Operations Research
http://lists.repec.org/mailman/listinfo/nep-ore
Operations Research
2018-03-12
Bayesian Analysis of Realized Matrix-Exponential GARCH Models
http://d.repec.org/n?u=RePEc:ems:eureir:104259&r=ore
The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric function to develop news impact curves. We consider Bayesian MCMC estimation to allow non-normal posterior distributions. For three US nancial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The empirical results indicate that the realized MEGARCH models outperform the other models regarding in-sample and out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.
Asai, M.
McAleer, M.J.
Multivariate GARCH, Realized Measure, Matrix-Exponential, Bayesian Markov, chain Monte Carlo method, Asymmetry
2018-01-01
Inference with Correlated Clusters
http://d.repec.org/n?u=RePEc:ran:wpaper:wr-1137-1&r=ore
This paper introduces a method which permits valid inference given a finite number of heterogeneous, correlated clusters. Many inference methods assume clusters are asymptotically independent or model dependence across clusters as a function of a distance metric. With panel data, these restrictions are unnecessary. This paper relies on a test statistic using the mean of the cluster-specific scores normalized by the variance and simulating the distribution of this statistic. To account for cross-cluster dependence, the relationship between each cluster is estimated, permitting the independent component of each cluster to be isolated. The method is simple to implement, can be employed for linear and nonlinear estimators, places no restrictions on the strength of the correlations across clusters, and does not require prior knowledge of which clusters are correlated or even the existence of independent clusters. In simulations, the procedure rejects at the appropriate rate even in the presence of highly-correlated clusters.
David Powell
Finite Inference, Correlated Clusters, Fixed Effects, Panel Data, Hypothesis
2017-06
Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors
http://d.repec.org/n?u=RePEc:fip:fedawp:2018-02&r=ore
Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the out-of-sample behavior of the regime parameters. However, the hierarchical priors have been parametric. Their parametric nature leads to global shrinkage that biases the estimates of the parameter coefficient of extraordinary regimes toward the value of the average regime. To overcome this shrinkage, we model the hierarchical prior nonparametrically by letting the hyperparameter's priorâ€”in other words, the hyperpriorâ€”be unknown and modeling it with a Dirichlet processes prior. To apply a nonparametric hierarchical prior to the probability of a break occurring, we extend the change point model to a multiple-change-point panel model. The hierarchical prior then shares in the cross-sectional information of the break processes to estimate the transition probabilities. We apply our multiple-change-point panel model to a longitudinal data set of actively managed, U.S. equity, mutual fund returns to measure fund performance and investigate the chances of a skilled fund being skilled in the future.
Fisher, Mark
Jensen, Mark J.
Bayesian nonparametric analysis; change points; Dirichlet process; hierarchical priors; mutual fund performance
2018-02-01
Forecasting Stock Returns: A Predictor-Constrained Approach
http://d.repec.org/n?u=RePEc:brd:wpaper:116r&r=ore
We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it at zero whenever its value falls below the variable's past 12-month high. Empirically, we find that relative to standard unconstrained predictive regressions, our approach leads tosignificantly larger forecasting gains, both in statistical and economic terms. We also show how a simple equal-weighted combination of the constrained forecasts leads to further improvements in forecast accuracy, with predictions that are more precise than those obtained either using the Campbell and Thompson (2008) or Pettenuzzo, Timmermann, and Valkanov (2014) methods. Subsample analysis and a large battery of robustness checks confirm that these findings are robust to the presence of model instabilities and structural breaks.
Davide Pettenuzzo
Zhiyuan Pan
Yudong Wang
Equity premium; Predictive regressions; Predictor constraints; 24-month high and low; Model combinations
2017-10