SVJD-LIBOR随机动态模型的市场校准估计与实证模拟

针对跳跃扩散LIBOR市场模型（JD-LIBOR）与随机波动率LIBOR市场模型（SVJD-LMM）各自应用局限,首先将正态跳跃扩散与Heston随机波动率同时引入标准化LIBOR市场模型中,建立一类新型双重驱动非标准化LIBOR市场模型（SVJD-LMM）。其次,运用Cap、Swaption等利率衍生产品市场数据和Black逆推校准方法,对模型的局部波动参数与瞬间相关性参数进行有效市场校准;并运用自适应马尔科夫链蒙特卡罗模拟方法（此后简称A-MCMC）对模型的随机波动率、跳跃扩散等其他主要参数进行有效理论估计与实证模拟。最后,针对六月期美元Libor远期利率实际数据,对上述三类市场模型进行了模拟比较分析。研究结论认为,若在单因子Libor利率市场模型基础上引入跳跃扩散过程,并且联立波动率的随机微分方程,则可极大地提高利率模型的解释力;加入随机波动率和跳跃扩散过程的模拟计算结果与实际利率的误差更小,从而更接近现实情况。     

有限关注与特质波动率之谜：来自行为金融学新证据

本文在我国股票市场检验了有限关注对特质波动率定价的影响。研究发现，特质波动率-收益负相关关系在高关注度股票组合中更显著，而在低关注度组中，特质波动率-收益关系变弱甚至不再显著；Fama和MacBeth（1973）回归结果显示，关注度显著降低了特质波动率的定价作用；另外，通过Hou和Loh（2016）拆分方法发现，有限关注对特质波动率之谜具有最强的解释力度。进一步研究发现，传统的解释不能消除关注度在特质波动率定价中的作用。因而，本文的研究揭示，投资者的有限关注可能是造成一些市场异象的主要原因。     

Vector error correction heterogeneous autoregressive forecast model of realized volatility and implied volatility

A vector error correction model is proposed for forecasting realized volatility which takes advantage of the cointegration relation between realized volatility and implied volatility. The model is constructed by adding a cointegration error term to a vector-and-unit-root version of the heterogeneous autoregressive (HAR) model of Corsi (2009 Corsi, F. 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7 (2):174–96. [Google Scholar]). The proposed model is easier to implement, extend, and interpret than fractional cointegration models. A Monte Carlo simulation and real data analysis reveal advantages of the proposed model over other existing models of Corsi (2009 Corsi, F. 2009. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7 (2):174–96. [Google Scholar]), Busch Christensen and Nielsen (2011 Busch, T., B. J. Christensen, and M. Nielsen. 2011. The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics 160 (1):48–57. [Google Scholar]), Cho and Shin (2016 Cho, S. J. and D. W. Shin. 2016. An integrated heteroscedastic autoregressive model for forecasting long-memory volatilities. Journal of the Korean Statistical Society, 45:371–380. [Google Scholar]), and Bollerslev Patton, and Quaedvlieg (2016 Bollerslev, T., A. J. Patton, and R. Quaedvlieg. 2016. Exploiting the errors:A simple approach for improved volatility forecasting. Journal of Econometrics 192:1-18. [Google Scholar]).     

预期引导、政策冲击与股市波动——基于文本分析法的异质性诊断

防范股票市场异常波动是维护金融稳定和防控金融风险的关键一环。货币政策实践中,预期引导与政策冲击对股市波动的实际影响和传导机制迥然不同。现有文献对两者之于股票市场波动 的异质性影响多有讨论,但分歧明显。基于2005年到2019年中国人民银行各季度《货币政策执行报告》和《货币政策大事记》,本文分别构建表征货币政策预期引导强度和实际操作频度的代理变量,对上述指标之于同期A股市场主要行业指数的波动性影响做了多维诊断和系统梳理。研究发现,第一,预期引导效应和政策冲击效应对于股票市场波动性的影响存在显著异质性特征,预期引导有助于平抑市场波动,而频繁调控则会放大股市波动。第二,预期引导的明确性会制约其对股市波动的平缓作用,货币调控意愿的表达越明确,越有助于平抑股票市场波动;而更坚决的“严厉型”表述比态度相对温和的“温和型”表述能够更显著地平抑股票市场波动。第三,实际操作频度对股市波动的放大作用受制于具体调控方向,宽松型调控的频率上升仅会小幅放大股市波动,而紧缩型货币调控则会大幅抬升股市波动性。由此,从平抑股市异常波动、维持金融稳定的角度出发,强化货币政策的预期引导比相机抉择的频繁调控更为重要;在预期引导过程中,应当增强调控意愿表达的明确性和坚决性,以限制其对金融市场运行带来的扰动。     

