Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts

• Autorzy: Ewa Ratuszny
• Języki publikacji: EN

Abstrakty

EN

The aim of the research is to compare VaR methods/models for commodities. For risk measurement Conditional Autoregressive Value at Risk models (CAViaR), implied quantile model and encompassing method are used. The aim is to check whether simultaneous use of information both from historical time series and regarding markets' expectation can improve accuracy of forecasts. For this purpose four methods of combining forecasts are used: a simple average combining, an unrestricted linear combination, a weighted averaged combining and a weighted averaged combining using exponential weighting. In the case of the commodities neither the encompassing method nor the combining forecast method improve VaR forecasts. The method of choosing the most adequate model leads to simple CAViaR-SAV model as the source of most optimal measure of risk forecasts. The Kupiec test, the Christoffersen and the Dynamic Quantile test indicate the model as an adequate to forecast VaR for gold and oil for short positions at the 0.01 and the 0.05 significance level, and for a long position at the 0.05 significance level. (original abstract)

Słowa kluczowe

EN

Commodity price; VaR method; Forecasting

PL

Ceny towarów; Miernik ryzyka (VaR); Prognozowanie

• Czasopismo: Dynamic Econometric Models
• Rocznik: 2015
• Tom: 15
• Strony: 129--156

Twórcy

autor

Ewa Ratuszny

• Warsaw School of Economics, Poland

Bibliografia

• Armendola, A., Storti, G. (2008), A GMM procedure for combining volatility forecasts, Computational Statistics and Data Analysis, 52, 3047-3060.
• Armstrong, J. S. (2001), Principles of Forecasting: A Handbook for Researchers and Practitioners, Dordrecht, Kluwer Academic Publishers, DOI: http://dx.doi.org/10.1007/978-0-306-47630-3.
• Artzner, P., Delbaen, F., Ebe,r J.-M., Heath, D. (1999), Coherent Measures of Risk, Mathematical Finance, 9, 203-228.
• Bates, J. M., Granger, C. W. J. (1969), The combination of forecasts, Operations Research Quarterly, 20, 451-468, DOI: http://dx.doi.org/10.2307/3008764.
• Blair, B. J, Poon, S .H., Taylor, S. J. (2001), Forecasting S&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns, Journal of Econometrics, 105, 5-26, DOI: http://dx.doi.org/10.1016/S0304-4076(01)00068-9.
• Boudoukh, J., Richardson, M., Whitelaw, R. F. (1998), The best of both worlds, Risk, 11, 64-67.
• Bunn, D. W. (1989), Forecasting with more than one model, Journal of Forecasting, 8, 161-166, DOI: http://dx.doi.org/10.1002/for.3980080302.
• Chong, J. (2004), Value at risk from econometric models and implied from currency options, Journal of Forecasting, 23, 603-620, DOI: http://dx.doi.org/10.1002/for.934.
• Chong, Y., Henry, D. F. (1986), Econometric evaluation of linear macro-economic models, Review of Economic Studies, 53, 671-690, DOI: http://dx.doi.org/10.2307/2297611.
• Christoffersen, P. (1998), Evaluating interval forecasts, International Economics Review, 39, 841-862, DOI: http://dx.doi.org/10.2307/2527341.
• Christoffersen, P .F., Mazzotta, S. (2005), The accuracy of density forecasts from foreign exchange options, Journal of Financial Econometrics, 3, 578-605, DOI: http://dx.doi.org/10.1093/jjfinec/nbi021.
• Claessen, H, Mittnik, S. (2002), Forecasting stock market volatility and the informational efficiency of the DAX-index options market, The European Journal of Finance, 8, 302-321, DOI: http://dx.doi.org/10.1080/13518470110074828.
• Clemen, R. T. (1986), Linear constraints and the efficiency of combined forecasts, Journal of Forecasting, 5, 31-38, DOI: http://dx.doi.org/10.1002/for.3980050104.
• Corredor, P., Santamaria, R. (2004), Forecasting volatility in the Spanish option market, Applied Financial Econometrics, 14, 1-11, DOI: http://dx.doi.org/10.1080/0960310042000164176.
• Crane, D. B., Crotty, J. R. (1967), A two-stage forecasting model: Exponential smoothing and multiple regression, Management Science, 13, 501-507, DOI: http://dx.doi.org/10.1016/B978-0-08-019605-3.50019-5.
