Are VIX futures prices predictable? An empirical investigation

This paper investigates whether volatility futures prices per se can be forecasted by studying the fast-growing VIX futures market. To this end, alternative model specifications are employed. Point and interval out-of-sample forecasts are constructed and evaluated under various statistical metrics. Next, the economic significance of the forecasts obtained is also assessed by performing trading strategies. Only weak evidence of statistically predictable patterns in the evolution of volatility futures prices is found. No trading strategy yields economically significant profits. Hence, the hypothesis that the VIX volatility futures market is informationally efficient cannot be rejected.     

OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

基于分形理论的原油期货高频交易风险测度研究

为了捕捉原油期货高频波动规律,采用WTI原油期货五分钟数据,基于分形理论分别构建GED分布和Skew-t分布的FIGARCH、FIAPARCH和HYGARCH模型,分析其波动特征并对风险进行测度。结果显示：三种模型均较好地刻画出WTI原油期货波动的长记忆特征;基于Skew-t分布的HYGARCH模型在度量原油期货高频交易风险时尤为精确;多头与空头头寸的VaR呈现非对称性;套期保值者或高频交易者可依据模型预测波动率,防止短期波动率过大导致保证金不足而被强制平仓。高频交易在提高市场流动性和拓宽市场深度方面具有一定的作用,因此,在风险可控的条件下,政府应该鼓励高频交易,促进我国衍生品市场繁荣发展,并增强衍生品市场稳定性和国际竞争力。     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

虱内外燃料油期货市场不完善程度测度和实证检验

论文使用一个包含了价格期望因素以及套利机制不完全性的期货市场不完善程度测度模型，分别对上海和新加坡燃料油期货市场的不完善程度进行了检验。检验结果发现上海燃料油期货市场的不完善程度要比新加坡高很多，可见上海燃料油期货市场有待于进一步完善。最后，论文通过上海与新加坡燃料油期货市场的不同点进行分析，探讨了上海燃料油期货市场不完善程度的可能原因，对实证结果作了进一步解释，并提出了相应的政策建议。     

Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach

In this study, we examine the connection between geopolitical risk (GPR) and stock market volatility in emerging economies. Our motivation for this study is premised on the need to assess both the predictability and the associated economic gains in relation to the subject in order to offer more useful insights to investors and practitioners. To the best of our knowledge, this is the first study that jointly considers these objectives. Consequently, we employ the GARCH-MIDAS framework which accommodates mixed data frequencies thereby circumventing information loss or any associated bias. We find that emerging stock market volatility responds more positively to geopolitical risks although the act-related GPR index offers better out-of-sample forecasts than the threat-related GPR. We also find that accounting for global economic factors in the predictability analysis is crucial for robust outcomes. Finally, we provide some utility gains of including GPR in the predictive model of stock market volatility while also highlighting some useful implications of our findings for investment and policy decisions.     

Transient fads and the crash of ′87

Using a fad model with Markov-switching heteroscedasticity in both the fundamental and fad components (UC-MS model), this paper examines the possibility that the 1987 stock market crash was an example of a short-lived fad. While we usually think of fads as speculative bubbles, what the UC-MS model seems to be picking up is unwarranted pessimism which the market exhibited with the OPEC oil shock and the '87 crash. Furthermore, the conditional variance implied by the UC-MS model captures most of the dynamics in the GARCH specification of stock return volatility. Yet unlike the GARCH measure of volatility, the UC-MS measure of volatility is consistent with volatility reverting to its normal level very quickly after the crash.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Real‐time forecast combinations for the oil price

Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338–351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea‐based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor‐based Nelson–Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures‐based model improves forecast accuracy at longer horizons.     

Do carbon traders behave as a herd?

This paper shows the existence of herding behavior in the European Carbon Futures Market and studies its possible causes and consequences. This market is characterized by leading the carbon price discovery process and by being highly dominated by professional traders. Both features make it an appropriate environment for the existence of herding. A patterns analysis indicates that the herding level increases in speculative periods, on those days on which the price and size clustering effect is stronger, and with the arrival of carbon-related news. Regarding possible market drivers, we find that herding behavior is positively related with the number of trades, the intraday volatility and on days with extreme returns. Our results appear to support the claim that the lower the availability of information, the higher the level of herding. Finally, we show that herding increases market volatility and leads carbon traders to overreact.     

Individual investors surpass their reputation: Trading behaviour on the Polish futures market

This paper examines individual investors’ trading behaviour by testing the presence of Monday and January anomalies on the Polish futures market, where individuals are the predominant trader type. Both anomalies are well established in the literature, and they are at least partially attributed to individual investors’ trading activities. We conduct an intraday analysis of trading volume, open interest, returns, and return volatility on the futures market in Poland and find the contribution of individuals to market anomalies to be grossly overstated. Hence, individual investors’ trading on the Polish futures market surpasses the prediction by the majority of investigations for mature stock markets.     

