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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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 36206.

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Date of creation: Jan 2012
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Handle: RePEc:pra:mprapa:36206

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Keywords: Time Varying Parameters; GARCH models; Nonnormal distributions; Risk Management;

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  1. Jose Oliver Q. Suaiso & Dennis S. Mapa, 2009. "Measuring market risk using extreme value theory," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 46(2), pages 91-121, December.
  2. Hartmut Kliemt & Bernd Lahno & Christopher Lingle & D. Reisman & Robert Bish & Dean Lueck & Donald Boudreaux, 1992. "Reviews," Constitutional Political Economy, Springer, vol. 3(2), pages 267-287, March.
  3. Peter Christoffersen & Denis Pelletier, 2003. "Backtesting Value-at-Risk: A Duration-Based Approach," CIRANO Working Papers 2003s-05, CIRANO.
  4. Peter Julian A. Cayton & Dennis S. Mapa, Ph. D. & Mary Therese A. Lising, 2010. "Estimating Value-At-Risk (Var) Using TIVEX-POT Models," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 0(2), pages 152 - 170, December.
  5. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
  6. ROCKINGER, Michael & JONDEAU, Eric, 2000. "Entropy densities," Les Cahiers de Recherche 709, HEC Paris.
  7. Jondeau, Eric & Rockinger, Michael, 2001. "Gram-Charlier densities," Journal of Economic Dynamics and Control, Elsevier, vol. 25(10), pages 1457-1483, October.
  8. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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