Nonparametric Tail Copula Estimation: An Application to Stock and Volatility Index Returns

In this study, we measure asymmetric negative tail dependence and discuss their statistical properties. In a simulation study, we show the reliability of nonparametric estimators of tail copula to measure not only the common positive lower and upper tail dependence, but also the negative “lower–upper” and “upper–lower” tail dependence. The use of this new framework is illustrated in an application to financial data. We detect the existence of asymmetric negative tail dependence between stock and volatility indices. Many common parametric copula models used in finance fail to capture this characteristic.     

Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996

In this article we present a technique for implementing large-scale optimal portfolio selection. We use high-frequency daily data to capture valuable statistical information in asset returns. We describe several statistical issues involved in quantitative approaches to portfolio selection. Our methodology applies to large-scale portfolio-selection problems in which the number of possible holdings is large relative to the estimation period provided by historical data. We illustrate our approach on an equity database that consists of stocks from the Standard and Poor's index, and we compare our portfolios to this benchmark index. Our methodology differs from the usual quadratic programming approach to portfolio selection in three ways: (1) We employ informative priors on the expected returns and variance-covariance matrices, (2) we use daily data for estimation purposes, with upper and lower holding limits for individual securities, and (3) we use a dynamic asset-allocation approach that is based on reestimating and then rebalancing the portfolio weights on a prespecified time window. The key inputs to the optimization process are the predictive distributions of expected returns and the predictive variance-covariance matrix. We describe the statistical issues involved in modeling these inputs for high-dimensional portfolio problems in which our data frequency is daily. In our application, we find that our optimal portfolio outperforms the underlying benchmark.     

A New Approach to Volatility Modeling: The Factorial Hidden Markov Volatility Model

A new process—the factorial hidden Markov volatility (FHMV) model—is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a product of three components: a Markov chain controlling volatility persistence, an independent discrete process capable of generating jumps in the volatility, and a predictable (data-driven) process capturing the leverage effect. An economic interpretation is attached to each one of these components. Moreover, the Markov chain and jump components allow volatility to switch abruptly between thousands of states, and the transition matrix of the model is structured to generate a high degree of volatility persistence. An empirical study on six financial time series shows that the FHMV process compares favorably to state-of-the-art volatility models in terms of in-sample fit and out-of-sample forecasting performance over time horizons ranging from 1 to 100 days. Supplementary materials for this article are available online.     

A Comparison of the Runs Test for Volatility Forecastability and the LM Test for GARCH Using Aggregated Returns

Christoffersen and Diebold (2000 Christoffersen , P. F. , Diebold , F. X. ( 2000 ). How relevant is volatility forecasting for financial risk management? Review of Economics and Statistics 82 : 12 – 22 .[Crossref] , [Google Scholar]) have introduced a runs test for forecastable volatility in aggregated returns. In this note, we compare the size and power of their runs test and the more conventional LM test for GARCH by Monte Carlo simulation. When the true daily process is GARCH, EGARCH, or stochastic volatility, the LM test has better power than the runs test for the moderate-horizon returns considered by Christoffersen and Diebold. For long-horizon returns, however, the tests have very similar power. We also consider a qualitative threshold GARCH model. For this process, we find that the runs test has greater power than the LM test. Theresults support the use of the runs test with aggregated returns.     

Estimating the Stock/Portfolio Volatility and the Volatility of Volatility: A New Simple Method

We devise a convenient way to estimate stochastic volatility and its volatility. Our method is applicable to both cross-sectional and time series data, and both high-frequency and low-frequency data. Moreover, this method, when applied to cross-sectional data (a collection of risky assets, portfolio), provides a great simplification in the sense that estimating the volatility of the portfolio does not require an estimation of a volatility matrix (the volatilities of the individual assets in the portfolio and their correlations). Furthermore, there is no need to generate volatility data.     

Semiparametric Estimation of Risk–Return Relationships

This article proposes semiparametric generalized least-squares estimation of parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of parametric factors. A distinctive feature of our estimator is that it does not require a fully parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is nonstandard due to the presence of estimated factors. We provide sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the finite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using nonparametric estimates, we find a positive and significant price of risk in our semiparametric setting.