نوع مقاله : مقاله پژوهشی

نویسندگان

1 محقق پسادکتری، صندوق حمایت از پژوهشگران کشور، تهران

2 دانشیار دانشکده اقتصاد و مدیریت دانشگاه سیستان و بلوچستان

چکیده

به دلیل نقش استراتژیک تلاطم و بی‌ثباتی قیمت‌های نفت خام و تأثیرات آن بر همه کشورهای جهان، روش‌های مختلف مدل‌سازی و پیش‌بینی در این مورد ضروری است. در دو دهه گذشته ادبیات گسترده‌ای در مورد رویکردهای مختلف برای مدل‌سازی تجربی تلاطم در بازار نفت خام پدید آمده است. در این پژوهش، مدل‌سازی تلاطم قیمت‌های نفت خام WTI که در بازار این کالای استراتژیک یکی از مهم‌ترین انواع نفت خام است با شش مدل‌ «تلاطم تصادفی» انعطاف‌پذیر بررسی شده است. سپس عملکرد تجربی این مدل‌‌ها در مقایسه با یکدیگر با استفاده از روش‌های بیزی بررسی شده است. یافته‌های این پژوهش نشان می‌دهد که افزودن پرش در معادله بازده و «اثر اهرمی به مدل تلاطم تصادفی» عملکرد آن را در مقایسه با سایر مدل‌ها بسیار بهبود می‌بخشد. براساس یافته‌های این مدل، پایداری تلاطم در بازار WTI بسیار بالاست و به طور متوسط هر سال یک پرش در این بازار روی می‌دهد. با این حال، این مدل نشان می‌دهد که در سال 2020 دو پرش در بازده WTI در ماه‌های آوریل و مه روی داده است که رویدادی کم‌نظیر است. علاوه بر این همبستگی میان مؤلفه پرش در بازده و پرش در تلاطم (پرش همبسته مرتون) در داده‌های WTI تأیید نمی‌شود. همچنین، به دلیل اثر اهرمی منفی، شوک‌های منفی اثرات تلاطمی قوی‎تری نسبت به شوک‌های مثبت در بازار نفت خام دارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling Crude Oil Price Dynamics: Investigation of Jump and Volatility Using Stochastic Volatility Models (Case study: WTI crude oil prices in 2020 and 2021)

نویسندگان [English]

  • mojtaba rostami 1
  • Mohammad Nabi Shahiki Tash 2

1 Postdoctoral Researcher, Iran National Science Foundation, Tehran, Iran

2 Associate Professor in Economics, Sistan and Baluchestan University, Zahedan, Iran

چکیده [English]

Due to the strategic role of volatility and instability of crude oil prices and their effects on all countries of the world, different methods of modeling and forecasting are necessary. Over the past two decades, an extensive literature has emerged on various approaches to empirically modeling volatility in the crude oil market. In this research, WTI crude oil price volatility modeling, which is one of the most important types of crude oil in the market of this strategic commodity, is examined with six flexible stochastic volatility (SV) models. Then the experimental performance of these models is compared with each other using Bayesian methods. The findings of this study show that adding one jump in efficiency and leverage effect to the stochastic volatility (SVLJ) model greatly improves its performance compared to other models. According to the findings of this model, the stability of volatility in the WTI market is very high and on average one jump occurs in this market every year. However, this model shows that in 2020, two jumps in WTI returns occurred in April and May, which is a unique event. In addition, the correlation between the return jump component and the volatility jump (Merton correlation jump) is not confirmed in the WTI data. Also, due to the negative leverage effect, negative shocks have stronger volatility effects than positive shocks in the crude oil market.

کلیدواژه‌ها [English]

