APPROACHES TO PRICE FORMATION IN FINANCIALIZED COMMODITY MARKETS

A recent debate about the financialization of commodity markets has stimulated the development of new approaches to price formation which incorporate index traders as a new trader category. I survey these new approaches by retracing their emergence to traditional price formation models and show that they arise from a synthesis between commodity arbitrage pricing and behavioural pricing theories in the tradition of Keynesian inspired hedging pressure models. Based on these insights, I derive testable hypotheses and provide guidance for a growing literature that seeks to empirically evaluate the effects of index traders on price discovery in commodity futures markets.     

Open interest,volume, and volatility: evidence from Taiwan futures markets

This paper examines the relationships amongst volatility, total trading volume (TVOL) and total open interest (TOI) for three Taiwan stock index futures markets as well as the role of the latter two variables in the dynamics of GARCH modeling and forecasting. From both ex-post and ex-ante perspectives, we study this issue by using the VAR model and augmented GARCH-type models, respectively. For the GARCH-type models, we employ both symmetric and asymmetric models augmented with lagged logs in TOI and/or TVOL. We find that whether addition of these two variables helps the basic GARCH models predict future volatility depends upon the sample period examined for all three sets of futures. Nonetheless, the best three models for out-of-sample volatility forecasting in the MSE sense are generally the augmented models for all sub-intervals and all three futures contracts.     

Empirical evidence of conditional asset pricing in the Indian stock market

Studies of various alternative empirical asset pricing models have mostly concentrated on developed markets. However, despite the importance of this issue, surprisingly little is known about how different asset pricing models behave in emerging capital markets. The purpose of this paper is to determine the suitability of conditional compared to unconditional versions namely, the capital asset pricing model and the Fama-French three-factor model for the Indian stock market. The key distinction between the present empirical tests and previous tests is the application of the Kalman filter method for dynamic beta estimation in the Indian market. The findings indicate that the cross-sectional variation in expected returns is driven by mainly two firm characteristics size and book-to-market ratio.Unlike the unconditional model, the market beta is able to capture the variation of expected return in conditional model. The results imply that information has a role and investors use the prior belief and macroeconomic variables as predictive variables to determine the cost of capital. These results are supported by some recent findings that Fama-French three-factor model is the only multifactor model that consistently sources three different types of risk included in the list of anomalies.     

Forecast on silver futures linked with structural breaks and day-of-the-week effect

Silver future is crucial to global financial markets. However, the existing literature rarely considers the impacts of structural breaks and day-of-the-week effect simultaneously on the volatility of silver future price. Based on heterogeneous autoregressive (HAR) theory, we establish six new type heterogeneous autoregressive (HAR) models by incorporating structural breaks and day-of-the-week effect to forecast the volatility. The empirical results indicate that new models’ accuracy is better than the original HAR model. We find that structural breaks and the day-of-the-week effect contain much forecasting information on silver forecasting. In addition, structural breaks have a positive effect on the silver futures’ volatility. Day-of-the-week effect has a significantly negative influence on silver futures’ price volatility, especially in the mid-term and the long-term. Our works is the first to combine the structural breaks and day-of-the-week effect to identify more market information. This paper provides a better forecasting method to predict silver future volatility.     

The cross section of Chinese commodity futures return

This paper investigates the cross-section of expected commodity futures returns in China using a large panel of 13 individual factors. We find that 6 out of 13 individual factors produce positive and significant returns. To aggregate the information among these factors, we apply not only the traditional Fama-MacBeth regression (FM), but also a set of alternative methods, including the forecast combination method (FC), principal component analysis (PCA), principle component regression (PCR) and partial least squares (PLS). It turns out that PLS outperform other methods in forecasting the cross-section of Chinese expected futures returns. The equally weighted combination of 5 methods produces an even higher annualized return and lower standard deviation compared to each single method. The investigation of factor importance reveals that the skewness (SKEW) factor is more important than other factors in predicting expected futures returns in Chinese markets.     

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.     

Forecasting stock prices with commodity prices: New evidence from Feasible Quasi Generalized Least Squares (FQGLS) with non-linearities

The complexities in modern stock markets make it imperative to unravel the possible predictors of their future values. This paper thus provides insights into the predictability of stock prices of the BRICS countries with large dependence on commodities either for foreign exchange earnings or industrial while accounting for the role of asymmetries. Essentially, empirical evidence abound for the high volatility in world commodity markets, thus making us to determine if positive and negative changes in commodity prices predict stock prices differently. In addition, unlike the traditional forecast models, our choice of forecast models additionally addresses certain statistical features, including conditional heteroskedasticity, serial dependence, persistence and endogeneity, inherent in the predictors, which have the potential of causing estimation bias. In all, we find evidence in favour of the ability of commodity prices to predict stock prices of Brazil, Russia and South Africa. Also, both the in-sample and out-of-sample forecast performances of the predicted models support asymmetries in a number of commodity prices in each of these three countries. Our results are robust to different data samples and forecast horizons.     

