高阶矩波动性建模及应用

为度量高阶矩风险的动态特征、考察时变高阶矩风险对金融投资决策的影响，本文提出了一个新的高阶矩波动模型：NAGARCHSK-M模型。讨论了该模型的包容性，给出了关于高阶矩波动性建模的一整套建模技术，基于正态密度的Gram-Charlier展开给出了模型的参数估计方法。利用该模型对我国股市的高阶矩风险进行了动态描述，并讨论了时变方差风险、时变偏度风险和时变峰度风险对资产收益的影响。     

高阶矩风险与金融投资决策

针对传统投资组合理论没有考虑高阶矩风险这一缺陷,总结近期金融领域中有关偏度和峰度的研究成果,基于"均值-方差"效用函数的Taylor展开,讨论了投资者对高阶矩风险(偏度风险和峰度风险)的偏好特征。     

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。     

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。     

中国股市收益波动特征分析

在以往文献中发现用传统的GARCH模型估计收益率序列通常表现出波动具有长记忆性特征和较高的方差持续性，这些特征可以由方差的结构性变点造成。本文采用Chow检验对上证综指收益率序列进行了方差结构性变点的检测，证实了这些结构性变点与影响中国股市收益结构的国内外重大的经济和政治事件相符合采用GARCH模型分段建模，发现了国内、国际的重大经济和政治事件对股市的影响作用。分段建模很好地刻画了我国股票市场的发展过程，各阶段的GARCH模型表明股票市场波动逐渐减缓，市场逐步成熟。     

中国股市β系数的多尺度特性分析

本文提出一种基于小波方差和小波协方差的β系数估计方法,并通过小波方差和小波协方差的多尺度分解估计出不同尺度上的风险系数,用该方法对中国证券A股市场分行业及投资组合的β系数进行了多尺度估计分析。实证结果表明,我国股市具有复杂的多尺度波动的特征,不同时间尺度上证券市场所表现出的风险不一样,短期投资的风险主要表现在高频波动,投资者应当考虑低尺度下的β系数,而长期投资风险主要表现为低频波动,应当考虑大尺度下的β系数。     

上证综合指数收益率的波动性研究

本文通过用GARCH类模型理论探讨上证综合指数的这种条件异方差特征和聚类现象,进一步分析收益与风险的关系以及波动是否影响股指未来的变化,并且研究上海股市对利好、利空的消息是否存在不对称的反应。     

FIGARCH的协同持续性研究

波动持续性是广泛存在于金融事件序列的一类普遍现象,波动持续性建模是从动态的角度研究风险变化的一种有效方法。由于分形理论能够准确描述经济行为本身的非线性结构特点,将分形方法引入了协同持续研究中,引出了FIGARCH模型,考察了FIGARCH的协同持续性。     

四阶矩国际资产定价模型探讨

金融市场中不仅存在方差风险,而且存在偏度和峰度风险.国际投资面临着诸多的不确定性因素,极值事件的发生会极大的影响国际资产收益.试图推导出四阶矩国际资产定价模型,为国际资产定价研究提出框架.     

中国产业结构分布与经济波动

本文在阿西莫格鲁（Acemoglu）关于产业结构分布与经济波动关系研究的基础上,引入劳动力占比,实证分析了中国产业结构分布与经济波动之间的关系。基于中国投入产出表的数据,设定多种产业规模指标反映中国不同产业的关联度差异,既验证中国产业结构的不对称性,也探讨了产业层面潜在的波动形成源。通过Nadaraya-Watson 非参数估计回归,发现产业规模不对称时,产业部门的波动确实会导致宏观经济波动。由于高阶关联关系的存在,波动会有一定的持续性,对宏观经济的影响较大,产业波动可以解释9％的宏观波动原因。     

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.     

On the predictability of the distribution of excess returns in currency markets

This paper investigates the predictability of foreign exchange (FX) volatility and liquidity risk factors on returns to the carry trade, an investment strategy that borrows in currencies with low interest rates and invests in currencies with high interest rates. Previous studies have suggested that this predictability could have been spuriously accounted for due to the persistence of the predictors. The analysis uses a predictive quantile regression model developed by Lee (2016) that allows for persistent predictors. We find that predictability changes remarkably across the entire distribution of currency excess returns. Predictability weakens substantially in the left tail once persistence is accounted for, implying a moderate negative predictive relation between FX volatility risk and carry trade returns. By contrast, it becomes stronger in the right tail. Furthermore, we provide evidence that FX volatility risk still dominates liquidity risk after controlling for persistence. These findings suggest that the persistence of the predictors needs to be taken into account when one measures predictability in currency markets. Finally, out-of-sample forecast performance is also presented.     

