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随机波动模型的贝叶斯分析AI 解读

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance…

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《随机波动模型的贝叶斯分析》评论AI 解读

This article contains comments on 'Bayesian Analysis of Stochastic Volatility Models,' by Jacquier, Polson, and Rossi. The Markov-chain Monte Carlo (MCMC) method proposed is compared empirically with a simulated maximum likelihood (SML) method. The MCMC and SML estimators yield very similar results, both when applied to actual data and in a Monte Carlo experiment.

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在未知平稳化方式下建模股票价格AI 解读

Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and…

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波动率与各国股票市场之间的联系AI 解读

The authors attempt to account for the covariances between stock markets and to assess their integration. They estimate a factor model for sixteen national stock market returns whose volatility is induced by changing volatility in the factors. Unanticipated returns depend on innovations in economic variables and 'unobservable' factors. Assets risk premia are linear combinations of the factors risk premia. The authors find that idiosyncratic risk is priced and the 'price of…

机制转换 ARCH 的马尔可夫模型AI 解读

In this article I present a new approach to model more realistically the variability of financial time series. I develop a Markov-ARCH model that incorporates the features of both Hamilton's switching-regime model and Engle's autoregressive conditional heteroscedasticity (ARCH) model to examine the issue of volatility persistence in the monthly excess returns of the three-month treasury bill. The issue can be resolved by taking into account occasional shifts in the…

风险中性多证券市场中的不完全竞争AI 解读

THE CENTRAL PURPOSE OF THIS PAPER is to develop a model of insider trading (i.e., trading based on private information) in the context of an imperfectly competitive multi-security market with risk-neutral agents. Imperfect competition allows us to consider strategic behavior, and a multi-security market lets us study the effect of a correlated environment on equilibrium. We employ the informational assumption that market makers can observe all order flows, and so portfolio…

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基于扩散过程离散观测的贝叶斯因子AI 解读

We present an approach to model selection for a time series of data on a fine time scale. The underlying process generating the data is modelled as a continuous time stochastic process. The underlying continuous processes are assumed to be diffusions with time varying drift and diffusion coefficient. Several approaches to modelling the diffusion coefficient are described. To perform model selection, we propose an approximation to the Bayes factor that uses only the discrete…

GARCH(1,1) 准最大似然估计量的渐近理论AI 解读

This paper investigates the sampling behavior of the quasi-maximum likelihood estimator of the Gaussian GARCH(1,1) model. The rescaled variable (the ratio of the disturbance to the conditional standard deviation) is not required to be Gaussian nor independent over time, in contrast to the current literature. The GARCH process may be integrated (α + β = 1), or even mildly explosive (α + β > 1). A bounded conditional fourth moment of the rescaled variable is sufficient for the…

单变量 ARCH 模型的渐近滤波理论AI 解读

Researchers often employ ARCH models to estimate conditional variances and covariances. How successfully can misspecified ARCH models carry out this estimation? This paper employs continuous record asymptotics to approximate the distribution of the measurement error. This allows the authors to (1) compare the efficiency of various ARCH models, (2) characterize the impact of different kinds of misspecification on efficiency, and (3) characterize asymptotically optimal ARCH…

将 Hansen–Jagannathan 界用于资产定价模型的经典检验AI 解读

In this article, tests of the implications of consumption-based asset-pricing models are developed. Four of these tests are based on variance bounds for intertemporal marginal rates of substitution introduced by L. P. Hansen and R. Jagannathan. The tests provide one means of quantifying the effects of sampling error when the bounds are used as a diagnostic device. The tests are used to construct confidence regions for the parameters of an asset-pricing model using U.S. data.…