基于高频数据的波动率预测研究

基于高频数据的金融分析与建模研究目前已成为金融工程研究领域的一大热点。在金融资产价格波动率的刻画上,金融高频波动率有着低频波动率无法比拟的信息优势,能够较为准确地刻画金融市场波动率的相关特征,并对金融市场波动率的变化做出较为精确的预测。本文选择基于高频数据的沪深300指数为样本,通过构建已实现波动率和已实现极差的长记忆性模型去研究高频数据建模预测中的方法,以对比研究的形式分析了已实现波动率和已实现极差在波动率预测中的能力大小,为高频数据波动率预测研究提供了参考和借鉴。     

基于高频数据的波动率预测研究

基于高频数据的金融分析与建模研究目前已成为金融工程研究领域的一大热点.在金融资产价格波动率的刻画上,金融高频波动率有着低频波动率无法比拟的信息优势,能够较为准确地刻画金融市场波动率的相关特征,并对金融市场波动率的变化做出较为精确的预测.本文选择基于高频数据的沪深300指数为样本,通过构建已实现波动率和已实现极差的长记忆性模型去研究高频数据建模预测中的方法,以对比研究的形式分析了已实现波动率和已实现极差在波动率预测中的能力大小,为高频数据波动率预测研究提供了参考和借鉴.     

基于“已实现”波动率的ARFIMA模型预测实证研究

本文采用二次移动平均方法平衡影响"已实现"波动率预测精度的测量误差和市场微观结构误差,利用沪深300指数高频数据实证研究,结果表明"已实现"波动率序列的分布是非正态分布且具有长记忆性,对数"已实现"波动率序列接近于正态分布;最后建立ARFIMA模型,并对波动率进行了预测研究。     

基于Copula的股票市场波动溢出分析

对于动态投资组合与风险管理来说,测定波动溢出效应是非常重要的。已有的研究是建立在不同金融市场之间的波动是线性相关的,而线性相关并不能描述金融市场之间的非线性关系。借用Copula技术来描述股票市场之间的非线性关系、SV模型来刻画股票市场数据的边缘分布,并引入波动变结构论分析判断波动溢出,实证分析验证了方法是可行的。     

带高频信息及交互效应的波动率模型

本文利用股票市场的高频数据波动率预测,采用隔夜波动率和交易时段波动率预测模型,其中,隔夜波动率模型考虑了周末效应对波动率的影响,在交易时段波动率模型中,"已实现波动率"采用基于周平均收益率的函数系数形式,以考察短期收益与高频信息的交互影响,建立了函数系数GARCH模型。基于上证综指的实证分析显示,隔夜波动率存在明显的周末效应,交易时段波动率"杠杆效应"显著,短期收益与高频信息存在显著的非线性交互作用。     

浅谈股指期货市场与股票市场波动率的相关性

随着我国对于股指期货市场管理的放开,期货市场与股票市场波动率关系,对于整体金融市场影响正在加大。所以在金融市场管理研究中,我们必须做好两个市场间波动率相关性研究工作,确保我国整体金融市场发展中风险管理的有序进行。     

政策不确定性与金融市场系统性风险——基于信息溢出的视角

本文从静态和动态两个方面测度了经济政策不确定性与金融市场收益率、波动率间的信息溢出效应,分析了股票市场、外汇市场、债券市场、黄金市场和货币市场五个金融子市场的信息溢出贡献度及其动态特征。研究发现,经济政策不确定性与金融市场间存在显著信息溢出效应,并呈现出时变性和双向性特征。样本期内,存在金融市场收益率对经济政策不确定的正向净溢出,金融市场波动率对经济政策不确定则表现为负向净溢出。从数值上看,政策不确定性与金融市场波动率间信息溢出强于与金融市场收益率间信息溢出。各个金融子市场在方向性溢出效应中贡献率结果显示,不同时期不同金融市场与经济政策不确定性间溢出效应存在差异。     

我国股市“已实现”波动率最优频率选取研究

近年来,随着高频数据的可获取,已实现波动率成为金融研究领域的热点,而抽样频率的选择对准确估计已实现波动率至关重要。最优抽样频率理论上应能较好的平衡测量误差和微观结构误差的存在,本文结合国内外的研究经验,给出了一种适合我国股票市场已实现波动率最优抽样频率的选取方法,并实证得出我国股票市场最优抽样频率为15分钟。     