  • Stochastic Volatility
  • Jump
  • Bayesian methods
  • Crude oil
  • Leverage effect
Andersen, T. G. and Davis, R. A. and Kreiss J.P. and. Mikosch T. V. (2009). Handbook of financial time series. Berlin, Heidelberg: Springer, pp. 555-576.
Chan, J. C. and Grant, A. L. (2016). Modeling energy price dynamics: GARCH versus stochastic volatility. Energy Economics, Vol. 54, pp. 182-189.
Charles, A., and Darné, O. (2017). Forecasting crude-oil market volatility: Further evidence with jumps. Energy Economics, Vol. 67, pp.508-519.
Chen, L. and Zerilli, P. and Baum, C. F. (2019). Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications. Energy Economics, Vol. 79, pp. 111-129.
Chou, R.Y. (1988). Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH. Journal of Applied Econometrics, Vol. 3, pp. 279-294.
Crisostomo, R. (2015). An analysis of the Heston stochastic volatility model: Implementation and calibration using MATLAB. arXiv Preprint. https://arxiv.org/abs/1502.02963.
Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingrom inflation. Econometrica, Vol. 50, pp. 391-407.
Eraker, B. and Johannes, M. and Polson, N. (2003). The impact of jumps in volatility and returns. The Journal of Finance, Vol. 58(3), pp. 1269-1300.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian data analysis. CRC press.
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B. (2004). Bayesian data analysis, 2nd edn. London: Chapman and Hall.
Geweke, J. (1989). Bayesian Inference in Econometric Models Using Monte Carlo Integration. Econometrica, Vol. 57, pp. 1317-1339.
Glosten LR. and Jaganathan R. and Runkle DE. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, Vol. 48(5), pp. 1779-1801.
Hoeting, J. A. and Madigan, D. and Raftery, A. E. and Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial.  Statistical Science, Vol. 14 (4), pp. 382-417.
Hull, J. and White, A. (1987). The pricing of options on assets with stochastic volatilities. Journal of Finance, Vol. 42, pp. 281-300.
Jacquier, E. and Polson, N. and Rossi, P. (2004). Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics, Vol. 122, pp. 185-212.
Jeffreys, H. (1939). Theory of Probability. Oxford: Oxford University Press.
Kim, S. and Shephard, N. and Chib, S. (1998). Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models. Review of Economic Studies, Vol. 65, pp. 361-393.
Larsson, K. and Nossman, M. (2011). Jumps and stochastic volatility in oil prices: Time series evidence. Energy Economics, Vol. 33(3), pp. 504-514.
Lee, S. W. and Hansen, B. E. (1994). Asymptotic Theory for the GARCH (1, 1) Quasi-Maximum Likelihood Estimator. Econometric Theory, Vol. 10, pp. 29-52.
Lin, Y. and Xiao, Y. and Li, F. (2020). Forecasting crude oil price volatility via a HM-EGARCH model. Energy Economics, Vol. 87, 104693.
Mandelbrot, B. (1963). The Variation of Certain Speculative Prices, Journal of Business, Vol. 36, pp. 394-419.
Meyer, R. and Yu, J. (2000). BUGS for a Bayesian analysis of stochastic volatility models. Econometrics Journal, Vol. 3, pp. 198-215.
Nakajima, J. (2009). Bayesian analysis of GARCH and stochastic volatility: Modeling leverage, jumps and heavy-tails for financial time series [Technical report Mimeo]. Department of Statistical Science, Duke University.
Nelson, D.B. (1991). Conditional Heteroscedasticity in Asset Returns: A New Approach. Econometrica, Vol. 59, pp. 347-370.
Nelson, D.B. and Foster, D.P. (1994). Asymptotic Filtering Theory for Univariate ARCH Models, Econometrica, Vol. 62, pp. 1-41.
Omori, Y.  and Chib,S. and Shephard,N.  Nakajima,J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics, Vol. 140 (2), pp. 425-449.
Oyuna, D. and Yaobin, L. (2021). Forecasting the Crude Oil Prices Volatility with Stochastic Volatility Models. SAGE Open, Vol. 11(3), 21582440211026269.
Poterba, J. M. and Summers, L. H. (1988). Mean reversion in stock prices: Evidence and implications. Journal of financial economics, Vol. 22(1), pp. 27-59.
Rostami, M., & Makiyan, S. N. (2020). Modeling Stock Return Volatility Using Symmetric and Asymmetric Nonlinear State Space Models: Case of Tehran Stock Market. Journal of Economic Modeling Research, 11(41), pp. 197-229.
Sadorsky, P. (2005). Stochastic volatility forecasting and risk management. Applied Financial Economics, Vol. 15, pp. 121-135.
Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, Vol. 28(4), pp. 467-488.
Schwert, G.W. (1989). Why Does Stock Market Volatility Change Over Time?. Journal of Finance, Vol. 44, pp. 1115-1153.
Shephard, N. (Ed.). (2005). Stochastic volatility: selected readings. Oxford University Press on Demand.
Śmiech, S. and Papież, M. and Rubaszek, M. and Snarska, M. (2021). The role of oil price uncertainty shocks on oil-exporting countries. Energy Economics, Vol. 93, 105028.
Stock, J.H. and Richardson, M.P. (1989). Drawing Inferences from Statistics Based on Multi-Year Asset Returns. Journal of Financial Economics, Vol. 25, pp. 323-348.
Tanner, M. A. and Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American statistical Association, Vol. 82(398), pp. 528-540.
Taylor, S.J (1986). Modelling Financial Time Series. John Wiley, New York.
Withers, S. D. (2002). Quantitative Methods: Bayesian Inference, Bayesian Thinking, Progress in Human Geography, Vol. 26 (4), pp. 553-566.
Yong, L., and Zhang, J. (2014). Bayesian testing for jumps in stochastic volatility models with correlated jumps. Quantitative Finance, Vol. 14(10), pp. 1693-1700.
Yu, J. (2005). On leverage in a stochastic volatility model. Journal of Econometrics, Vol. 127(2), pp. 165-178.‏
Zhong, M. and Darrat, A. F. and Anderson, D. C. (2003). Do US stock prices deviate from their fundamental values? Some new evidence. Journal of banking and finance, Vol. 27(4), pp. 673-697.