Measuring Nonlinear Granger Causality in Mean

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than 6 months. We also find that there is a high degree of predictability at the 1-month horizon, that can be attributed to a nonlinear causal effect. Supplementary materials for this article are available online.     

The Changing Transmission of Uncertainty Shocks in the U.S.

This article investigates if the impact of uncertainty shocks on the U.S. economy has changed over time. To this end, we develop an extended factor augmented vector autoregression (VAR) model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on a range of variables. We find that the impact of uncertainty shocks on real activity and financial variables has declined systematically over time. In contrast, the response of inflation and the short-term interest rate to this shock has remained fairly stable. Simulations from a nonlinear dynamic stochastic general equilibrium (DSGE) model suggest that these empirical results are consistent with an increase in the monetary authorities’ antiinflation stance and a “flattening” of the Phillips curve. Supplementary materials for this article are available online.     

Modeling and forecasting realized covariance matrices with accounting for leverage

The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modifications.     

中国股市大幅波动期间股指期货对指数现货波动的影响分析

针对股指期货做空机制与做多机制对指数现货的影响有无异同的问题,将2014年7月至2015年8月间中国股市的大幅波动分为上涨期和下跌期,通过沪深300股指期货以及沪深300指数现货每5分钟的数据,利用Garman & Klass波动率度量公式、双变量GARCH模型和EGARCH模型来分析波动中股指期货对指数现货波动的影响。研究认为,在市场处于大幅波动期,股指期货和指数现货两个市场之间存在双向价格引导关系,其中指数现货市场处于主导地位,两个市场之间风险是相互传递的,其中指数现货市场的风险传递更加显著;在中国股市波动的利多阶段,股指期货的负收益会促进指数现货的波动,在利空阶段,股指期货的负收益影响大于正收益影响,股指期货的做空机制对指数现货市场带来的影响大于做多机制带来的影响;股指期货以其本身固有的领先于股指的特性,在应该抑制波动的时候反而加剧了波动,造成了负面影响,中国金融市场应重视其负面影响,并采取有效措施来加强对负面影响的控制。     

考虑成分股联跳与宏观信息发布的沪深300指数已实现波动率模型研究

利用日内高频数据计算的已实现波动率较好度量了金融资产的风险,因此对其预测模型的研究具有重要意义。考虑到指数成分股的联跳可能蕴含指数跳跃所未能反映的信息,提出运用非参数方法识别指数成分股的联跳,采用自回归条件风险模型估计成分股联跳强度,并将其引入指数的已实现波动率异质自回归（HAR-RV-CJ）模型中,分析模型预测性能的改进。进一步的,考虑到宏观信息公告的发布可能对股市产生整体性影响,相应影响成分股联跳的几率;因此,在成分股联跳的自回归条件风险模型中引入居民消费价格指数、国内生产总值、贸易差额等宏观信息公告变量,并分析对联跳强度估计以及指数已实现波动率预测的影响。采用2011年1月4日至2013年7月11日沪深300指数及其成分股高频数据的实证表明,指数成分股联跳与指数跳跃具有不同的特征;用成分股联跳强度代替HAR-RV-CJ模型中的跳跃构建的HAR-RV-CI模型,较原始的HAR-RV-CJ模型,以及同时考虑指数跳跃与成分股联跳强度的HAR-RV-CJI模型,具有明显较优的样本内拟合与样本外预测性能。引入宏观信息公告变量可以改进联跳强度自回归条件风险模型的拟合效果,并提高指数已实现波动率模型的样本内拟合能力,但对于指数已实现波动率的样本外预测性能并无明显的帮助。     

基于广义双曲线分布的人民币汇率波动性研究

针对人民币汇率的波动性问题,运用广义双曲线分布,对2006年1月4日到2015年7月24日美元兑人民币汇率的波动特征进行实证分析。研究认为,人民币汇率不仅具有显著的“尖峰厚尾”特点,而且具有波动的非对称性和集群性特征,同时发现GH分布能较为准确地描述和呈现美元兑人民币汇率收益率波动状况的分布情况。     

SABR随机波动率LIBOR市场模型的参数校准估计方法与实证模拟

针对标准化Libor市场模型（LMM）和Heston随机波动率Libor市场模型（Heston-LMM）的应用局限,首先将SABR代替Heston过程引入标准化Libor市场模型框架,建立非标准化的SABR随机波动率Libor市场模型（SABR-LMM）;在此基础上,运用利率上限期权（Cap）、利率互换期权（Swaption）和自适应马尔科夫链蒙特卡罗模拟方法（MCMC）对模型参数进行有效市场校准与模拟估计;最后,针对三个月美元Libor远期利率实际数据,对上述三类Libor市场模型的实际运行效果进行了实证模拟计算与比较分析。研究结论认为,基于模拟利差计算结果,针对短期Libor利率模拟而言,与LMM和Heston -LMM两类模型而言,加入SABR波动项的SABR-LMM模型具有更小的模拟误差,因而具有更好的模拟效果。     

台湾当代儒学的诠释学考察

检视台湾当代儒学诠释学的正当性与方法论,以回答它是否导致了当代新儒家过度诠释经典这一问题。林安梧否认熊十力有"六经注我"嫌疑,诠释是"存有三态论"哲学之要求。刘述先则提出"理一分殊"论。黄俊杰强调诠释与经典之间有两种张力,而孟子脉络解经法、"以意逆志"解经法是对此张力的缓解。李瑞全在儒学教学中实践体悟诠释方法。何信全则主张以西方形式逻辑来诠释儒学经典。     

中国汇率收益率及波动的周内效应实证研究

利用修正的GARCH-M模型,检验了中国2005 - 2010年期间人民币-美元汇率和人民币-欧元汇率收益率及波动的周内效应.研究发现,人民币-美元汇率在周二和周四具有显著升值特征,而人民币-欧元汇率在周四则更容易贬值并同时存在波动性的周二效应,仅在人民币-美元汇率收益率与波动之间呈现显著风险与收益的负向关系,反映出风险越高则人民币-美元汇率越容易升值,这可能是由于汇率市场中投资者的自适应预期所导致.     

The Realized Laplace Transform of Volatility

We introduce and derive the asymptotic behavior of a new measure constructed from high‐frequency data which we call the realized Laplace transform of volatility. The statistic provides a nonparametric estimate for the empirical Laplace transform function of the latent stochastic volatility process over a given interval of time and is robust to the presence of jumps in the price process. With a long span of data, that is, under joint long‐span and infill asymptotics, the statistic can be used to construct a nonparametric estimate of the volatility Laplace transform as well as of the integrated joint Laplace transform of volatility over different points of time. We derive feasible functional limit theorems for our statistic both under fixed‐span and infill asymptotics as well as under joint long‐span and infill asymptotics which allow us to quantify the precision in estimation under both sampling schemes.