This paper presents a new method for identifying triangular systems of time-series data. Identification is the product of a bivariate GARCH process. Relative to the literature on GARCH-based identification, this method distinguishes itself both by allowing for a timevarying covariance and by not...

Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails are Pareto like, under conditions that allow the innovations from the, respective, processes to be skewed. Skewness is considered a stylized fact for many financial returns...

Simple, multi-step estimators are developed for the popular GARCH(1,1) model, where these estimators are either available entirely in closed form or dependent upon a preliminary estimate from, for example, quasi-maximum likelihood. Identification sources to asymmetry in the model's innovations,...

Contained herein are detailed proofs of all the Lemmas that support the main Theorems discussed in the paper, "Simple Estimators for GARCH models."Original paper can be found at: "https://ssrn.com/abstract=2897867" https://ssrn.com/abstract=2897867

Closed-form estimators are developed for the popular GARCH(1,1) and threshold GARCH(1,1) models, with select results extending to the general GARCH(p, q) model. Identification sources to asymmetry, either in the distribution of rescaled errors or in the conditional variance function. Given...

Strong consistency and weak distributional convergence to highly non-Gaussian limits are established for closed-form, two stage least squares (TSLS) estimators for a class of ARCH(p) models. Conditions for these results include (relatively) mild moment existence criteria that are supported...

A new estimator is proposed for linear triangular systems, where identification results from the model errors following a bivariate and diagonal GARCH(1,1) process with potentially time-varying error covariances. This estimator applies when traditional instruments are unavailable. I demonstrate...