中国股市波动率的年线效应

本文以上证综合指数年线作为标准,把股市划分为牛市和熊市两种状态,并采用ARMA-EGARCH-M模型分别研究了牛市和熊市的股市波动率,称为年线效应。通过实证分析,得到以下主要结论：无论是牛市还是熊市,中国股市的日历效应都不显著;熊市的收益率序列几乎不存在任何的序列相关性;牛市波动率更容易受到坏消息的影响,而在熊市中,好消息的影响较大。如果不对消息内容进行区分,牛市会更容易受消息的冲击,加剧市场波动;而熊市中好消息和坏消息对市场波动的影响并不存在显著的差别。     

我国沪市股票价格波动非对称性分析

在资本市场中,好消息和坏消息对资产价格的冲击往往是不对称的,好消息对资产价格的冲击没有坏消息对资产价格冲击的程度大。本文将通过运用非对称的TARCH及EGARCH模型,对我国沪市股票价格的非对称效应作出实证分析,分析造成这一现象的原因并提出相关政策建议。     

我国沪市股票价格波动非对称性分析

在资本市场中,好消息和坏消息对资产价格的冲击往往是不对称的,好消息对资产价格的冲击没有坏消息对资产价格冲击的程度大.本文将通过运用非对称的TARCH及EGARCH模型,对我国沪市股票价格的非对称效应作出实证分析,分析造成这一现象的原因并提出相关政策建议.     

中国股票市场与商品期货市场传导关系的实证分析——基于风险Granger因果检验的研究

从产业链的角度分析了中国股票市场与商品期货市场间的传导关系,并采用风险Granger因果检验进行实证分析,研究结果发现:铜、铝、锌、原油、白糖价格的上涨(下跌)和上游公司股价的上涨(下跌)存在双向风险溢出关系;铜、铝、锌、原油、白糖价格的下跌和下游公司股价的下跌存在双向风险溢出关系。这说明这些大宗商品价格受下游需求的影响较大,但其价格波动对上游企业的冲击较大。     

全球大宗商品价格联动网络及其对我国物价的溢出影响研究

双循环背景下防范全球大宗商品价格波动所产生的风险传染和价格传导是维护国内物价稳定的重要环节。本文以国内外大宗商品为研究对象,采用基于广义方差分解的动态溢出指数方法来测度全球大宗商品对我国物价水平(CPI、PPI)的波动(收益)溢出效应。研究结果表明:国内外大宗商品之间均存在时变波动(收益)溢出效应,其主要受到外部波动(收益)的驱动;国内外大宗商品在波动溢出和收益溢出层面上对我国物价(CPI、PPI)产生冲击并形成差异化的连通网络。在波动溢出连通网络上,矿产现货会重点对PPI产生较大的价格波动风险溢入效应;在收益溢出连通网络上,金属期货会重点对PPI产生较大的价格传导效应;国际原油期货均处于全球大宗商品连通网络的中心地位,重点对我国CPI产生较大的正向溢出影响。不同时频下国内外大宗商品对我国物价的溢出效应具有趋同性和持久性。周期频率越长,大宗商品对物价的波动(收益)溢出效应越大。在治理通货膨胀方面,要根据我国CPI、PPI在宏观调控中的不同定位,继续实施稳健货币政策,推动供给侧结构性改革,打造强大的国内统一大市场,畅通国内国际双循环发展格局。     

原油价格与经济政策不确定性对大宗商品市场非对称冲击效应研究

利用面板分位回归模型,考量不同市场环境下原油价格与经济政策不确定性对大宗商品市场非对称性冲击效应。结果表明：油价冲击对中国大宗商品收益的影响具有非对称性,正负油价冲击对其均有促进作用,但随着市场环境好转,正油价冲击的作用逐渐增强,负油价冲击则逐渐减弱;政策不确定性对大宗商品收益有促进作用,但在牛市环境下有抑制作用;且危机前后,油价冲击对大宗商品收益的影响存在非对称性效应。     

上海股票市场波动的非对称性和杠杆效应研究

基于人们对股票市场波动"利好"利坏"消息反应程度的不同,本文利用国外证券市场比较成熟的非对称ARCH族理论,对我国上海股票市场建立了ARCH、TARCH、EGARCH模型进行实证检验分析,得出上海股票市场的波动具有非对称性和杠杆效应,坏消息会导致比好消息更大波动性的结论。     

上海金属期货市场的非线性波动特征研究

期货市场是一个典型的非线性动力系统,通过对上海期货交易所(SHFE)的铜、铝期货合约进行非线性波动特征检验,采用基于GED(广义误差分布)的GARCH族模型考察期货收益率的ARCH效应、杠杆效应,并用R/S分析法检验期货收益率和波动率的长期记忆性,得到的实证结果表明:铜、铝期货价格波动有明显的集丛性,铜期货收益率波动没有"杠杆效应",而对铝期货来说,"利好"对条件方差的冲击大于"利空"的冲击.R/S分析结果显示:铜、铝期货收益率均呈现长期记忆性,铜期货有一个约43个日历月的非周期循环,而铝期货并没有明显的非周期循环.更重要的是,实证结果表明期货收益波动率有明显的长期记忆性,因此,在对期货市场波动率建模时应充分考虑这一点.     

上海股票市场波动的非对称性和杠杆效应研究

基于人们对股票市场波动"利好""利坏"消息反应程度的不同,本文利用国外证券市场比较成熟的非对称ARCH族理论,对我国上海股票市场建立了ARCH、TARCH、EGARCH模型进行实证检验分析,得出上海股票市场的波动具有非对称性和杠杆效应,坏消息会导致比好消息更大波动性的结论.     