我国股市“已实现”波动率最优频率选取研究

近年来,随着高频数据的可获取,已实现波动率成为金融研究领域的热点,而抽样频率的选择对准确估计已实现波动率至关重要。最优抽样频率理论上应能较好的平衡测量误差和微观结构误差的存在,本文结合国内外的研究经验,给出了一种适合我国股票市场已实现波动率最优抽样频率的选取方法,并实证得出我国股票市场最优抽样频率为15分钟。     

低波动率形势下的银行业对策

波动率,学术上是指在一个时间结构中,时间与价格的运动规律.目前常用的芝加哥选择权交易所波动率指数（简称VIX）等波动率指数主要用于衡量未来市场波动率水平,是判断金融市场走势的重要标杆.较高的波动率表明市场情绪变化较大,往往意味着一场危机正在形成.因此,波动率指数也被称为“恐慌指数”.较低的波动率则表明市场预期一致,投资者风险偏好提升,加大对高风险、高收益资产的投入.市场对高波动率一般都保持着足够的警惕,但却时常忽视低波动率背后的隐忧.     

Comparative analysis on the effects of the Asian and global financial crises on precious metal markets

The global financial crisis has vigorously struck major financial markets around the world, in particular in the developed economies since they have suffered the most. However, some commodity markets, and in particular the precious metal markets, seem to be unscathed by this financial downturn. This paper investigates therefore the nature of volatility spillovers between precious metal returns over fifteen years (1995-2010 period) with the attention being focused on these markets’ behavior during the Asian and the global financial crises. Daily closing values for precious metals are analyzed. In particular, the variables under study are the US$/Troy ounce for gold, the London Free Market Platinum price in US$/Troy ounce, the London Free Market Palladium price in US$/Troy once, and the Zurich silver price in US$/kg. The main sample is divided into a number of sub periods, prior to, during and after the Asian crisis. The aim of this division is to provide a wide and deep analysis of the behavior of precious metal markets during this financial event and of how these markets have reacted during times of market instability. In addition, this paper also looks at the effects of the global financial crisis from August 2007 to November 2010 using GARCH and EGARCH modeling. The main results show that there is clear evidence of volatility persistence between precious metal returns, a characteristic that is shared with financial market behavior as it has been demonstrated extensively by the existing literature in the area. In terms of volatility spillover effects, the main findings evidence volatility spillovers running in a bidirectional way during the periods; markets are not affected by the crises, with the exception of gold, that tends to generate effects in all other metal markets. However, there is little evidence in the case of the other precious metals generating any kind of influence on the gold market. On the other hand, there is little evidence of spillover effects during the two crisis episodes. Finally, the results from asymmetric spillover effects show that negative news/information have a stronger impact in these markets than positive news, again a characteristic that has been also exhibited by financial markets.     

Volatility transmission between stock and bond markets

A two-factor no-arbitrage model is used to provide a theoretical link between stock and bond market volatility. While this model suggests that short-term interest rate volatility may, at least in part, drive both stock and bond market volatility, the empirical evidence suggests that past bond market volatility affects both markets and feeds back into short-term yield volatility. The empirical modelling goes on to examine the (time-varying) correlation structure between volatility in the stock and bond markets and finds that the sign of this correlation has reversed over the last 20 years. This has important implications far portfolio selection in financial markets.     

The role of jumps and leverage in forecasting volatility in international equity markets

We analyze the importance of jumps and the leverage effect on forecasts of realized volatility in a large cross-section of 18 international equity markets, using daily realized measures data from the Oxford-Man Realized Library, and two widely employed empirical models for realized volatility that allow for jumps and leverage. Our out-of-sample forecast evaluation results show that the separation of realized volatility into a continuous and a discontinuous (jump) component is important for the S&P 500, but of rather limited value for the remaining 17 international equity markets that we analyze. Only for 6 equity markets are significant and sizable forecast improvements realized at the one-step-ahead horizon, which, nevertheless, deteriorate quickly and abruptly as the prediction horizon increases. The inclusion of the leverage effect, on the other hand, has a much larger impact on all 18 international equity markets. Forecast gains are not only highly significant, but also sizeable, with gains remaining significant for forecast horizons of up to one month ahead.     

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.     