Sources of volatility persistence: A case study of the U.K. pound/U.S. dollar exchange rate returns

This paper utilizes a new approach to examine the inherent nonlinear dynamics of the exchange rate returns volatility. Specifically, we utilize a regime switching threshold (i) generalized autoregressive conditional heteroskedasticity (RS-TGARCH) and (ii) a fractional generalized autoregressive conditional heteroskedasticity (RS-TFIGARCH) model. The RS-TGARCH model is found to be adequate in analyzing the first two moments of the U.K. pound/U.S. dollar monthly exchange rate returns series. The RS-TFIGARCH is found to be adequate for the daily returns series. The volatility persistence and leverage effects associated with exchange rate returns series are jointly tested by means of a Wald Chi-square test.     

Temporal aggregation of volatility models

In this paper, we consider temporal aggregation of volatility models. We introduce semiparametric volatility models, termed square-root stochastic autoregressive volatility (SR-SARV), which are characterized by autoregressive dynamics of the stochastic variance. Our class encompasses the usual GARCH models and various asymmetric GARCH models. Moreover, our stochastic volatility models are characterized by multiperiod conditional moment restrictions in terms of observables. The SR-SARV class is a natural extension of the class of weak GARCH models. This extension has four advantages: (i) we do not assume that fourth moments are finite; (ii) we allow for asymmetries (skewness, leverage effect) that are excluded from weak GARCH models; (iii) we derive conditional moment restrictions and (iv) our framework allows us to study temporal aggregation of IGARCH models.     

A General Approach to Testing Volatility Models in Time Series

Volatility models have been playing important roles in economics and finance. Using a generalized spectral second order derivative approach, we propose a new class of generally applicable omnibus tests for the adequacy of linear and nonlinear volatility models. Our tests have a convenient asymptotic null N(0,1) distribution, and can detect a wide range of misspecifications for volatility dynamics, including both neglected linear and nonlinear volatility dynamics. Distinct from the existing diagnostic tests for volatility models, our tests are robust to time-varying higher order moments of unknown form (e.g., time-varying skewness and kurtosis). They check a large number of lags and are therefore expected to be powerful against neglected volatility dynamics that occurs at higher order lags or display long memory properties. Despite using a large number of lags, our tests do not suffer much from the loss of a large number of degrees of freedom, because our approach naturally discounts higher order lags, which is consistent with the stylized fact that economic or financial markets are affected more by the recent past events than by the remote past events. No specific estimation method is required, and parameter estimation uncertainty has no impact on the convenient limit N(0,1) distribution of the test statistics. Moreover, there is no need to formulate an alternative volatility model, and only estimated standardized residuals are needed to implement our tests. We do not have to calculate tedious and model-specific score functions or derivatives of volatility models with respect to estimated parameters, which are required in some existing popular diagnostic tests for volatility models. We examine the finite sample performance of the proposed tests. It is documented that the new tests are rather powerful in detecting neglected nonlinear volatility dynamics which the existing tests can easily miss. They are useful diagnostic tools for practitioners when modelling volatility dynamics.     

Modeling common volatility characteristics and dynamic risk premia in European equity markets

The paper applies a Factor-GARCH model to evaluate the impact of the market portfolio, as a single common dynamic risk factor, on conditional volatility and risk premia for the returns on size-based equity portfolios of three major European markets; France, Germany and the United Kingdom. The results show that for the size-based portfolios the factor loading for the dynamic market factor is significant and positive but the association between the risk premia and the conditional market volatility is weak. However, the dynamic market factor is shown to explain common characteristics in the conditional variance such as asymmetry and persistence. This finding is consistent across markets and portfolio sizes.     

Volatility persistence,long memory and time-varying unconditional mean: Evidence from 10 equity indices

This paper re-examines evidence of volatility persistence and long memory in the light of potential time-variation in the unconditional mean of the volatility series. Daily equity volatility is generally regarded as exhibiting long memory, however, recent evidence has suggested that long memory may be a spurious finding arising from neglected breaks or time-variation in unconditional variance. The results presented here suggested that long memory is apparent when analysed on the assumption that unconditional variance is constant. However, both breakpoint tests and a moving average application suggest that unconditional variance exhibits substantial, although slow moving, time-variation. The apparent long-memory property largely disappears when this time-variation is taken into account. A modification of the GARCH model to allow for mean variation generates improved volatility forecasting performance, but only over long horizon. At the daily level the assumption of a constant unconditional variance does not seem to affect forecasts.     

Robustness of stable volatility strategies

The paper analyzes the robustness of stable volatility strategies, i.e. strategies in which the portfolio weight of the stock is inversely proportional to its local volatility. These strategies are optimal for a CRRA investor if the stock follows a diffusion process, the expected excess return is proportional to its volatility, and the hedging demand is zero. We assess the performance of stable volatility strategies when these restrictive assumptions do not hold, in particular, when the risk premium is not proportional to volatility and when the stock price is subject to jumps. We find that stable volatility strategies are indeed robust or close to robust under a maxmin decision rule. In addition to our theoretical results, we perform a simulation analysis to evaluate strategies that scale the portfolio weight by the volatility, variance or a constant portfolio weight, and also analyze the strategies using empirical excess returns. Both analyses confirm the robustness of stable volatility strategies.     

Aggregate volatility expectations and threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.