VADELİ PETROL FİYATLARINDA REJİMLE DEĞİŞEN VOLATİLİTE

Öz

Büyük değişimler geçiren vadeli petrol fiyatları, akademisyenler için önemli bir araştırma konusu olmaya devam etmektedir. Politik ve ekonomik nedenlerle 1990’lı yıllarda düşme eğiliminde olan petrol fiyatları, Asya Krizi sonrası 12 ABD dolarına kadar düşmüş, 2002 itibariyle tekrar yükselmiştir. 2008 krizinden sonar tekrar düşme eğilimine giren petrol fiyatları, bir daha 100 ABD doları seviyesine ulaşmamıştır.  Bu çalışma, Petrol vadeli işlem sözleşmelerinin volatilitesini düşük ve yüksek volatilite olarak iki rejimli bir Markov Rejim Değişim GARCH modeli ile açıklamaktadır. yapıda açıklayan çalışmada. Ocak 1990-Ekim 2017 dönemindeki  7077 gözlemlik uzun bir örneklem dönemini ele alan çalışmada, iki farklı volatilite rejimi arasındaki geçiş olasılıkları ve durasyonları açıklanmıştır. Petrol vadeli işlem sözleşmesinin volatilitesi, düşük ve yüksek volatiliteye sahip iki rejim arasında bir markov sürecine bağlı olarak geçiş yapmaktadır. 

Anahtar Kelimeler

• MS GARCH,petrol vadeli işlem sözleşmeleri,volatilite

REGIME RELATED VOLATILITY IN OIL FUTURES PRICES

Öz

Oil futures prices, which have undergone major changes, maintain an important research topic for academics. Oil prices, which tended to decline for political and economic reasons in the 1990s, fell to as low as 12 US dollars after the Asian Crisis, rising again in 2002. The oil prices, which have fallen again after the 2008 crisis, have not reached the level of 100 US dollars again.  This study explains the volatility of petroleum futures contracts as low and high volatility in two regimes by the Markov Regime Switching GARCH model. In the study based on 7077 observations in a long sample period from January 1990 to October 2017, the transition probabilities and durations between two different volatility regimes of oil futures prices are explained. The volatility of the oil futures contract is switching between two regimes with low volatility and high volatility depending on a markov process.

Anahtar Kelimeler

• MS GARCH,oil futures,volatility

Kaynakça

1. Agnolucci, P. (2009). Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models. Energy Economics, 31, 316-321. Anderson, H.M., K. Nam and Vahid, F. (1999). Asymmetric nonlinear smooth transition GARCH models. in P. Rothman (ed.), Nonlinear Time Series Analysis of Economic and Financial Data, Boston: Kluwer, 191–207. Baillie, R.T., Bollerslevi T. and Mikkelsen, H.-O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74, 3–30. Baum, C. C. and Zerilli, P. (2016). Jumps and stochastic volatility in crude oil futures prices using conditional moments of integrated volatility. Energy Economics, 53, 175-181. Bildirici, M., Aykaç Alp, E.i Ersin, Ö. Ö. and Bozoklui Ü. (2010). İktisatta Kullanılan Doğrusal Olmayan Zaman Serisi Yöntemleri. İstanbul, Türkmen Kitabevi. Bodie, Z. and Rosansky V. I. (1980) Risk and Return in Commodity Futures. Financial Analysts Journal, 36(3) 27-39. Cai, J., (1994). A Markov model of switching-regime ARCH. Journal of Business & Economic Statistics, 12, 309–16. Chevallier, J. (2013). Price relationships in crude oil futures: new evidence from CFTC disaggregated data. Environmental Economics and Policy Studies, 15(2), 133–170. Dueker, M. J. (1997) Markov Switching in GARCH Processes and Mean-Reverting Stock Market Volatility. Journal of Business and Economic Studies, 15(1), 26-34. Dusak, K. (1973). Futures Trading and Investor Returns: An Investigation of Commodity Market Risk Premiums. Journal of Political Economy, 81(6), 1387-1406. Engle,R.F., Lilien, D. M. and Robins, R. P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica, 55, 391–407. Fong, W. M. and See, K. H. (2002). A Markov switching model of the conditional volatility of crude oil futures prices. Energy Economics, 24(1):71–95.
2. Fornari, F. and A. Mele (1997). Sign and volatility switching ARCH models: theory and applications to international stock markets. Journal of Applied Econometrics, 12, 49–65. Franses, P. H. and Dijk D. (2000). Nonlinear Time Series Models in Empirical Finance. Cambridge Universtiy Press. Gonz´alez-Rivera, G., (1998). Smooth transition GARCH models. Studies in Nonlinear Dynamics and Econometrics, 3, 61–78 Glosten, L.R., R. Jagannathan and D.E. Runkle, (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779–801 Grauer, F. L. A. (1977). A Test of the Hypothesis that Backwardation is a Function of the Real Social Risk of Commodity Futures Contracts. Stanford University Graduate School of Business Dissertation. Chapter IV. Grauer, F. L. A. and Litzenberger, R. H. (1979). The Pricing of Commodity Futures Contracts, Nominal Bonds and Other Risky Assets under Commodity Price Uncertainity. The Journal of Finance, 34(1), 69-83. Günay, S. (2015). Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns. International Journal of Energy Economics and Policy, 5(4), 979-985. Hagerud, G.E. (1997). A new non-linear GARCH model, PhD thesis, IFE, Stockholm School of Economic Hamilton, J.D. and Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64, 307–3. Kim, C.-J. (1993=. Unobserved-components time series models with Markov-Switching heteroskedasticity: changes in regime and the link between inflation rates and inflation uncertainty. Journal of Business & Economic Statistics, 11, 341–9. Klaassen, F. (1999). Improving GARCH volatility forecasts, Tilburg University, unpublished manuscript Kordnoori, S., Mostafaei, H., and Ostadrahimi, M. (2013). Modelling the fluctuations of Brent oil prices by a probabilistic Markov chain. African Journal of Business Management, 7(17):1648–1654. Krolzig, H- M. (2000). Predicting Markov-Switching Vector Autoregressive Processes. Mimeo, Institute of Economics and Statistics, University of Oxford. Marcucci, J. (2005). Forecasting Stock Market Volatility with Regime-Switching GARCH Models. Studies in Nonlinear Dynamics & Econometricsi, 9(4), Retrieved 23 Nov. 2017, from doi:10.2202/1558-3708.1145. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59(2), 347-370. Nelson, D.B., (1990). Stationarity and persistence in the GARCH(1,1) model. Econometric Theoryi 6, 318–34 Roberti F. Eç (1982), Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1008. Rockwell, C. S. (1967). Normal Backwardation, Forecasting and the Returns to Commodity Futures Traders. Food Research Institute Studies. 7, 107-130. Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28, 467–488. 316–321 Sentana, E., (1995). Quadratic ARCH models. Review of Economic Studies, 639–61. Vo, M. T. (2009). Regime-switching stochastic volatility: Evidence from the crude oil market. Energy Economics, 31(5):779–788. Zakoikan, J. M. (1994). Threshold Heteroscedastic Models. Journal of Economic and Dynamic Control, 18(5), 931-955. Zhang, Y.-J. and Zhang, L. (2015). Interpreting the crude oil price movements: Evidence from the Markov regime switching model. Applied Energy, 143(C):96–109. Zlatcu, I., Kubinschi, M., and Barnea, D. (2015). Fuel Price Volatility and Asymmetric Transmission of Crude Oil Price Changes to Fuel Prices. Theoretical and Applied Economics, 22(4):33–44