中国创业板市场与中小板市场间的波动溢出效应研究——基于方法比较视角

基于创业板市场与中小板市场的特殊关系,本文分别运用单变量条件异方差模型和多元条件异方差模型考察二者之间的信息传导和波动溢出效应,研究结果表明:(1)创业板市场对中小板市场存在波动的集聚性和持久性溢出效应,中小板市场对创业板市场不存在波动的集聚性溢出效应,但存在波动的持久性溢出效应;(2)创业板市场与中小板市场间均值溢出效应不显著,二者互不构成对方的定价中心;(3)单变量GARCH模型下,创业板市场对中小板市场存在单向的波动溢出效应,而多元GARCH模型下,创业板市场与中小板市场间存在双向波动溢出效应,表明多元GARCH模型效果优于单变量GARCH模型;(4)创业板市场与中小板市场间的波动溢出效应程度均不大,但中小板市场对创业板市场的波动溢出效应程度要大于创业板市场对中小板市场的波动溢出效应程度,表明老市场向新兴市场的信息流动量较大.     

基于ARCH类模型的国债市场波动率研究

本文运用自回归条件异方差（ARCH）类模型对我国国债市场（包括银行间国债市场和交易所国债市场）的波动率进行实证分析，结果显示：我国国债市场具有波动率集聚的特征，存在ARCH效应，不存在杠杆效应和高风险、高收益特征，同时国债市场的波动具有很强的持续性。     

我国股市与汇市的波动溢出效应实证研究

本文选取上证综指和人民币对美元名义汇率两个变量,运用GARCH-BEKK模型对我国股市与汇市之间的波动溢出效应进行了实证研究。结果表明,汇改后我国股市与汇市存在双向波动溢出效应,且有非对称性的特点,即汇市对股市的波动溢出效应比股市对汇市的波动溢出效应更强。     

中国股市与汇市波动溢出效应研究

以上证综合指数和人民币兑美元名义汇率为指标,运用多元GARCH模型对中国股票市场和外汇市场之间的波动溢出效应进行实证研究。结果表明：汇率制度改革后,我国股市与汇市存在显著的双向波动溢出效应;汇市对股市表现出较强的波动传导,而股市对汇市的波动传递则相对较弱,存在着波动传导的非对称性。     

交易量适合作为股价波动信息的代理变量吗?——来自中国沪深股市的证据

本文利用中国沪深股市日交易数据,采用多元GARCH模型从信息传递的角度进行实证研究,结果表明:股价对交易量具有显著的波动溢出效应,但交易量对股价的波动溢出效应不明显。这种波动的单向溢出说明在应对信息的冲击上股价比交易量能更快地做出反应,其后才通过波动溢出在交易量上得到反映,股价波动对成交量波动具有先导作用。因此,从波动冲击传导和信息传递的角度看,单纯地将交易量视为股价变动信息的代理变量还缺乏稳健的统计证据。     

沪港通背景下行业间波动溢出效应及形成机理

研究目标：研究资本市场开放是否强化跨境资本市场间的联系及如何促进波动溢出和风险传染等问题。研究方法：运用广义溢出指数法对比分析沪港通开通前后中国内地与中国香港股市行业间波动溢出效应的变化及其形成机理。[HTH]研究发现：沪港通开通前,信息、电信等第三产业的信息溢出水平较高,但沪港通开通后,材料、工业等第二产业的信息溢出能力显著增强;沪港通的实施提高了两市行业间的双向波动溢出程度,且主要增强了上证各行业对恒生行业的波动溢出强度;市盈率效应、规模效应和投资者情绪变化等内地市场的非理性特征也会影响中国内地与中国香港股市间的波动溢出。研究创新：在沪港通政策背景下,从行业层面考察两地股市间的波动溢出效应及形成机理,将风格投资和投资者情绪等非理性行为因素纳入两地资本市场波动溢出的解释框架。研究价值：为两地资本市场的风险传染机制提供更为系统的视角,为检验内地资本市场开放政策提供更为全面的评估,为推行“深港通”等制度提供借鉴。     

