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Estimating Value At Risk Var Using Tivex Pot Models

Author

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  • Peter Julian A Cayton
  • Dennis S Mapa
  • Mary Therese A Lising

Abstract

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 modelling since it has consistent green status on the number of exemptions and lower risk capital

Suggested Citation

  • Peter Julian A Cayton & Dennis S Mapa & Mary Therese A Lising, 2010. "Estimating Value At Risk Var Using Tivex Pot Models," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 1(2), pages 152-170.
    • Handle: RePEc:srs:jasf00:v:1:y:2010:i:2:p:152-170
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    References listed on IDEAS

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    1. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
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    3. 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.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. 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.
    6. 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.
    7. 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.
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    Cited by:

    1. Leonard Arvi & Herman Manakyan & Kashi Khazeh, 2023. "Estimated Impact of Covid-19 on Exchange Rate Risk of Multinational Enterprises Operating in Emerging Markets," International Journal of Economics and Financial Issues, Econjournals, vol. 13(4), pages 23-29, July.
    2. 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.
    3. Mudakkar, Syeda Rabab & Uppal, Jamshed Y. & Zaman, Khalid & Naseem, Imran & Shah, Ghias Ud Din, 2013. "Foreign exchange risk in a managed float regime: A case study of Pakistani rupee," Economic Modelling, Elsevier, vol. 35(C), pages 409-417.

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    More about this item

    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
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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