• Day, T. E., Lewis, C. M. (1992), Stock market volatility and the information content of stock index options, Journal of Econometrics, 52, 267-287, DOI: http://dx.doi.org/10.1016/0304-4076(92)90073-Z.
• Diebold, F. X. (1988), Serial correlation and combination of forecasts, Journal of Business and Economic Statistics, 6, 105-111, DOI: http://dx.doi.org/10.1080/07350015.1988.10509642.
• Diebold, F. X. (1989), Forecast combination and ecompassing: Reconciling two divergent literatures, International Journal of Forecasting, 2, 589-592.
• Doidge, C., Wei, J. Z. (1998), Volatility forecasting and the efficiency of the Toronto 35 index options market, Canadian Journal of Administrative Sciences, 15, 28-38, DOI: http://dx.doi.org/10.1111/j.1936-4490.1998.tb00150.x.
• Doman, M., Doman, R. (2009), Volatility and risk modeling, Oficyna Wolters Kluwer Business, Kraków.
• Donaldson, R. G., Kamstra, M. J. (2005), Volatility forecasts, trading volume, and the ARCH versus option-implied volatility trade-off, The Journal of Financial Research, 28, 519-538, DOI: http://dx.doi.org/10.1111/j.1475-6803.2005.00137.x.
• Capital Requirements Directive IV, CRD IV, Dz. Urz. UE L176, June 27th, 2013.
• Engle, R. F., Manganelli, S. (2004), CAViaR: conditional autoregressive Value at Risk by regression quantiles, Journal of Business and Economic Statistics, 22, 367-381, DOI: http://dx.doi.org/10.1198/073500104000000370.
• Fiszeder, P. (2009), GARCH class models in empirical financial research, Wydawnictwo Naukowe UMK, Toruń.
• Giacomini, R., Komunjer, I. (2005), Evaluation and combination of conditional quantile forecasts, Journal of Business and Economic Statistics, 23, 416-431, DOI: http://dx.doi.org/10.1198/073500105000000018.
• Giot, P. (2005), Implied volatility indexes and daily value at risk models, Journal of Derivatives, 12, 54-64, DOI: http://dx.doi.org/10.3905/jod.2005.517186.
• Giot, P., Laurent, S. (2007), The information content of implied volatility in light of the jump/continuous decomposition of realized volatility, Journal of Futures Markets, 27, 337-359, DOI: http://dx.doi.org/10.1002/fut.20251.
• Grajek, M. (2002), Prognozy łączone (Combining Forecasts), Przegląd Statystyczny, 2, 70-81.
• Granger, C. W. J. (1989), Invited review: Combining forecasts-20 years later, Journal of Forecasting, 8, 167-173.
• Granger, C. W. J., White, H., Kamstra, M. J. (1989), Interval forecasting: An analysis based upon ARCH-quantile estimators, Journal of Econometrics, 40, 87-96, DOI: http://dx.doi.org/10.1016/0304-4076(89)90031-6.
• Greszta, M., Maciejewski, W. (2005), Kombinowanie prognoz gospodarki Polski (Combining Forecasts of the Polish Economy), Gospodarka Narodowa, 5-6, 49-60.
• Iwanicz-Drozdowska, M. (2005), Zarządzanie Finansowe Bankiem (Financial Management of Bank), Warsaw, PWE.
• Jajuga, K., Jajuga, T. (2011), Instrumenty finansowe, aktywa niefinansowe, ryzyko finansowe, inżynieria finansowa (Financial instruments, Non-financial Assets, Financial Risk, Financial Engineering), PWN, Warsaw.
• Jarque, C. M., Bera, A. K. (1987), A Test for Normality of Observations and Regression Residuals, International Statistical Review, 55, 163-172.
• DOI: http://dx.doi.org/10.2307/1403192.Jeon J., Taylor J.W (2013), Using CAViaR Models with Implied Volatility for Value at Risk Estimation, Journal of Forecasting, 32, 62-74, DOI: http://dx.doi.org/10.1002/for.1251.
• Koenker, R., Bassett, G. S. (1978), Regression quantiles, Econometrica, 46, 33-50, DOI: http://dx.doi.org/10.2307/1913643.
• Kupiec, P. (1995), Techniques for Verifying the Accuracy of Risk Measurement Models, Journal of Derivatives, 3, 73-84, DOI: http://dx.doi.org/10.3905/jod.1995.407942.
• Lomnicki, Z. A. (1961), Tests for Departure from Normality in the Case of Linear Stochastic Processes, Metrika, 4, 37-62, DOI: http://dx.doi.org/10.1007/BF02613866.
• Lopez, J. A. (1998), Testing your Risk test, The Financial Survey, 18-20.
