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[CRUDE OIL MARKETS INTEGRATION ON SECOND MOMENT: EVIDENCE FROM VOLATILITY TRANSMISSION AND VOLATILITY IMPULS RESPONSE FUNCTIONS]
[Xiaoye Jin, Cass Business School, 0044 1442 214059, xiaoye.jin.1@city.ac.uk]
Overview
Using daily data from 1 July 2005 to 28 February 2011, the VAR-BEKK and Volatility Impulse Response Function (VIRF) methods are employed to investigate the volatility transmission and analyze the impact of historical events on conditional volatility of crude oil future markets. The empirical findings about the volatility transmission effects among three crude oil markets indicate that the robustness of modelling volatility in one crude oil market could be improved by considering the changes in volatility in another crude oil market and the accuracy of forecasting should be improved by taking into account these transmissions effects. This paper also quantifies the size and persistence of these connections through the VIRF analysis of two historical shocks, namely the Financial Crisis 2007-2010 and the Deepwater Horizon oil spill, which indicates that the Brent crude oil market is the world reference for crude oil pricing and Dubai market has been substituting WTI market as another world benchmark for crude oil pricing.
Methods
The BEKK of Engle and Kroner (1995) is applied to investigate the volatility transmission on the conditional variance for modelling crude oil volatility in the returns of future prices within and across the WTI, Dubai and Brent markets. Furthermore, the volatility impulse response function analysis methodology developed by Hafner and Herwartz (2006) is employed to uncover the impact of historical innovations on conditional volatility following the estimated results from previous multivariate volatility models.
Results
Some off-diagonal coefficients of reported BEKK model are statistically significant, which indicates that volatility spillovers are transmitted through the cross product of innovations and squared innovations as well. This result indicates that volatility spillovers across crude oil markets in the returns of future prices should be considered for crude oil volatility modelling. Moreover, the coefficients for the covariance term are also statistically significant. This finding implies indirect volatility transmission through the covariance term from one crude oil market to another crude oil market among all three benchmark markets. Higher levels of conditional volatility in the past are associated with higher conditional volatility in the current period.
We find that the window horizon of post the bankruptcy of Lehman Brothers can be described as a more volatile period than the window horizon of pre the bankruptcy of Lehman Brothers and the shocks that hit the returns at this window horizon are larger compared to their previous values for the year 2008, even before the bankruptcy of Lehman Brothers. The different trend of 2008 Financial Crisis around the bankruptcy of Lehman Brothers proves that only large shocks compared to the current level of volatility will result in an increase in expected conditional volatilities. Furthermore, another interesting finding is that even if the shocks are absorbed by crude oil markets simultaneously, the dynamics of the impact of shocks are largely specific: Dubai and Brent are more volatile and sensitive than WTI in terms of the volatility impulse response analysis on the information source of Lehman Brothers bankruptcy. It seems that the findings reflect better the global consensus on oil price benchmark of Dubai and Brent, rather than WTI which is more of a domestic US crude (and recently completely out of sync with Brent).
The volatility impulse responses to the shock of Deepwater Horizon oil spill indicate that a positive impact has been exerted into the expected conditional variance. For all three crude oil markets, the impact is instantaneous, although it tends to decline within a relatively short period and tends to gradually disappear within a longer period (five hundred days after initial shock). In this context, Dubai is the crude oil with the most responsive pass-through from the shock to the one-step-ahead expected conditional variance illustrated by 40% increase. For Brent, the expected conditional variance is significantly influenced by the spill shock. The one-step ahead conditional variance is increased by 30%, and the peak response of expected conditional variance occurs about one hundred and fifty days after the shock. For WTI, the expected conditional variance is also significantly influenced by the spill shock. However, the effect tends to decline significantly within a short period (about forty days after initial shock exerted). We also depict the time profile of the impulse response of expected conditional variance for the shocks of the rig catching fire (April 21), the rig sinking (April 22) and the first trading day following the information of oil spill (April 26). Obviously, for all three crude oil markets, the impact is instantaneously recorded. However, two significant differences, in comparison with the previous mentioned response from the oil spill on April 23, have been observed. Firstly, the impacts are negative, which mean that expected conditional variance following the shocks tends to decrease rather than increase as described in previous case. Secondly, the size of the shocks in these cases is relatively smaller than in previous case correspondingly. The comparison indicates that the impact of shocks depend on the current level of volatility and therefore only large shocks compared to the current level of volatility will result in an increase in expected conditional volatilities and relative small shocks seem to decrease expected conditional volatility.
