Cayton, Peter Julian A. and Mapa, Dennis S. (2012): Timevarying conditional Johnson SU density in valueatrisk (VaR) methodology.

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

Original Title:  Timevarying conditional Johnson SU density in valueatrisk (VaR) methodology 
Language:  English 
Keywords:  Time Varying Parameters; GARCH models; Nonnormal distributions; Risk Management 
Subjects:  G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing ; Trading Volume ; Bond Interest Rates C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods G  Financial Economics > G3  Corporate Finance and Governance > G32  Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 
Item ID:  36206 
Depositing User:  Dennis S. Mapa 
Date Deposited:  26 Jan 2012 23:14 
Last Modified:  27 Nov 2016 17:54 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/36206 