国际金融市场波动溢出效应与动态相关性

在DCC GARCH、DCC EGARCH、DCC TGARCH方法下,采用中、美、日、德、英等国家1993年1月至2013年12月的金融数据,实证得出如下结论：样本国市场利率和股指波动率呈现尖峰、肥尾、有偏的特征,更为符合t分布。样本国市场利率波动表现出显著的溢出效应、杠杆效应和联动效应。样本国股指波动率对中国股指波动率的溢出效应趋于增强,特别在美国金融危机后。样本国利率波动对中国股指波动率具有一定的溢出效应和杠杆效应,但影响程度非常低。治理世界性金融风险,各国当局应加强政策协调性,合理进行风险分担。     

基于ARCH类模型的国债市场波动率研究

本文运用自回归条件异方差(ARCH)类模型对我国国债市场(包括银行间国债市场和交易所国债市场)的波动率进行实证分析,结果显示:我国国债市场具有波动率集聚的特征,存在ARCH效应,不存在杠杆效应和高风险、高收益特征,同时国债市场的波动具有很强的持续性。     

国内外期货市场之间波动性溢出效应——基于沪铜和美铜的实证检验

本文用BEKK多元GARCH模型对国内外期货市场之间的波动性特征以及波动溢出效应进行实证检验。结果证明:国内期货市场对国外期货市场存在显著的波动溢出效应,与此同时,国外期货市场也对国内期货市场有着显著的波动溢出效应。     

基于非对称扩散跳跃过程的利率模型研究

在短期利率的扩散跳跃模型基础上,进一步考虑了模型扩散项方差自相关性、非对称性以及跳跃项的均值回复性等设定,以捕捉短期利率的均值回复、波动率集聚、非零偏态和超额峰度以及非连续性等特征。利用上海银行同业拆放市场（SHIBOR）日交易利率数据得出以下结论。首先,SHIBOR利率市场存在均值回复效应,由跳跃设定引起的混合正态分布能捕捉利率增量的尖峰特征。其次,利率增量方差遵循显著的非对称自相关过程,且正的冲击会产生更大的波动性,导致有偏分布。最后,跳跃是利率均值回复速率的重要组成部分,也是利率的水平值动态,尤其是波动性动态的重要来源。     

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

Oil,Gas, or Financial Conditions-Which One Has a Stronger Link with Growth?

Using the models of Diebold-Yilmaz (2012) and Barunik and Krehlik (2018) and monthly U.S. data from January 1992 to May 2019 (329 observations), this study estimates the return and volatility connectedness transmitted from commodity markets (natural gas and crude oil) and the Kansas City financial stress index to macroeconomic indicators (GDP and CPI). As a research target, crude oil has received significant attention. Although natural gas plays an important role in the energy markets as an environment-friendly alternative, it has not been studied extensively. We find the different spread speed of shocks to return and volatility variables through the total spillover index. We focus on both crude oil and natural gas and find that after the bankruptcy of the Lehman Brothers on September 19, 2008, there was a significant jump in the total return spillover from 35.09% to 46.91%, peaking in October 2008. Furthermore, in the frequency domain, we find that the total long-term return spillover index had the highest proportion during the global financial crisis. When the total spillover is concentrated on the high frequencies, it means the system will have an impact mostly in the short term. When it is concentrated on the lower frequencies, it shows that shocks are persistent and works in the long term among the system. It could give some information to the policymakers.     

A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous-time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data. The model setup allows us to directly assess the structural inter-dependencies among the shocks to returns and the two different volatility components. The model estimates suggest that the leverage effect, or asymmetry between returns and volatility, works primarily through the continuous volatility component. The excellent fit of the model makes it an ideal candidate for an easy-to-implement auxiliary model in the context of indirect estimation of empirically more realistic continuous-time jump diffusion and Lévy-driven stochastic volatility models, effectively incorporating the interdaily dependencies inherent in the high-frequency intraday data.     

