Econometric analysis of realized volatility and its use in estimating stochastic volatility models

Summary. The availability of intraday data on the prices of speculative assets means that we can use quadratic variation-like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realized volatility error—the difference between realized volatility and the discretized integrated volatility (which we call actual volatility). These properties can be used to allow us to estimate the parameters of stochastic volatility models without recourse to the use of simulation-intensive methods.     

Inference Under Heteroskedasticity and Leveraged Data

We evaluate the finite-sample behavior of different heteros-ke-das-ticity-consistent covariance matrix estimators, under both constant and unequal error variances. We consider the estimator proposed by Halbert White (HC0), and also its variants known as HC2, HC3, and HC4; the latter was recently proposed by Cribari-Neto (2004 Cribari-Neto , F. ( 2004 ). Asymptotic inference under heteroskedasticity of unknown form . Computat. Statist. Data Anal. 45 : 215 – 233 .[Crossref], [Web of Science ®] , [Google Scholar]). We propose a new covariance matrix estimator: HC5. It is the first consistent estimator to explicitly take into account the effect that the maximal leverage has on the associated inference. Our numerical results show that quasi-t inference based on HC5 is typically more reliable than inference based on other covariance matrix estimators.     

Assessing Influence on the Liu Estimates in Linear Regression Models

The Liu estimator has been developed as an alternative to the ordinary least squares estimator in the presence of collinearity among the elements of regressors in linear regression models. We present the DFFITS and different versions of the Cook distance analogous to the ones given for the ordinary linear regression models of each individual observation on the Liu estimates. We suggest a version of the Cook distance based on one-step approximation. The mean shift outlier model for the Liu regression has also been investigated. Moreover, using the Sherman-Morrison-Woodbury theorem, we find approximate versions of the DFFITS and the Cook distance. The proposed diagnostics are evaluated on two data sets and yield promising results.     

财政支农杠杆效应的实证研究：1987～2007

 尽管改革以来中国财政支农效率低下已是不争的事实,但目前仍有待揭示其症结以作为改进财政支农工作的切入点。本文依据1987～2007年统计数据,着眼于财政支农杠杆效应视角,对中国财政支农的效率状况展开实证分析,结果发现由于财政与金融彼此割裂式单干支农格局致使财政支农杠杆效应未能有效发挥,财政支农效率十分低下,建议整合财政支农与金融支农,以实现在提升财政支农杠杆效应基础上放大支农资金规模,从而大幅提升资金支农整体能力的目标。     

基于ARCH—M模型上证基金指数收益性与波动性的实证分析

以日收盘价指数的对数收益率为基础,采用ARCH—M类模型（GARCH—M,TGARCH—M和EGARCH—M）对上证基金指数的波动性与收益性进行了实证研究。结果表明基金波动性存在集聚性、波动率与收益率正相关．利空与利好消息对基金波动冲击存在不显著的杠杆效应。     

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.     

Multivariate Stochastic Volatility: A Review

The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also reviewed.     

Multivariate Stochastic Volatility Models with Correlated Errors

We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation matrix to be identically zero. The model is estimated using a Markov chain simulation method that samples from the posterior distribution of the volatilities and parameters. We illustrate the approach using both simulated and real examples. In the real examples, the method is applied to equities at three levels of aggregation: returns for firms within the same industry, returns for different industries, and returns aggregated at the index level. We find pronounced correlation effects only at the highest level of aggregation.     

An in-depth mixed-methods approach to Ryan White HIV/AIDS care program comprehensive needs assessment from the Northeast Georgia Public Health District: The significance of patient privacy,psychological health,and social stigma to care

We apply a social-ecological interpretive framework to understanding relationships among patient privacy, psychological health, social stigma, and continuity in care in the HIV treatment cascade in the rural southeastern US. This research was conducted as part of the 2013 comprehensive needs assessment for the Northeast Georgia Ryan White Consortium using an anthropologically informed mixed-methods design, and a deductive-inductive approach to thematic analysis of qualitative data obtained in interviews and focus groups with service providers and service utilizers. Our comprehensive needs assessment yielded two key components. First, we identified salient phenomena influencing introduction to, retention among, and satisfaction of patients in the Ryan White-coordinated treatment cascade in NE-GA. Second, we formulated actionable recommendations around leverage points identified in the current district-wide system of care. Results highlight spatial, institutional, and interpersonal aspects of the system of care that intersect around issues of patient privacy, psychological health, and social stigma. These intersections constitute pathways by which persons living with HIV are exposed to stigma and other negative social signals regarding their health status without sufficient access to behavioral health services. These negative issues, in turn, can erect significant barriers to long-term continuity in care.     

股权结构、财务杠杆对ST公司投资行为的影响——来自ST上市公司的经验数据

ST公司的投资行为是资本市场所关注的重要问题。基于此,从股东和债权人的视角,分别检验了公司股权结构和财务杠杆对ST公司投资支出的影响。以2005-2012年被ST的A股上市公司为背景,研究发现：股权集中度和长期负债均促进了企业投资,而股权制衡和短期负债抑制了企业投资。此外,规模越大的ST公司投资越少,自由现金流则与投资支出正相关。