Estimating Value-At-Risk (Var) Using TIVEX-POT Models
Financial 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.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Peter Christoffersen & Denis Pelletier, 2003.
"Backtesting Value-at-Risk: A Duration-Based Approach,"
CIRANO Working Papers
- Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 84-108.
- 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.
- McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
- Byström, Hans, 2001.
"Extreme Value Theory and Extremely Large Electricity Price Changes,"
2001:19, Lund University, Department of Economics.
- Bystrom, Hans N. E., 2005. "Extreme value theory and extremely large electricity price changes," International Review of Economics & Finance, Elsevier, vol. 14(1), pages 41-55.
- Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
- Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
When requesting a correction, please mention this item's handle: RePEc:srs:jasf12:6:v:1:y:2010:i:2:p:152-170. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Laura Stefanescu)
If references are entirely missing, you can add them using this form.