Risk assessment of oil price from static and dynamic modelling approaches

The price gap between West Texas Intermediate (WTI) and Brent crude oil markets has been completely changed in the past several years. The price of WTI was always a little larger than that of Brent for a long time. However, the price of WTI has been surpassed by that of Brent since 2011. The new market circumstances and volatility of oil price require a comprehensive re-estimation of risk. Therefore, this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk (VaR) model. The VaR is estimated by the extreme value theory (EVT) and GARCH model on the basis of generalized error distribution (GED). The results show that EVT is a powerful approach to capture the risk in the oil markets. On the contrary, the traditional variance–covariance (VC) and Monte Carlo (MC) approaches tend to overestimate risk when the confidence level is 95%, but underestimate risk at the confidence level of 99%. The VaR of WTI returns is larger than that of Brent returns at identical confidence levels. Moreover, the GED-GARCH model can estimate the downside dynamic VaR accurately for WTI and Brent oil returns.     

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

ABSTRACT

This article identifies the breakdowns in the covariance of three benchmark crude oil futures markets (WTI, Brent and Dubai) and investigates the changes of market connectedness across the breakdown periods. As the crude oil futures are traded in different regions, this article eliminates the non-synchronous trading data by employing the Vector Moving Average structure and the Bayesian data augmentation approach, which keeps the integrity of original data without changing its properties. The results show that there are significant breaks in the covariance structure of crude oil futures markets. The breakdown periods are consistent with the periods when the market volatilities are at high level and the returns are volatile. The changes of market connectedness are independent of the covariance states, which supports the globalization hypothesis for the crude oil market. The results also suggest that there is more information flow out of the WTI than to the WTI during the sample period, particularly during the breakdown periods in 2008–2009.     

Responses of international stock markets to oil price surges: a regime-switching perspective

We propose an enhanced regime-switching model to investigate the relationships between oil price surges and stock market cycles in five oil-dependent countries. Our model accounts for the joint effects of the West Texas Intermediate (WTI) and Brent oil markets and simultaneously captures asymmetry, volatility persistence and regime shifts contained in the underlying financial data. We find that stock market returns strongly exhibit a regime-switching behaviour, but they react differently to the increases in the price of oil. More precisely, the conditional volatility of studied stock markets during the bear market phases is found to be less affected by oil price surges than during the bull market phases. Whether the effects of oil shocks are positive or negative depends greatly on the degree of reliance on imported oil, the share of the cost of oil in the national income and the degree of improvement in energy efficiency of a given country. Finally, the relatively opposite effects of the WTI and Brent oil markets suggest the potential of substitution between them as well as the necessity of a diversification strategy of oil supply sources.     

An investigation into the dynamic relationship between international and China’s crude oil prices

This article studies the dynamic relationship between international (WTI, Brent and Dubai) and domestic (Da Qing) crude oil prices in China using threshold cointegration method. We find evidence of a long-run equilibrium relationship between each pair of international and Da Qing oil prices, favouring the market integration hypothesis. We also estimate asymmetric adjustments under the momentum threshold autoregressive (M-TAR) specification in a TVECM, and the results show that adjustments to eliminate disequilibrium happen faster when oil price spread increases than when it decreases. The long-run and short-run Granger causality tests support the notion that China has influence on the international oil markets. The results imply that China should open up its domestic and imported oil markets, and also establish a well-functioning crude oil futures market, as they are essential for arbitrage and hedging strategies.     

Optimization agricultural production under financial risk of water constraint in the Jordan Valley

Using high-frequency data the co-movements among crude oil prices are analysed in order to address the question of regionalization of the world crude oil market. Time-series econometrics in the form of error-correction modelling is applied for daily crude oil price data covering the time period 1988 to 2004 and in this framework topics like weak and strong exogeneity among three major oil prices – represented by Brent, OPEC and Texas (WTI) – are addressed. The empirical results are that causality is most likely bi-directional among these crude oil prices – and hence rejecting a regionalization hypothesis of the global oil market – and also an influence from the OPEC oil price towards Bent and WTI, which are usually claimed to have a benchmark role.     

