The estimation for Lévy processes in high frequency data

In this article, a generalized Lévy model is proposed and its parameters are estimated in high-frequency data settings. An infinitesimal generator of Lévy processes is used to study the asymptotic properties of the drift and volatility estimators. They are consistent asymptotically and are independent of other parameters making them better than those in Chen et al. (2010 Chen, S. X., Delaigle, A., Hall, P. (2010). Nonparametric estimation for a class of Lévy processes. Journal of Econometrics 157:257–271.[Crossref], [Web of Science ®] , [Google Scholar]). The estimators proposed here also have fast convergence rates and are simple to implement.     

Bayesian analysis of skew-normal independent linear mixed models with heterogeneity in the random-effects population

We present a new class of models to fit longitudinal data, obtained with a suitable modification of the classical linear mixed-effects model. For each sample unit, the joint distribution of the random effect and the random error is a finite mixture of scale mixtures of multivariate skew-normal distributions. This extension allows us to model the data in a more flexible way, taking into account skewness, multimodality and discrepant observations at the same time. The scale mixtures of skew-normal form an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal, skew-Student-t, skew-slash and the skew-contaminated normal distributions as special cases, being a flexible alternative to the use of the corresponding symmetric distributions in this type of models. A simple efficient MCMC Gibbs-type algorithm for posterior Bayesian inference is employed. In order to illustrate the usefulness of the proposed methodology, two artificial and two real data sets are analyzed.     

基于随机波动模型的人民币汇率波动性分析

自人民币汇率体制改革以来,汇率波动日趋复杂,对我国经济的影响也更加重要.鉴于此,文章运用随机波动(SV)模型对汇改后美元兑人民币汇率进行分析,结果表明杠杆效应对我国外汇市场的影响较小,而人民币汇率收益率与市场风险密切相关.     

Preserving data utility via BART

When preparing data for public release, information organizations face the challenge of preserving the quality of data while protecting the confidentiality of both data subjects and sensitive data attributes. Without knowing what type of analyses will be conducted by data users, it is often hard to alter data without sacrificing data utility. In this paper, we propose a new approach to mitigate this difficulty, which entails using Bayesian additive regression trees (BART), in connection with existing methods for statistical disclosure limitation, to help preserve data utility while meeting confidentiality requirements. We illustrate the performance of our method through both simulation and a data example. The method works well when the targeted relationship underlying the original data is not weak, and the performance appears to be robust to the intensity of alteration.     

SV模型下中国股票型开放式基金杠杆效应分析

针对中国股票型开放式基金收益波动中是否存在杠杆效应的问题,在对该类基金整体及所选取的三支具有代表性的单个基金分析的基础上,运用一个带杠杆效应的SV模型对其收益的波动性建模,并利用MCMC方法对模型进行参数估计。结果显示：不同于一般对股票市场的研究结论,无论股票型开放式基金整体还是单个基金,其收益率序列的波动中均不存在显著的杠杆效应。     

The Effect of Timing on Jump Bidding in Ascending Auctions

We investigate the role of timing in ascending auctions under the premise that time is a valuable resource. Traditional models of the English auction ignore timing issues by assuming that the auction occurs instantaneously. However, when auctions are slow, as Internet auctions used for procurement often are, there are significant opportunity or monitoring costs to bidders, and the choice of the size of the jump bid becomes a strategic decision. We study the choice in the experimental laboratory by systematically varying the opportunity costs associated with fast bidding. When time is more valuable bidders respond by choosing larger jump bids. Surprisingly, the economic performance of the auction is not significantly affected. We develop a simple model of ascending auctions with impatient bidders that provides insights into the effect jump bids have on auction performance.     

我国银行间同业拆借利率的动态研究———基于跳跃 扩散 机制转换模型的实证分析

针对我国短期利率易受政策影响,波动较大并存在结构变化等特点,构建了跳跃 扩散 机制转换模型,同时考察了银行间 7 天同业拆借利率的波动、跳跃和结构变化三种效应,发现我国同业拆借利率不仅具有均值回归特性而且还存在明显的跳跃与机制转换,并且该模型 比其嵌套的受限模型表现更佳． 在高波动状态下利率波动的水平效应和 ARCH 效应可以忽略; 低波动状态下,水平效应可以忽略． 另外,跳跃具有聚类效应,高( 低) 的跳跃概率和高( 低)状态概率对应着高( 低) 利率和较高( 低) 的波动率,跳跃主要发生在高状态机制下,低状态机 制下发生跳跃的可能性很小．     

货币政策对M2的动态效应时滞分析及危机效应测算

科学测算金融危机期间已出台货币政策对货币供应量M2的效应时滞和贡献程度具有一定的现实价值。文章利用Markov状态转换及HP滤波模型识别不同货币政策状态的时间区间;利用多项式滞后分布模型分析政策工具变量对货币供应量影响的滞后效应,并以此为先验信息,建立了货币政策的Bayesian-PDLS动态效应分布模型,对金融危机期间已出台的货币政策效果进行了实际测算。主要结果表明:(1)外汇储备为M2的决定性因素,存款准备金率为M2主要的限制性因素;(2)货币政策变量M2总体对利率的弹性变化呈"U"型分布;在紧缩型情况下M2对利率的弹性变化呈"W"型分布;在扩张情形下M2对利率的弹性变化呈"v"型分布。     

我国银行间同业拆借利率的动态研究——基于跳跃-扩散-机制转换模型的实证分析

针对我国短期利率易受政策影响,波动较大并存在结构变化等特点,构建了跳跃-扩散-机制转换模型,同时考察了银行间7天同业拆借利率的波动、跳跃和结构变化三种效应,发现我国同业拆借利率不仅具有均值回归特性而且还存在明显的跳跃与机制转换,并且该模型比其嵌套的受限模型表现更佳.在高波动状态下利率波动的水平效应和ARCH效应可以忽略...     

Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework

Abstract

A key question for understanding the cross-section of expected returns of equities is the following: which factors, from a given collection of factors, are risk factors, equivalently, which factors are in the stochastic discount factor (SDF)? Though the SDF is unobserved, assumptions about which factors (from the available set of factors) are in the SDF restricts the joint distribution of factors in specific ways, as a consequence of the economic theory of asset pricing. A different starting collection of factors that go into the SDF leads to a different set of restrictions on the joint distribution of factors. The conditional distribution of equity returns has the same restricted form, regardless of what is assumed about the factors in the SDF, as long as the factors are traded, and hence the distribution of asset returns is irrelevant for isolating the risk-factors. The restricted factors models are distinct (nonnested) and do not arise by omitting or including a variable from a full model, thus precluding analysis by standard statistical variable selection methods, such as those based on the lasso and its variants. Instead, we develop what we call a Bayesian model scan strategy in which each factor is allowed to enter or not enter the SDF and the resulting restricted models (of which there are 114,674 in our empirical study) are simultaneously confronted with the data. We use a Student-t distribution for the factors, and model-specific independent Student-t distribution for the location parameters, a training sample to fix prior locations, and a creative way to arrive at the joint distribution of several other model-specific parameters from a single prior distribution. This allows our method to be essentially a scaleable and tuned-black-box method that can be applied across our large model space with little to no user-intervention. The model marginal likelihoods, and implied posterior model probabilities, are compared with the prior probability of 1/114,674 of each model to find the best-supported model, and thus the factors most likely to be in the SDF. We provide detailed simulation evidence about the high finite-sample accuracy of the method. Our empirical study with 13 leading factors reveals that the highest marginal likelihood model is a Student-t distributed factor model with 5 degrees of freedom and 8 risk factors.