Compared to the conditional mean or median, conditional quantiles provide a more comprehensive picture of a variable in various scenarios. A semi-parametric quantile estimation method for a double threshold auto-regression with exogenous regressors and heteroskedasticity is considered, allowing...

Bayesian variable selection in quantile regression models is often a difficult task due to the computational challenges and non-availability of conjugate prior distributions. These challenges are rarely addressed via either penalized likelihood function or stochastic search variable selection....

In this article, we develop a Bayesian method for quantile regression in the case of dichotomous response data. The frequentist approach to this type of regression has proven problematic in both optimizing the objective function and making inference on the regression parameters. By accepting...

The analysis of Tobit model with non-normal error distribution is extended to the case of asymmetric Laplace distribution (ALD). Since the ALD probability density function is known to be continuous but not differentiable, the usual mode-finding algorithms such as maximum likelihood can be...

Significantly driven by JP Morgan's RiskMetrics system with EWMA (exponentially weighted moving average) forecasting technique, value-at-risk (VaR) has turned to be a popular measure of the degree of various risks in financial risk management. In this paper we propose a new approach termed...

A parametric approach to estimating and forecasting Value-at-Risk (VaR) and expected shortfall (ES) for a heteroscedastic financial return series is proposed. The well-known GJR–GARCH form models the volatility process, capturing the leverage effect. To capture potential skewness and heavy...

Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation...

Insurers, investors and regulators are interested in understanding the behavior of insurance company expenses, due to the high operating cost of the industry. Expense models can be used for prediction, to identify unusual behavior, and to measure firm efficiency. Current literature focuses on...

In the present paper, the asymmetric type II compound Laplace distribution is introduced and various properties are studied. The maximum likelihood estimation procedure is employed to estimate the parameters of the proposed distribution and an algorithm in R package is developed to carry out the...