Abstract The Generative Adversarial Networks, as one of the most creative deep learning
models in recent years, has achieved great success in computer vision and natural …

The asymptotic behavior of quantile regression inference becomes dramatically different
when it involves a persistent predictor with zero or nonzero intercept. Distinguishing various …

This paper proposes a first-order zero-drift GARCH (ZD-GARCH (1, 1)) model to study
conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH …

Estimating conditional quantiles of financial time series is essential for risk management and
many other financial applications. For time series models with conditional heteroscedasticity …

Generative AI is making buzz all over the globe and has mostly drawn attention due to it's
ability to generate variety of content that mimics human behaviour and intelligence along …

This article proposes a general class of tests to examine whether the error term is a
martingale difference sequence in a multivariate time series model with parametric …

We compute the value-at-risk of financial losses by fitting a generalized Pareto distribution to
exceedances over a threshold. Following the common practice of setting the threshold as …

Recently, Heras et al.(2018. An application of two-stage quantile regression to insurance
ratemaking. Scandinavian Actuarial Journal 9, 753–769) propose a two-step inference to …

This paper provides an entire inference procedure for the autoregressive model under
(conditional) heteroscedasticity of unknown form with a finite variance. We first establish the …

Conditional least squares estimation is often employed to infer an integer-valued AR (1)
model and its convergence rate and asymptotic variance differ for the stable and nearly …