Volatility forecasting in European government bond markets

In this paper we examine the predictive power of the heterogeneous autoregressive (HAR) model for the return volatility of major European government bond markets. The results from HAR-type volatility forecasting models show that past short- and medium-term volatility are significant predictors of the term structure of the intraday volatility of European bonds with maturities ranging from 1 year up to 30 years. When we decompose bond market volatility into its continuous and discontinuous (jump) component, we find that the jump component is a significant predictor. Moreover, we show that feedback from past short-term volatility to forecasts of future volatility is stronger in the days that precede monetary policy announcements.     

The contagion effect of jump risk across Asian stock markets during the Covid-19 pandemic

This paper tests the market jump contagion hypothesis in the context of the Covid-19 pandemic. We first use a nonparametric approach to identify jumps by decomposing the realized volatility into continuous and jump components, and we use the threshold autoregressive model to describe the jump interdependency structure between different markets. We empirically investigate the contagion effect across several major Asian equity markets (Mainland China, Hong Kong, Japan, South Korea, Singapore, Thailand, and Taiwan) using the 5-minute high frequency data. Some key findings emerge: jump behaviors occur frequently and make an important contribution to the total realized volatility; jump dynamics exhibit significant nonlinearity, asymmetry, and the feature of structural breaks, which can be effectively captured by the threshold autoregressive model; jump contagion effects are obviously detected and this effect varies depending on the regime.     

An application of nonparametric volatility estimators to option pricing

We discuss the impact of volatility estimates from high frequency data on derivative pricing. The principal purpose is to estimate the diffusion coefficient of an Itô process using a nonparametric Nadaraya–Watson kernel approach based on selective estimators of spot volatility proposed in the econometric literature, which are based on high frequency data. The accuracy of different spot volatility estimates is measured in terms of how accurately they can reproduce market option prices. To this aim, we fit a diffusion model to S&P 500 data, and successively, we use the calibrated model to price European call options written on the S&P 500 index. The estimation results are compared to well-known parametric alternatives available in the literature. Empirical results not only show that using intra-day data rather than daily provides better volatility estimates and hence smaller pricing errors, but also highlight that the choice of the spot volatility estimator has effective impact on pricing.     

Pricing Range Accrual Interest Rate Swap employing LIBOR market models with jump risks

This research derives the LIBOR market model with jump risks, assuming that interest rates follow a continuous time path and tend to jump in response to sudden economic shocks. We then use the LIBOR model with jump risk to price a Range Accrual Interest Rate Swap (RAIRS). Given that the multiple jump processes are independent, we employ numerical analysis to further demonstrate the influence of jump size, jump volatility, and jump frequency on the pricing of RAIRS. Our results show a negative relation between jump size, jump frequency, and the swap rate of RAIRS, but a positive relation between jump volatility and the swap rate of RAIRS.     

Econometric analysis of jump-driven stochastic volatility models

This paper introduces and studies the econometric properties of a general new class of models, which I refer to as jump-driven stochastic volatility models, in which the volatility is a moving average of past jumps. I focus attention on two particular semiparametric classes of jump-driven stochastic volatility models. In the first, the price has a continuous component with time-varying volatility and time-homogeneous jumps. The second jump-driven stochastic volatility model analyzed here has only jumps in the price, which have time-varying size. In the empirical application I model the memory of the stochastic variance with a CARMA(2,1) kernel and set the jumps in the variance to be proportional to the squared price jumps. The estimation, which is based on matching moments of certain realized power variation statistics calculated from high-frequency foreign exchange data, shows that the jump-driven stochastic volatility model containing continuous component in the price performs best. It outperforms a standard two-factor affine jump–diffusion model, but also the pure-jump jump-driven stochastic volatility model for the particular jump specification.