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Time-varying conditional Johnson SU density in value-at-risk (VaR) methodology

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  • Cayton, Peter Julian A.
  • Mapa, Dennis S.

Abstract

Stylized facts on financial time series data are the volatility of returns that follow non-normal conditions such as leverage effects and heavier tails leading returns to have heavier magnitudes of extreme losses. Value-at-risk is a standard method of forecasting possible future losses in investments. A procedure of estimating value-at-risk using time-varying conditional Johnson SU¬ distribution is introduced and assessed with econometric models. The Johnson distribution offers the ability to model higher parameters with time-varying structure using maximum likelihood estimation techniques. Two procedures of modeling with the Johnson distribution are introduced: joint estimation of the volatility and two-step procedure where estimation of the volatility is separate from the estimation of higher parameters. The procedures were demonstrated on Philippine-foreign exchange rates and the Philippine stock exchange index. They were assessed with forecast evaluation measures with comparison to different value-at-risk methodologies. The research opens up modeling procedures where manipulation of higher parameters can be integrated in the value-at-risk methodology.

Suggested Citation

  • Cayton, Peter Julian A. & Mapa, Dennis S., 2012. "Time-varying conditional Johnson SU density in value-at-risk (VaR) methodology," MPRA Paper 36206, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:36206
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    References listed on IDEAS

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    Cited by:

    1. Domino, Krzysztof & Błachowicz, Tomasz, 2014. "The use of copula functions for modeling the risk of investment in shares traded on the Warsaw Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 77-85.
    2. Sree Vinutha Venkataraman & S. V. D. Nageswara Rao, 2016. "Estimation of dynamic VaR using JSU and PIV distributions," Risk Management, Palgrave Macmillan, vol. 18(2), pages 111-134, August.
    3. Domino, Krzysztof & Błachowicz, Tomasz & Ciupak, Maurycy, 2014. "The use of copula functions for predictive analysis of correlations between extreme storm tides," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 489-497.
    4. Domino, Krzysztof & Błachowicz, Tomasz, 2015. "The use of copula functions for modeling the risk of investment in shares traded on world stock exchanges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 142-151.

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    More about this item

    Keywords

    Time Varying Parameters; GARCH models; Nonnormal distributions; Risk Management;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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