Spillover effects of the 2008 global financial crisis on the volatility of the Indian equity markets: Coupling or uncoupling? A study on sector-based data

This paper focuses on the following question: has the global financial stress in the US markets during the subprime crisis induced a persistent volatility of Indian equity stocks? We answer this question using sector-based data and we propose a simple stochastic volatility model augmented with exogenous inputs (financial stress indicators in the US market). We derive analytically the autocorrelation of the squared returns using cross-moments and estimate the impact of several variables such as the CDS spreads, the ABCP spreads, market liquidity, the volatility of the S&P 500 using a Kalman filter approach with the impact captured through Almon polynomials. We find a strong evidence of persistent volatility irrespective of the sector and interpret this finding as the result of two factors: the lower liquidity of the Indian equity markets during the subprime crisis and a wake-up call effect.     

次贷危机期间国际资本市场传染效应研究

本文分析了九个国家(地区)的资本市场指数在次贷危机期间的传染效应。研究发现,危机前,美国对除中国外的7国(地区)在危机发生时冲击力更大,资本市场出现单向传染效应。在金融全球化背景下,由于国际金融危机交叉传染的存在,通过脉冲响应函数的检验揭示了危机传染的动态效应。在危机期内,美国次贷危机对其他国家市场的影响强度迅速增大,持续时间比传统过程相对延长。本文还通过高低波动率机制的比例系数伽玛检测了这些国家(地区)资本市场间的转移传染和纯传染效应。     

The effects of quantitative easing on the integration of UK capital markets

We examine the effects of quantitative easing (QE) on the volatility of and correlation between stocks, short-term bonds and long-term bonds in the UK. Using a multivariate dynamic conditional correlation generalised autoregressive conditional heteroscedasticity model, we find that volatility in each of the markets experiences a significant increase during the financial crisis that is reversed during the first phase of QE. We find limited effects of the specific occurrence or intensity of QE activity on either the volatility or correlations for these asset classes, but some evidence that volatility persistence experienced temporary shifts during the sample period. We find short-term variability in the correlations between the markets during the crisis and QE periods, but cannot reject the hypothesis that correlations were constant throughout the sample period.     

Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework

This paper empirically estimates the spatial correlation relationship of volatility spillovers and its influencing factors across G20 stock market. We apply GARCH-BEKK model to estimate volatility spillover and construct dynamic volatility networks. The connectedness analysis shows that the spatial linkage of volatility spillover is time varying and has obvious multiple superposition phenomena. As somewhat innovation results, we use the factor analysis method to obtain centrality comprehensive indicators that can clearly depict the risk contagion intensity and risk acceptance intensity. In general, the developed markets are more influential than the emerging markets during periods of turbulence, and the emerging markets are more sensitive to volatility shocks than developed markets during any period. Finally, this paper introduces quadratic assignment procedure (QAP) method to identify the major factors that influence the spatial linkage of volatility spillovers. Results show that geography influences the volatility spatial correlation differently across economic cycles, and the centrality structure factors have greater impact on the spatial correlation than the external economic factors. The QAP regression analysis shows that these influencing factors can explain about 50% of the spatial correlation variation of international financial markets' volatility spillovers.     

The impacts of climate policy uncertainty on stock markets: Comparison between China and the US

The interplay between climate policy uncertainty and stock market performance has emerged as a pressing research question in light of the challenges posed by climate change to financial markets. This paper measures China's daily and monthly climate policy uncertainty (CPU) from Jan 2000 to Mar 2022 based on Chinese news data for the first time. Then, the nonlinear and lag impacts of the US CPU and China's CPU on the return, volatility, correlation and tail dependence of China's and US stock markets are investigated and compared by adopting copula function and the distribution lag nonlinear model (DLNM). The data of stock markets includes the Shanghai Composite Index (SSCI) and NASDAQ from Jan 2000 to Mar 2022 from the Choice database, and the Shenzhen Composite Index (SCI) and S&P 500 are used for the robustness test. The empirical results indicate that (1) the growth trend of China’s CPU index is similar to that of the US. However, there are significant differences between the impacts of these two CPUs on stock markets. (2) For China, high CPU decreases current stock market return and increases volatility but decreases it in the future. It could also increase the upper tail dependence between China’s and the US stock markets’ volatilities in current period. (3) For the US, CPU decreases stock market return in the short term but increases it in the long term. High CPU increases volatility in short term, decreases volatility in 5 months and increases it again after 6 months. Both low and high CPU could increase the correlation between China's and US stock markets' volatilities.