Causality patterns for Brent,WTI, and Argus oil prices

Causality patterns are analysed for daily Brent, West Texas Intermediate (WTI), and Argus Sour Crude Index (Argus) oil prices, Argus is the reference price for exports from Saudi Arabia, Kuwait and Iraq. Nonparametric Granger causality testing uncovers bi-directional causal links between Brent and WTI prices at multiple lags. Unidirectional causality from both Brent to Argus and WTI to Argus is also documented. If the current Saudi Arabia attempt to increase market share is successful, variations in Argus prices may start preceding movements in Brent and WTI, also.     

The heterogeneous dependence between global crude oil and Chinese commodity futures markets: evidence from quantile regression

This paper explores the dependence between global crude oil and Chinese commodity futures markets across different quantiles of the return distributions. Based on weekly data from 11 June 2004 to 7 July 2017, we address this issue by applying a quantile regression method. This technique provides a more detailed investigation of the dependence. Moreover, considering the structural breaks caused by market turmoil or financial crises, we divide the full period of every commodity sector market into sub-periods based on these break dates to further explore the dependence changes. The empirical results indicate that the dependence between global crude oil and Chinese commodity futures markets is different across quantiles in different commodity sectors. The dependence is significantly positive, except in markets with high expected returns. Additionally, the effects caused by structural breaks are distinctly heterogeneous across quantiles. The effect of the same break on the degree of dependence exhibits an increasing tendency as the quantile level increases, which suggests that markets with high expected returns are more susceptible to crises. Finally, we apply a prediction analysis to further verify the heterogeneity of the commodity sectors, which may be the cause of the heterogeneous dependence.     

Are securitised real estate markets efficient?: New international evidence based on an improved automatic Portmanteau test

We re-examine the efficiency of real estate markets based on the Escanciano-Lobato (2009) autocorrelation test which we improved by means of wild bootstrapping. Through Monte Carlo simulation, we find that the wild bootstrap-based autocorrelation test has very good performance even in small samples. We apply the improved test to examine the efficiency of 14 international securitized real estate markets—Australia, Canada, France, Germany, Hong Kong, Italy, Japan, Netherlands, Norway, Singapore, Sweden, Switzerland, United Kingdom and the United States. Our results show that only six of these markets—Australia, Hong Kong, Italy, Japan, Sweden and the United States are efficient while the rest are inefficient. We also find that the degree of efficiency or inefficiency of each of these markets varies considerably across time. These findings indicate that real estate markets are relatively less efficient as compared to stock and bond markets in general and may also offer an explanation as to why existing studies on real estate market efficiency have mixed results.     

Multifractal detrended cross-correlations between WTI crude oil price fluctuations and investor fear gauges

This article investigates the cross-correlations between WTI crude oil prices and fear gauges using cross-correlation statistic test and multifractal detrended cross-correlation analysis. The results show that the cross-correlations between crude oil prices and three different kinds of fear gauges are multifractal. By finding the ‘crossover’, we separate the three pairs of time series into the short term and long term, and find that cross-correlations of small fluctuations are persistent in the short and long terms, cross-correlations of large fluctuations are strongly anti-persistent in the short and long terms. The relationship is useful to profit in future markets.     

Realized volatility transmission: The role of jumps and leverage effects

This paper is the first to employ a multivariate extension of the LHAR–CJ model for realized volatility of Corsi and Renó (2012) considering continuous and jump volatility components and leverage effects. The model is applied to financial (S&P 500), commodity (WTI crude oil) and forex (US$/EUR) intraday futures data and allows new insights in the transmission mechanisms among these markets. Besides significant leverage effects, we find that the jump components of all considered assets do not contain incremental information for the one-step ahead realized volatility. The volatility of S&P 500 and US$/EUR exchange rate futures exhibits significant spillovers to the realized volatility of WTI. Moreover, decreasing equity prices appear to increase volatility in other markets, while strengthening of the US$ seems to calm down the crude oil market.     

