nep-ore 2022-05-16 papers

on Operations Research

Issue of 2022‒05‒16
two papers chosen by
Walter Frisch
Universität Wien

A multivariate GARCH model with an infinite hidden Markov mixture By Li, Chenxing
Weighted Average Estimation in Panel Data By Ali Mehrabani; Aman Ullah

A multivariate GARCH model with an infinite hidden Markov mixture

By:	Li, Chenxing
Abstract:	This paper proposes a new Bayesian semiparametric model that combines a multivariate GARCH (MGARCH) component and an infinite hidden Markov model. The new model nonparametrically approximates both the shape of unknown returns distributions and their short-term evolution. It also captures the smooth trend of the second moment with the MGARCH component and the potential skewness, kurtosis, and volatility roughness with the Bayesian nonparametric component. The results show that this more-sophisticated econometric model not only has better out-of-sample density forecasts than benchmark models, but also provides positive economic gains for a CRRA investor at different risk-aversion levels when transaction costs are assumed. After considering the transaction costs, the proposed model dominates all benchmark models/portfolios when No Short-Selling or No Margin-Trading restriction is imposed.
Keywords:	Multivariate GARCH; IHMM; Bayesian nonparametric; Portfolio allocation; Transaction costs
JEL:	C11 C14 C32 C34 C53 C58
Date:	2022–03–16
URL:	http://d.repec.org/n?u=RePEc:pra:mprapa:112792&r=

Weighted Average Estimation in Panel Data

By:	Ali Mehrabani (Southern Illinois University); Aman Ullah (Department of Economics, University of California Riverside)
Abstract:	Mukhtar M. Ali has made many innovative and influential contributions in different areas of economics, finance, econometrics, and statistics. His contributions include developing econometric models to examine the determinants of the demand for casino gaming, investigating the approximate and exact distribution and moments of various econometric estimators and test statistics, and studying the statistical properties of time series based statistics under stationary and non-stationary processes (for example, see Ali and Thalheimer (1983, 2008), Ali (1977, 1979, 1984, 1989), Ali and Sharma (1993, 1996), Tsui and Ali (1992, 2002), Ali and Giaccotto (1982a, 1982b, 1984), Ali and Tiao (1971), and Ali and Silver (1985, 1989), among others). All of these have made significant impact on the profession and have been instrumental in advancing further research in statistics and econometrics. In this paper, we study the approximate rst two moments of two weighted average estimators of the slope parameters in linear panel data models. The weighted average estimators shrink a generalized least squares estimator towards a restricted generalized least squares estimator, where the restrictions represent possible parameter specifications. The averaging weight is inversely proportional to a weighted quadratic loss function. The approximate bias and second moment matrix of the weighted average estimators using the large-sample approximations are provided. We give the conditions under which the weighted average estimators dominate the generalized least squares estimator on the basis of their mean squared errors.
Keywords:	Asymptotic approximations; xed-e ects; panel data; random-e ects; Stein-like shrinkage estimator.
Date:	2022–04
URL:	http://d.repec.org/n?u=RePEc:ucr:wpaper:202209&r=

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