The influence of ETFs on the price discovery of gold, silver and oil

In this study we examine gold, silver and oil exchange traded funds (ETFs) and their relation to their respective futures instruments and underlying commodities by using intradaily data. We find that the gold, silver and oil ETFs closely track the performance of their underlying assets by using tracking error and pricing deviation metrics. It has been documented in the finance literature that price discovery occurs in the futures market. We test whether in recent times the existence of ETFs has changed the dominating role of the futures market in price discovery. We find that the availability of ETFs has shifted price discovery for gold and silver to the ETF market, while the oil market has price discovery occurring still predominantly in the futures market.     

Derivative trading by utility firms

This paper examines the use of derivatives by a utility company. The hedging problem for utilities is atypical; the goal is not strictly to minimize average costs. Rather, the objectives are to minimize the upside risk associated with extreme bills, volatility of bills, and average expected bills for consumers. We characterize the optimal positions on futures contracts and options on futures that a utility company should assume. The results indicate that the use of derivatives (both futures and options on futures) is an efficient means of optimizing the objective functions without exposing consumers to speculative risk.     

Evidence of information transmission across currency futures markets using frequency domain tests

In this article, I examine the returns and volatility spillovers in the currency futures market incorporating the recently developed frequency domain tests. Such analysis allows differentiating between permanent (long-run) and transitory (short-run) linkages among the currency futures markets by investigating the causality dynamics at low and high frequencies respectively. I detect significant informational linkages between USD, EUR, GBP and JPY futures contracts in the Indian currency futures market. Evidence of innovations from USD futures market to other markets is the most significant for returns spillover and for volatility spillover, EUR is found to be the most significant compared to other currency futures contracts. The results would have implications for the market participants and policymakers.     

Market reactions to trade friction between China and the United States: Evidence from the soybean futures market

In March 2018, the US used an immense trade deficit as an excuse to provoke trade friction with China. This study uses the EGARCH model and event study methods to study the impact of the major risk event of Sino-US trade friction on soybean futures markets in China and the United States. Results indicate that the Sino-US trade friction weakened the return spillover effect between the soybean futures markets in China and the US, and significantly increased market volatilities. As the scale of additional tariffs increased, the volatility of the Chinese soybean futures market declined; however, the volatility of the US soybean futures market did not weaken. In addition, expanding the sources of soybean imports helped ease the impact of tariffs on China’s soybean futures market, while the decline in US soybean exports to China intensified the volatility of the US soybean futures market. In addition, while the release of multiple tariff increases has had a short-term impact on the returns of soybean futures markets, the impact of trade friction has gradually decreased.     

Optimal speculative trade among large traders

A collection of large traders holds heterogeneous prior beliefs regarding market fundamentals. This gives them a motive to engage in speculative trade with respect to market prices. Rather than assuming a particular institution or market for speculative trade, we take a mechanism-design approach by attempting to characterize the mechanism that maximizes the traders’ gains from speculative trade, subject to the incentive constraints that result from the traders’ ability to manipulate market prices. Within a stylized market model, we show that this mechanism affects price volatility without destroying ex-post efficient allocations. We also characterize the implementability of optimal speculative trade when the traders’ prior beliefs are private information. Financial support from the US-Israel Binational Science Foundation, Grant No. 2002298 is gratefully acknowledged.     

The Effect of Market Quality on the Causality between Returns and Volatilities: Evidence from CSI 300 Index Futures

This paper investigates the impact of market quality on volatility asymmetry of CSI 300 index futures by using short- and long-run causality measures proposed by Dufour et al. (2012). We use a high-frequency-based noise variance estimator as the comprehensive proxy for market quality and find that volatility asymmetry is closely related to market quality. Specifically, in the period of poor market quality, the volatility asymmetry will vanish or even be reversed, which is mainly due to the sharp decline of the leverage effects. Moreover, the volatility feedback effect will be enhanced while the leverage effect will be weakened if the noise variance is taken into consideration in the causal analysis. Finally, we use other market quality indices as auxiliary variables in the robustness analysis and get similar results.     

The time varying effect of oil price shocks on euro-area exports

In this paper we provide novel evidence on changes in the relationship between the real price of oil and real exports in the euro area. By combining robust predictions on the sign of the impulse responses obtained from a theoretical model with restrictions on the slope of the oil demand and oil supply curves, we identify oil supply and foreign productivity shocks in a time varying VAR with stochastic volatility. We find that from the 1980s onwards the relationship between oil prices and euro area exports has become less negative conditional on oil supply shortfalls and more positive conditional on foreign productivity shocks. Using the theoretical model we show that our empirical findings can be accounted for by (i) stronger trade relationship between the euro area and emerging economies (ii) a decrease in the share of oil in production and (iii) increased competitive pressures in the product market.     

Cross-region risk spillover between the stock and stock index futures markets under exogenous shocks

By integrating the stock and futures markets of mainland China and Hong Kong into the same financial system, we explore the cross-region risk spillovers between the stock market and stock index futures market under the impact of exogenous events. We find evidence of significant risk spillovers between the two stock markets, and confirm that exogenous shocks, including the adjustments of regulatory policies of mainland China and 2019 Hong Kong Protest, can significantly affect the volatility spillover across assets and markets. Our findings can potentially help regulators and investors understand the cross-region risk conduction and assess portfolio risk after exogenous event.