Crude Oil Futures as an Indicator of Market Changes: A Graphical Analysis

This study investigates the changing relationship between price and volume traded of short- and long-maturity NYMEX light sweet crude oil futures contracts and major changes in the physical crude oil market during the last decade. Monthly series for the #1-month to 84-month out maturity contracts are generated from daily price and volume data for NYMEX West Texas Intermediate (WTI) futures contracts for the period from January 2000 to the middle of 2009. 3-D graphical analysis of the futures prices, contract volumes, maturity dates, and time is used to demonstrate the changing trading volume pattern and evolution of the shape of futures price term structure across various contract maturities in different market regimes. The study observes the impacts of both May 2004, when excess production capacity reached nearly zero, and September 2006, when electronic trading was implemented on the NYMEX WTI futures markets. This analysis will be used to determine if futures contract information can provide an early indication of market regime shifts and improve short-run crude oil spot price forecast models.     

Does speculation Granger cause return in Chinese commodity markets?

We test the Granger causal relations between speculation and returns in Chinese commodity markets using quantile regression method. We find that speculation Granger causes returns in rebar, bean pulp and rapeseed oil markets. At lower quantiles, estimates are negative; but estimates are positive at upper quantiles. This indicates that larger speculation results in larger return volatility.     

Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than vice-versa, and this particularly applies for the U.S. case.

     

Relationship between oil price and exchange rate by FDA and copula

This article investigates the relationship between daily crude oil prices and exchange rates. Functional data analysis is used to show the clustering pattern of exchange rates and oil prices over the time period through high dimensional visualizations. We select exchange rates for important currencies related to crude oil prices by using the objective Bayesian variable selection method. The selected sample data exhibits non-normal distribution with fat tails and skewness. Under the non-normality of the return series, we use copula functions that do not require to assume the bivariate normality to consider marginal distribution. In particular, our study applies the popular and powerful statistical methods such as Gaussian copula partial correlations and Gaussian copula marginal regression. We find evidence of significant dependence for all considered pairs, except for the Mexican peso-Brent. Our empirical results also show that the rise in the West Texas Intermediate (WTI) oil price returns is associated with a depreciation of the US dollar.     

Forecasting realized volatility of crude oil futures with equity market uncertainty

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market.     

An empirical research of crude oil price changes and stock market in China: evidence from the structural breaks and quantile regression

This article investigates the relationship between real crude oil price changes and the Chinese real stock market at the industry level. Our study uses monthly data over the period 1994:03 to 2013:12. Based on input–output (IO) tables, this article will explore more details for the driving factors of sensitivity to oil price changes. We divide these driving factors into cost- and demand-side dependence. Empirical results reveal that sensitivity varies across different industries and periods based on structural breaks and asymmetric effects of oil price changes. Furthermore, some industries seemingly not directly affected by oil are sensitive to the real oil price changes. Finally, using a penalized quantile regression for panel data, we find that these two factors significantly affect lower, but not upper, quantile of sensitivity.     

Quantile spillovers and dependence between Bitcoin,equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.     

基于ARIMA与GARCH模型的国际油价预测比较分析

在分析影响油价波动因素的基础上,利用1986年1月至2010年12月的WTI国际原油价格月度数据,分别建立ARIMA和GARCH模型对油价进行预测。并通过对2011年1月至2012年4月WTI原油价格进行外推预测,检验模型的预测效果。比较分析发现,在短期预测中,ARIMA和GARCH模型对油价的预测均比较准确,但当油价由于受到重大事件的影响而有较大波动时,模型的预测精度下降;在长期预测中,GARCH模型的预测效果优于ARIMA模型;整体来看,GARCH模型预测的精度高于ARIMA模型。因此,在国际油价预测中,用GARCH模型是比较合适的。     

Integrated Conditional Moment testing of quantile regression models

In this paper we propose a consistent test of the linearity of quantile regression models, similar to the Integrated Conditional Moment (ICM) test of Bierens (1982) and Bierens and Ploberger (1997). This test requires re-estimation of the quantile regression model by minimizing the ICM test statistic with respect to the parameters. We apply this ICM test to examine the correctness of the functional form of three median regression wage equations.     

Oligopolistic Pricing of Crude Oil Futures

This paper applies a game-theoretic model of oligopolistic pricing to the crude oil futures contracts traded on the Brent 15-Day market and the London International Petroleum Exchange (IPE). Particular attention is given to the organizational features of the Brent 15-Day market and to the successive changes in the IPE contract