This paper proposes an adaptive quasi-maximum likelihood estimation when forecasting the volatility of financial data with the generalized autoregressive conditional heteroscedasticity(GARCH) model. When the distribution of volatility data is unspecified or heavy-tailed, we worked out adaptive...

We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially...

A data-cloning SMC² method is proposed as a general purpose optimization routine for estimating latent variable models by maximum likelihood. The latent variables are first marginalized out by SMC at any fixed parameter value, and the model parameters are then estimated by density tempered SMC....

Maximum likelihood Estimation is an important aspect of frequentist approach which was introduced by RA Fisher. Maximum Likelihood estimation method helps us to find the estimator for the unknown population parameter. There are other methods of estimation also available such as Least Square...

Market leverage is one of the main determinants for defining the optimal capital structure of the firm, and has a significant impact on several variables that affect the design of the firm's business strategy. This paper is focussed on the potential methodologies exploitable to obtain reliable...

We derive expressions of use in the maximum likelihood estimation of a parameterized growth rate where the quantity growing is a Poissonian count rate parameterized in such a manner as to make it suitable to measure the number of Twitter accounts following an account that makes directional...

Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used...

Integer valued time series are useful models for describing dependence structures over time for count data for which classical time series models are inappropriate. Such models have been used for a variety of applications. Difficulties arise in attempting to estimate the parameters via maximum...

This paper investigates the asymptotic properties of a simple empirical-likelihood-based inference method for discontinuity in density. The parameter of interest is a function of two one-sided limits of the probability density function at (possibly) two cut-off points. Our approach is based on...

We use the EM algorithm to derive recursive expressions for maximum likelihood location and scale estimators for Tukey's corner distributions, in particular the one-wild. This now enables optimal estimation for the triefficiency criterion used to appraise robust estimators. The effect of...