• Lopez, J. A. (1999), Methods for Evaluating Value at Risk Estimates, FRBNY Economic Policy Review, 2, 3-15, DOI: http://dx.doi.org/10.2139/ssrn.1029673.
• Mazur, B., Pipień, M. (2012), On the Empirical Importance of Periodicity in the Volatility of Financial Returns - Time Varying GARCH as a Second Order APC(2) Process, Central European Journal of Economic Modelling and Econometrics, 4, 95-116.
• Mittnik, S., Paolella, M. S. (2000), Conditional density and value-at-risk prediction of Asian currency exchange rates, Journal of Forecasting, 19, 313-333, DOI: http://dx.doi.org/10.1002/1099-131X(200007)19:4%3C313::AID-FOR776%3E3.0.CO;2-E.
• Nelson, C. R. (1972), The prediction performance of the F.R.B.-M.I.T.-PENN model of the U.S. economy, American Economic Review, 62, 902-917.
• Nelson, C. R. (1984), A benchmark for the accuracy of econometric forecasts of GNP, Business Economics, 19, 52-58.
• Noh, J., Kim, T. H. (2006), Forecasting volatility of futures market: The S&P 500 and FTSE 100 futures using high frequency returns and implied volatility, Applied Economics, 38, 395-413, DOI: http://dx.doi.org/10.1080/00036840500391229.
• Perignon, C., Smith, D. R. (2010), The Level and Quality of Value-at-Risk Disclosure by Commercial Banks, Journal of Banking and Finance, 34, 362-377, DOI: http://dx.doi.org/10.2139/ssrn.952595.
• Piłatowska, M. (2009), Prognozy kombinowane z wykorzystaniem wag Akaike\'a, w: Ekonomia XXXIX. Dynamiczne Modele Ekonometryczne, Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, Toruń, 51-62.
• Piontek, K. (2000), Modelowanie finansowych szeregów czasowych warunkową wariancją, w: Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 890, 218-226.
• Pipień, M. (2006), Wnioskowanie Bayesowskie w Ekonometrii finansowej, w: Zeszyty Naukowe Akademia Ekonomiczna, 176, Kraków.
• Pong, S., Shackleton, M. B., Taylor, S. J., Xu, X. (2004), Forecasting currency volatility: A comparison of implied volatilities and AR (FI) MA models, Journal of Banking and Finance, 28, 2541-2563, DOI: http://dx.doi.org/10.2139/ssrn.301981.
• Price, K., Storn, R. (1997), Differential Evolution, Dr. Dobb\'s Journal, 18-24, DOI: http://dx.doi.org/10.1007/978-3-642-30504-7_8.
• Rachev, S., Mittnik, S. (2002), Stable Paretian Models in Finance, John Wiley, New York.
• Ratuszny, E. (2013), Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology, Central European Journal of Economic Modelling and Econometrics, 5, 35-63.
• Ratuszny, E. (2015), Influence of robust estimation on Value at Risk. Bounded Innovation Propagation and regression quantiles method, Journal of Management and Financial Sciences, forthcoming.
• Sarma, M., Thomas, S., Shah, A. (2003), Selecting of VaR Models, Journal of Forecasting, 22, 337-358.
• Szakmary, A., Ors, E., Kim, J. K., Davidson III, W. N. (2003), The predictive power of implied volatility: Evidence from 35 futures markets, Journal of Banking and Finance, 27, 2151-2175, DOI: http://dx.doi.org/10.1016/S0378-4266(02)00323-0.
• Taylor, J. W. (2008), Using exponentially weighted quantile regression to estimate value at risk and expected shortfall, Journal of Financial Econometrics, 6, 382-406, DOI: http://dx.doi.org/10.1093/jjfinec/nbn007.
• Taylor, J. (1999), A Quantile Regression Approach to Estimating the Distribution of Multiperiod Returns, Journal of Derivatives, 64-78, DOI: http://dx.doi.org/10.3905/jod.1999.319106.
• Taylor, J. W., Bunn, D. W. (1998), Combining forecast quantiles using quantile regression: Investigating the derived weights, estimator bias and imposing constraints, Journal of Applied Statistics, 25, 193-206, DOI: http://dx.doi.org/10.1080/02664769823188.
• Umantsev, L., Chernozhukov, V. (2001), Conditional Value at risk: Aspects of modeling and estimation, Empirical Economics, 26, 271-292.
• Zarnowitz, V. (1967), An appraisal of short-term economic forecasts, National Bureau of Economic Research, New York.

Identyfikatory

DOI

10.12775/DEM.2015.006