Estimating Value-At-Risk (Var) Using TIVEX-POT Models
AbstractFinancial institutions hold risks in their investments that can potentially affect their ability to serve clients. For banks to weigh their risks, Value-at-Risk (VaR) methodology is used, which involves studying the distribution of losses and formulating a statistic from this distribution. From the myriad of models, this paper proposes a method of formulating VaR using the time-varying parameter through explanatory variables (TiVEx) - peaks over thresholds model (POT). The time varying parameters are linked to linear predictor variables through link functions. To estimate parameters, maximum likelihood estimation is used with the time-varying parameters being replaced from the likelihood function of the generalized Pareto distribution. The test series used for the paper was the Philippine Peso-US Dollar exchange rate from January 2, 1997 to March 13, 2009. Explanatory variables used were GARCH volatilities, quarter dummies, number of holiday-weekends passed, and annual trend. Three selected permutations of TiVEx-POT models by dropping covariates were conducted. Results show that econometric models and static POT models were better-performing in predicting losses from exchange rate risk, but simple TiVEx models have potential as part of VaR modeling since it has consistent green status on the number of exemptions and lower risk capital.
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Bibliographic InfoArticle provided by ASERS Publishing in its journal Journal of Advanced Studies in Finance.
Volume (Year): I (2010)
Issue (Month): 2 (December)
Pages: 152 - 170
Contact details of provider:
Web page: http://www.asers.eu/journals/jasf.html
Value-at-Risk; Extreme Value Theory; Generalized Pareto Distribution; Time-Varying Parameters; Use of Explanatory Variables; GARCH modeling; Peaks-over-Thresholds Model;
Other versions of this item:
- Mapa, Dennis S. & Cayton, Peter Julian & Lising, Mary Therese, 2009. "Estimating Value-at-Risk (VaR) using TiVEx-POT Models," MPRA Paper 25772, University Library of Munich, Germany.
- 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
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
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