国际大宗商品价格波动对A股市场的影响几何

2011以来,国际大宗商品价格轮番上演“过山车”行情,而国内众多上市企业受原材料成本上升影响,利润明显下降,因而导致季报收益不及预期,股价下挫.另外受外围国际环境因素影响,截止2012年3月底,A股市场跌声一片,由此来看,国际大宗商品价格的波动对国内A股市场的冲击效应非常显著.目前国内关于这方面的研究课题很多,但大部分是选择个例来研究,而本文采用局部分析与整体分析相结合的方法,最终站在整个大宗商品市场的角度来分析,通过实证计量方法,建立模型来深入研究国际大宗商品价格波动给我国A股市场带来的冲击,并得出结论以及提出相关针对性建议     

Dynamic volatility spillover effects between oil and agricultural products

The strong volatility spillover between crude oil and agricultural commodity markets reduces the diversification benefits and implies costly risk management process faced by portfolio managers and agricultural producers. This paper proposes a comprehensive study of their dynamic implied volatility spillover effects after the global financial crisis 2008–2009, while considering the transition between oil volatility's regimes. By using implied volatility, our analysis emphasizes on the forward-looking information that market traders usually convey in making decisions. We employ the generalized spillover indices within a fractionally integrated VAR model to capture the dynamic patterns of the volatility spillover effects alongside the Markov Switching Autoregressive model to extract the regimes of oil. Our results show new evidence that the net volatility spillover effect from crude oil to all agricultural commodities tends to decrease when crude oil remains in its low volatility regime. Conversely, this effect experiences an increasing trend when crude oil remains in its relatively high volatility regime. A dynamic strategy that combines oil and the most balanced agricultural commodity in terms of volatility transmission with oil (i.e., close-to-zero net volatility spillovers) depending on oil's regimes consistently outperforms the buy and hold strategy in terms of information ratio.     

Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.     

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.     

石油市场与黄金市场收益率波动溢出效应研究

在总结国内外相关研究的基础上,基于2002年12月2日到2010年9月30日的日数据,建立相应的ARCH族模型,并进行Granger因果关系检验,本文对石油市场和黄金市场收益的波动性、波动的非对称性及其波动溢出效应进行实证分析。结果表明:两市均具有显著的方差时变性及新信息对波动冲击的持续性;GARCH(1,1)模型能够很好地消除其ARCH效应;两市均存在明显的非对称性,即石油市场中利空消息引起的波动比同等利好消息引起的波动要大,而黄金市场相反;两市只存在从石油市场到黄金市场的单向波动溢出效应。研究结果对该领域投资者的相关投资及决策人的决策制定具有重要的参考价值。     

Bivariate FIGARCH and fractional cointegration

We consider the modelling of volatility on closely related markets. Univariate fractional volatility (FIGARCH) models are now standard, as are multivariate GARCH models. In this paper, we adopt a combination of the two methodologies. There is as yet little consensus on the methodology for testing for fractional cointegration. The contribution of this paper is to demonstrate the feasibility of estimating and testing cointegrated bivariate FIGARCH models. We apply these methods to volatility on the NYMEX and IPE crude oil markets. We find a common order of fractional integration for the two volatility processes and confirm that they are fractionally cointegrated. An estimated error correction FIGARCH model indicates that the preponderant adjustment is of the IPE towards NYMEX.     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.     

Soft commodities and the global financial crisis: Implications for the economy,resources and institutions

Over the past decade, soft commodities have been subjected to increasing speculative price fluctuations. Following the 2008 financial crisis, most studies have highlighted causal relationships between price volatility, derivative and future markets for underlying financial assets as well as agricultural and mineral commodities. This article investigates the multifaceted effects of unrestrained financialization of the resources and goods markets and its implications for agricultural markets and soft commodities for purposes other than direct human consumption. We place a particular emphasis on the process of commodification of food and non-food crops and their use as green source of liquid fuels (i.e. soy, sugar cane, palm oil, jatropha, and canola). It is argued that speculation in financial markets has led to spillover effects across commodity and resource markets. More importantly, speculation and price volatility in the commodity markets has had a direct bearing on the resource markets and organization and appropriation of common-pool resources. The article sheds further light on the causal relationship between derivative markets, hedging techniques, financial yields and price volatility and spillover effects in the market for food and soft commodities.     

Monetary policy and commodity markets: Unconventional versus conventional impact and the role of economic uncertainty

This study explores the impact of both conventional and unconventional monetary policies in the US and the Euro area on the mean and volatility of certain commodity prices. The analysis considers the prices of eight commodities, i.e. oil, natural gas, gold, silver, aluminium, copper, platinum, and nickel, while the methodology employs the EGARCH-X modelling approach. The empirical findings clearly document that (i) the direction of the impact of both conventional and unconventional monetary policy on commodity returns and commodity volatility is similar and (ii) the impact from unconventional monetary policy on both commodity returns and volatility is relatively more pronounced, while these findings hold valid, irrespective of the geographical region and commodity type. Further investigation of the disparity on the size of the impact through the prism of economic uncertainty reveals that unconventional monetary policy has a stronger effect on economic uncertainty, thereby offering an indirect channel of monetary policy transmission on commodity markets.     

Equilibrium Exhaustible Resource Price Dynamics

We develop equilibrium models of exhaustible resource markets with endogenous extraction choices and prices. Our analysis demonstrates how adjustment costs can generate oil and gas forward price dynamics with two factors, consistent with the behavior these commodities exhibit in the Schwartz and Smith (2000) calibration. Our two‐factor model predicts that stochastic volatility will arise in these markets as a natural consequence of production adjustments, however, and we provide supporting empirical evidence. Differences between endogenous price processes from our general equilibrium model and exogenous processes in earlier papers can generate significant differences in both financial and real option values.