Antonis Demos

An EM Algorithm for Conditionally Heteroscedastic Factor Models

This article discusses the application of the EM algorithm to factor models with dynamic heteroscedasticity in the common factors. It demonstrates that the EM algorithm reduces the computational burden so much that researchers can estimate such models with many series. Two empirical applications with 11 and 266 stock returns are presented, confirming that the EM algorithm yields significant speed gains and that it makes unnecessary the computation of good initial values. Near the optimum, however, it slows down significantly. Then, the best practical strategy is to switch to a first-derivative-based method.

An event study analysis of outward foreign direct investment: the case of Greece

The purpose of this article is twofold. Firstly, by applying the event study methodology, it provides detailed and updated evidence on the value generating effect of different modes of foreign direct investment (FDI) entry. Secondly, this is the first paper to empirically evaluate the impact of FDI on the stock returns of Greek firms participating in the Athens Stock Exchange (ASE). In the case of Greece, the cross‐section analysis revealed that successful outward FDI projects tend to be located in developed countries, performed in a high‐technology sector and linked to horizontal integration.

A Class of Indirect Inference Estimators: Higher‐Order Asymptotics and Approximate Bias Correction

In this paper, we define a set of indirect inference estimators based on moment approximations of the auxiliary estimators. Their introduction is motivated by reasons of analytical and computational facilitation. Their definition provides an indirect inference framework for some classical bias correction procedures. We derive higher‐order asymptotic properties of these estimators. We demonstrate that under our assumption framework, and in the special case of deterministic weighting and affinity of the binding function, these are second‐order unbiased. Moreover, their second‐order approximate mean square errors do not depend on the cardinality of the Monte Carlo or bootstrap samples that our definition might involve. Consequently, the second‐order mean square error of the auxiliary estimator is not altered. We extend this to a class of multistep indirect inference estimators that have zero higher‐order bias without increasing the approximate mean square error, up to the same order. Our theoretical results are also validated by three Monte Carlo experiments.

On the Validity of Edgeworth Expansions and Moment Approximations for Three Indirect Inference Estimators

Abstract This paper deals with higher order asymptotic properties for three indirect inference estimators. We provide conditions that ensure the validity of locally uniform, with respect to the parameter, Edgeworth approximations. When these are of sufficiently high order they also form integrability conditions that validate locally uniform moment approximations. We derive the relevant second order bias and MSE approximations for the three estimators as functions of the respective approximations for the auxiliary estimator. We confirm that in the special case of deterministic weighting and affinity of the binding function, one of them is second order unbiased. The other two estimators do not have this property under the same conditions. Moreover, in this case, the second order approximate MSEs imply the superiority of the first estimator. We generalize to multistep procedures that provide recursive indirect inference estimators which are locally uniformly unbiased at any given order. Furthermore, in a particular case, we manage to validate locally uniform Edgeworth expansions for one of the estimators without any differentiability requirements for the estimating equations. We examine the bias-MSE results in a small Monte Carlo exercise.

Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes likelihood analysis of these models computationally infeasible. This article outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.

Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model

Time-varying GARCH-M models are commonly used in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only 𝑂(𝑇) computational operations, where 𝑇 is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.

Risk and Return in January: Some UK Evidence

A large body of empirical research has found that stock returns tend to be higher in January than in other months. One possible explanation is that there is seasonality in the risk-return structure. We examine the risk-return relationship for the UK equity market. We use monthly sectorial data to estimate a dynamic version of the APT that explicitly allows for a different conditional factor structure in January from the rest of the year. While we confirm the US finding that the risk-return relationship is different in January, our results depart from the existing literature in two ways. First, we find a consistently positive and statistically significant relationship between non-diversifiable risk and return in non-January months. Second, we find some evidence against the APT restrictions on our model in January, in that the price of January risk may not be common across assets.

Finite sample theory and bias correction of maximum likelihood estimators in the EGARCH model

Abstract We derive the analytical expressions of bias approximations for maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of the EGARCH (1,1) parameters that enable us to correct after the bias of all estimators. The bias-correction mechanism is constructed under the specification of two methods that are analytically described. We also evaluate the residual bootstrapped estimator as a measure of performance. Monte Carlo simulations indicate that, for given sets of parameters values, the bias corrections work satisfactory for all parameters. The proposed full-step estimator performs better than the classical one and is also faster than the bootstrap. The results can be also used to formulate the approximate Edgeworth distribution of the estimators.