Local-Linear Estimation of Time-Varying-Parameter GARCH Models and Associated Risk Measures

Outliers in GARCH models and the estimation of risk measures

In this paper we focus on the impact of additive level outliers on the calculation of risk measures such as minimum capital risk requirements and four possible alternatives of reducing these measures’ estimation biases. The first and second alternatives are based on wavelets while the third is based on the traditional proposals in the literature and the three are based on the detection and corr...

Parameter estimation in nonlinear AR - GARCH models

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be ind...

Quantifying time-varying cellular secretions with local linear models

Extracellular protein concentrations and gradients initiate a wide range of cellular responses, such as cell motility, growth, proliferation and death. Understanding inter-cellular communication requires spatio-temporal knowledge of these secreted factors and their causal relationship with cell phenotype. Techniques which can detect cellular secretions in real time are becoming more common but ...

Estimation of Value at Risk (VaR) Based On Lévy-GARCH Models: Evidence from Tehran Stock Exchange

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of ...

Estimation of Time-varying Linear Systems