The volatility impulse response distributions for random shocks illustrate that they are all asymmetric and highly positively skewed, and concentrate on the zero following the increase of forecast time horizons. Considering the similarity among the VIRF fitted distribution on these two random dates, it seems that the change in the initial conditional volatility does not have a significant effect on the VRIF distributions.
Conclusions
In this paper we have specified and estimated both the multivariate GARCH model and volatility impulse response functions for three marker crudes to test the hypothesis that crude oil markets have been integrated on second moment (variance). The analysis is performed using daily closing future prices relative to crude oils from three places of production: West Texas intermediate crude oil (WTI), Europe Brent crude oil (Brent) and Dubai Fateh crude oil (Dubai) for the last five years. By adopting a trivariate BEKK representation, we first examine crude oil markets integration on second moment, i.e. linkages through the conditional variances of the series. As a second step, we employ the VIRFs technique developed by Hafner and Herwartz (2006) to estimate the impact of historical shocks on expected conditional variances. This technique enables us to quantify the size and persistence of three historical shocks that have significant impact on crude oil markets.
This paper quantifies the size and persistence of these connections through the VIRFs analysis of two historical shocks, namely the Financial Crisis 2007-2010 and the Deepwater Horizon oil spill, which provides useful insights on understanding the issue of crude oil markets integration. These historical shocks depict an obvious picture with large and positive impact on expected conditional variance. The results indicate that the Brent crude oil market is the world reference for crude oil pricing. This finding also suggests that Dubai market has been substituting WTI market as another world benchmark for crude oil pricing.
Furthermore, we simulate random shocks drawn from the estimated data generating process to fit the VIRF distributions for different forecast horizons. The VIRF distributions estimated from simulated random shocks are asymmetric and highly skewed to the right-hand side. These distributions show that the possibility of observing a large positive impact of a shock is very low while the probability of a relatively smaller positive impact is much higher. In comparison with other two crude oils, the VIRF distribution for crude oil Dubai shows that it has the highest possibility of observing a very large positive impact. However, as the time horizon increases the VIRF become more and more centred around zero, indicating the gradual fade of the impact of the shock.
We also simulate the VIRF for a given possibility of a random shock. The VRIF shows us at least three results. Firstly, only large shock, which is derived from the smaller possibility of occurrence, will result in an increase in expected conditional volatilities. Secondly, the possibility of p=0.3 is the critical transition point for the impact of the shocks since the impact of a given shock will be reversed to be negative following the increase of the possibility of the shock over the critical transition point. Thirdly, the size and the dynamics of the impact of a given shock are largely market specific as the crude oil Dubai is the most sensitive and information-efficient, which is reflected by the relatively large response from the given shock in comparison with other two markets.
A number of shortcomings and research opportunities could be followed to improve this study. Our empirical results may be sensitive to the data frequency. Thus, it would be interesting to consider other data frequencies, for example, high frequency data (tick-by-tick) and weekly data, which will provide an opportunity to examine the robustness of this study to data frequency. This study could be extended into describing the impact of shocks on conditional covariances and then correlations, which will be of practical importance to financial practitioners in making optimal portfolio allocation decisions. Furthermore, the VIRF methodology could be extended into two ways: to incorporate into long-memory and asymmetric effects in conditional volatility, which is not captured by the standard BEKK model, and to analyze the impacts of shocks on third and higher moments of a distribution, which has been pioneered by Jondeau and Rockinger (2006). Both these extensions will be the object of our future work.
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