MODELLING ENERGY MARKET VOLATILITY USING GARCH MODELS AND ESTIMATING VALUE-AT-RISK
Keywords:
Back testing, extreme value theory (EVT), Peak-over-threshold (POT), GARCH-EVT model, Value-at-Risk (VaR).Abstract
Purpose: The study focused on modelling the volatility of energy markets spot prices using GARCH models and estimating Value-at-Risk.
Methodology: The conditional heteroscedasticity models are used to model the volatility of gasoline and crude oil energy commodities. In estimating Value at Risk; GARCH-EVT model is utilized in comparison with other conventional approaches. The accuracy of the VaR forecasts is assessed by using standard statistical back testing procedures.
Results: The empirical results suggests that the gasoline and crude oil prices exhibit highly stylized features such as extreme price spikes, price dependency between markets, correlation asymmetry and non-linear dependency. We also conclude that the EGARCH-EVT model is more robust, provides the best t and outperforms the other conventional models in terms of forecasting accuracy and VaR prediction. Generally, the GARCH-EVT model can be used to plays an integral role as a risk management tool in the energy industry.
Unique contribution to theory, practice and policy: In light of the research findings, the study recommends that organizations should leverage modern technology as a basis of realizing efficiency, effectiveness, and sustainability of projects. The study likewise recommends that organizations should build capacities to enhance labour productivity. In addition, the study recommends that organizations should adopt transformational leadership approaches as a basis of enhancing performance. The study recommends the need to revise the legal framework with a view to ensure that it reflects the changing needs of the project requirements
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References
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3):307-327.
Bouseba, F. Z. and Zeghdoudi, H. (2015). Use of the garch models to energy markets: Oil price volatility. Global Journal of Pure and Applied, pages 4385-4394.
Brandt, M. W. and Kang, Q. (2004). On the relationship between the conditional mean and volatility of stock returns: A latent var approach. Journal of Financial Economics, 72(2):217- 257.
Christoffersen, P. F. (1998). Evaluating interval forecasts. International economic review, pages 841-862.
Coles, S., Bawa, J., Trenner, L., & Dorazio, P. (2001). An introduction to statistical modeling of extreme values (Vol. 208). London: Springer.
Fasanya, I. O. and Adekoya, O. B. (2017). Modelling inflation rate volatility in Nigeria with structural breaks. CBN Journal of Applied Statistics, 8(1):175-193.
Fisher, R. A. and Tippett, L. H. C. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. In Mathematical Proceedings of the Cambridge Philosophical Society, volume 24, pages 180-190. Cambridge University Press.
Gnedenko, B. (1943). Sur la distribution limite du terme maximum d'une serie aleatoire. Annals of mathematics, 423-453.
Halilbegovic, S. and Vehabovic, M. (2016). Backtesting value at risk forecast: the case of kupiec pof-test. European Journal of Economic Studies, (3):393-404.
Hasan, M. Z., Akhter, S., and Rabbi, F. (2013). Asymmetry and persistence of energy price volatility. International Journal of Finance and Accounting, 2(7).
Jenkinson, A. F. (1955). The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quarterly Journal of the Royal Meteorological Society, 81(348):158-171.
Kang, S. H., & Yoon, S. M. (2013). Modeling and forecasting the volatility of petroleum futures prices. Energy Economics, 36, 354-362.
Koutmos, G. (1998). Asymmetries in the conditional mean and the conditional variance: Evidence from nine stock markets. Journal of Economics and Business, 50(3):277-290.
Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models.
Li, Y.-X., Lian, J.-G., and Hang, H. (2016). Forecast and backtesting of var models in crude oil market. Research and Reviews: Journal of Statistics and Mathematical Sciences, 2:131-140. 27
Liu, C. and Maheu, J. M. (2007). Are there structural breaks in realized volatility? Journal of Financial Econometrics, 6(3):326-360.
McNeil, A. J., Frey, R., and Embrechts, P. (2015). Quantitative risk management: Concepts, techniques and tools. Princeton University Press.
Musaddiq, T. (2012). Modeling and forecasting the volatility of oil futures using the arch family models.
Omari, C. O. (2017). A comparative performance of conventional methods for estimating market risk using value at risk. International Journal of Econometrics and Financial Management, 5:22-32.
Saltik, O., Degirmen, S., and Ural, M. (2016). Volatility modelling in crude oil and natural gas prices. Procedia economics and finance, 38:476-491.
Springer. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, pages 987- 1007.
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