed:
  • V. I. Tinyakova

    (Chair of Information Technologies and Mathematical Methods in Economy, Voronezh State University (Voronezh, Russia))

Abstract

Volatility is one of the most important characteristics of any financial instrument return. The idea which states that all information about financial assets is contained in its price is implemented in current approaches to modeling the volatility of financial assets and it corresponds well with the efficient market hypothesis. Therefore, all volatility models use only the information contained in the price of the asset is being modeled. In this paper we propose an approach that implements the assumption that the volatility of an asset depends on the market volatility. But the relationship is not correlation-regression, though this may exist, but is probabilistic, in the sense that the probability of the high volatility of any asset increases with the volatility of the financial market. To implement this approach, a model which helps to evaluate the distributed volatility is offered. Distributed volatility, however, as VaR, helps to evaluate the positive and negative part of volatility, but unlike VaR, describes volatility dynamics. So it allows forecast calculation of the financial asset volatility, particularly in estimation of the intrinsic value of stock options.

Suggested Citation

  • V. I. Tinyakova, 2012. "The new approaches in econometric research of financial markets. Distributed volatility," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 4(2), pages 247-255, Decembre.
    • Handle: RePEc:rse:wpaper:v:4:y:2012:i:2:p:247-255
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    References listed on IDEAS

    as
    1. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    2. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    3. Satchell, Stephen & Knight, John, 2007. "Forecasting Volatility in the Financial Markets," Elsevier Monographs, Elsevier, edition 3, number 9780750669429.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    volatility; distributed volatility; forecast estimation of volatility; financial market; VaR; Black-Scholes formula; CRR-model; model ARCH;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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