Dynamic volatility connectedness between industrial metal markets

Combined with the spillover framework of Diebold and Yilmaz (2009, 2012, 2014) and the TVP-VAR-SV model of Primiceri (2005), this paper studies the dynamic volatility connectedness between six major industrial metal (i.e., aluminum, copper, lead, nickel, tin and zinc) spot and futures markets. The results show that: (1) The total volatility connectedness between industrial metal spot or futures markets has three obvious cyclical change periods with a higher connectedness level; (2) The net connectedness of zinc and copper with other metals has been at a high positive level for a long time, which indicates the two metal markets dominate the industrial metal market; (3) Zinc exhibits the strongest volatility spillovers, while tin exhibits the weakest volatility spillovers, no matter in spot markets or futures markets; (4) The connectedness of realized skewness and kurtosis have similarity with volatility connectedness but the spillover effects of skewness and kurtosis are not as obvious as the volatility spillover effects.     

基于高频数据的沪深300指数期货价格发现能力研究

从股指期货和现货对新信息的反应速度、新信息融入比率两个角度,研究了沪深300股指期货的价格发现能力。研究采用了沪深300指数期货和现货的1分钟高频数据进行实证分析,使用向量误差修正模型和脉冲响应函数分析的结果表明,股指期货市场对新信息的反映速度快于现货市场。使用I-S模型和P-T模型实证分析的结果表明,新信息主要通过沪深300指数期货市场进行反映。从新信息反映速度和融入比率两方面来看,沪深300指数期货市场的价格发现能力都要强于指数现货市场。     

基于长记忆的中国期货市场实证研究

通过某期货公司研发部编制的期货指数,基于长记忆研究方法,运用经典的R/S分析、修正的R/S分析,分别建立了研究长记忆的ARFIMA模型、FIGARCH模型和ARFLMA-FIGARCH模型。并运用这些模型对我国上海期货交易所的铜、铝、大连期货交易所的大豆、玉米、豆粕以及郑州期货交易所的小麦的收益率序列进行相关研究和分析,得出它们的收益率序列以及收益率波动序列均存在长记忆性,且ARFLMA(0,d1,0)-FIGARCH(1,d2,0)模型的预测效果较好。     

Testing the stabilization hypothesis in the UK short-term interest rates: Evidence from a GARCH-X model

The objective of this paper is to examine the validity of one of the recurring arguments made against futures markets that they give rise to price instability. The paper concentrates on the impact of futures trading on the spot market volatility of short-term interest rates. The analytical framework employed is based on a new statistical approach aiming to reconcile the traditional models of short-term interest rates and the conditional volatility processes. More specifically, this class of models aims to capture the dynamics of short-term interest rate volatility by allowing volatility to depend on both scale effects and information shocks. Using a GARCH-X and asymmetric GARCH-X model four main conclusions emerge from the present study. First, the empirical results suggest that there is an indisputable change in the nature of volatility with evidence of mean reversion after the onset of futures trading. Second, the information flow into the market has improved as a result of futures trading. Third, a stabilization effect has been detected running from the futures market to the cash market by lowering volatility levels and decreasing the risk in the spot market. Finally, trying to capture the leverage effect the findings suggest that positive shocks have a greater impact on volatility than negative shocks.     

The asymmetric effect of crude oil prices on stock prices in major international financial markets

The aim of this paper is to explore the potential asymmetric impacts of positive and negative shocks in crude oil prices on stock prices in six major international financial markets which include China, Hong Kong, America, Japan, Britain, and Germany. We test for these asymmetric effects on 8 major international financial markets indices over the 2007M01–2020M03 periods. Our independent measures include the prices of Brent crude oil futures and West Texas Intermediate (WTI) futures. We use the nonlinear ARDL (NARDL) model proposed by Shin et al. (2014), which can capture both short- and long-run nonlinearities through positive and negative partial sum decompositions of the explanatory variables. This research finds that positive and negative fluctuations of oil price have asymmetric effects on stock price index in four financial markets, but the performance of the asymmetry is different. Specifically, the impacts of volatility in oil prices on two indices of Chinese stock prices are different, and the asymmetric effects of oil price volatility on stock price indices in China and other financial markets are significantly different.     

Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators

Previous work has highlighted the difficulty of obtaining accurate and economically significant predictions of VIX futures prices. We show that both low prediction errors and a significant amount of profitability can be obtained by using a neural network model to predict VIX futures returns. In particular, we focus on open-to-close returns (OTCRs) and consider intraday trading strategies, taking into account non-lagged exogenous variables that closely reflect the information possessed by traders at the time when they decide to invest. The neural network model with only the most recent exogenous variables (namely, the return on the Indian BSESN index) is superior to an unconstrained specification with ten lagged and coincident regressors, which is actually a form of weak efficiency involving markets of different countries. Moreover, the neural network turns out to be more profitable than either a logistic specification or heterogeneous autoregressive models.     

钢材期货价格发现功能研究——基于印度市场信息共享模型的分析

本文借助Johansen协整检验、Granger因果检验、信息共享模型、方差分解等方法进行多层次实证研究,定量地刻画出期货市场在价格发现中作用的大小。研究结果显示：印度板材期货价格和现货价格之间存在长期均衡关系,在价格引导上仅存在现货对期货价格的单向引导关系,期货对现货没有引导关系;通过方差分解发现,现货市场在价格发现功能中处于主导地位,说明印度钢材期货市场效率有待提高,板材期货没有实现其应有的价格发现功能。     

成交量、持仓量对我国大豆类期货价差波动影响的实证分析

采用GARCH(1,1)模型就成交量、持仓量对大豆类期货价差波动率的影响进行实证分析,结果显示：当期成交量、持仓量对大豆期货价差波动的整体影响是显著的;滞后成交量、持仓量对大豆期货价差波动的整体影响也是显著的;当成交量、持仓量同时进入条件方差方程时,它们对大豆类期货价差波动的影响整体上也是显著的。这一结论揭示了我国大豆期货市场信息传递过程,验证了我国大豆期货市场的信息非有效性,对期货市场投资者以及期货市场监管者具有一定的借鉴意义。     

基于SSCP理论的青海省有色金属产业发展研究

有色金属产业是重要的基础原材料产业,产品种类多、应用领域广、产业关联度高。青海省有色金属资源丰富,有色金属产业是青海省经济发展最为重要的支柱产业之一。SSCP框架认为青海省有色金属产业结构层面,市场集中度越大,产业绩效越好;产业产权结构层面,非国有企业产出越少,产业绩效越好。故青海应支持大型国有有色金属企业兼并重组,组建二到三家国有控股大型集团;进而构建产学研结合新模式,持续增强产业发展内生动力;同时支持非国有企业发挥优势,加快发展生产性服务业,为有色金属产业持续发展做好配套。     

Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China

We develop a skewness-dependent multivariate conditional autoregressive value at risk model (SDMV-CAViaR) to detect the extreme risk transmission channels between the Chinese stock index futures and spot markets. The proposed SDMV-CAViaR model improves the forecast performance of extreme risk by introducing the high-frequency realized skewness. Specifically, the realized skewness has a significant impact on the spillovers, but the realized volatility and realized kurtosis do not, which implies that the jump component plays an important role in extreme risk spillovers. The empirical results indicate there are bidirectional extreme risk spillovers between the stock index futures and spot markets, the decline of one market has direct and indirect channels to exacerbate the extreme risk of the other market. Firstly, the market decline will directly increase the extreme risk of related markets by decreasing market returns. Besides, the decline will indirectly increase the extreme risk by increasing the negative realized skewness and extreme risk spillovers.     

The impact of funding liquidity on market quality

This study analyzes market quality during the 2007–2008 credit crunch, by examining the impact of funding liquidity on market liquidity and price discovery of S&P 500 exchange-traded funds (i.e., S&P 500 depositary receipts [SPYs]) and index futures (E-minis). The empirical results show that funding liquidity affects market liquidity, and that the impact of illiquidity contagion between SPYs and E-minis was significant during the subprime mortgage crisis. In particular, the contagion effects between the two markets mediate the impact of funding illiquidity on market liquidity during the credit crunch. Considering the influences of other market factors on price discovery, we suggest that E-mini index futures made less contributions to price discovery during the credit crunch compared to normal periods. The empirical finding emphasizes the importance of the contagion effect between ETF and E-mini futures markets, when they suffer from external shocks.     

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.     

期货市场操纵的认定：美国经验及其启示

基于美国期货市场的实践和学术界的相关研究,提出了期货市场操纵的定义,归纳了期货市场操纵的类型和手法。通过几个典型判例分析了美国在对期货市场操纵进行认定上的经验,并介绍了国外学术研究在市场操纵的判断依据和方法上的进展。在此基础上,得出了美国经验与国外学术研究对中国期货市场规范发展的启示。