This paper presents a Markov chain Monte Carlo (MCMC) algorithm to estimate parameters and latent stochastic processes in the asymmetric stochastic volatility (SV) model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian distribution and the marginal density of asset returns has heavytails. To test for the significance of the Box-Cox transformation...

Misspecification Testing in a Class of Conditional Distributional Models We propose a specification test for a wide range of parametric models for the conditional distribution function of an outcome variable given a vector of covariates. The test is based on the Cramer-von Mises distance between an unrestricted estimate of the joint distribution function of the data, and a restricted estimate t...

In this paper, we show the first order validity of the block bootstrap in the context of Kolmogorov type conditional distribution tests when there is dynamic misspecification and parameter estimation error. Our approach differs from the literature to date because we construct a bootstrap statistic that allows for dynamic misspecification under both hypotheses. We consider two test statistics; o...

We develop two specification tests of predictive densities based on that the generalized residuals of correctly specified predictive density models are i.i.d. uniform. The simultaneous test compares the joint density of generalized residuals with product of uniform densities; the sequential test examines the hypotheses of serial independence and uniformity sequentially based on the copula repre...

This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of ...

We find a subtle but substantial bias in a standard measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data. The mechanism is driven by a form of selection bias, which leads to an underestimate of the true conditional probability of a given outcome when conditioning on prior outcomes of the same kind. The biased measure has been used prominently ...

This paper develops a new framework and tools, and reexamines Fama-French regressions. For Fama-French portfolios, we consider a continuous-time factor model with a specific error component structure implied by the underlying asset pricing theory. The model is then analyzed as a continuous-time multivariate regression with a general martingale differential error, allowing for time-varying and s...

Article history: Received 25 October 2008 Received in revised form 19 July 2009 Accepted 5 January 2010 Available online 18 January 2010 In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the “superior predictive ability” (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify pred...

When weak identification is a concern researchers frequently calculate confidence sets in two steps, first assessing the strength of identification and then, on the basis of this initial assessment, deciding whether to use an identification-robust confidence set. Unfortunately, two-step procedures of this sort can generate highly misleading confidence sets, and we demonstrate that two-step conf...

We propose a Vuong (1989)-type model selection test for models defined by conditional moment restrictions. The moment restrictions can be standard equality restrictions that point identify the model parameters, or moment equality or inequality restrictions that partially identify the model parameters. The test uses a new average generalized empirical likelihood criterion function designed to in...