Parametric or Non-Parametric Estimation of Value-At-Risk

Backtesting Parametric Value-at-Risk with Estimation Risk

One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Since then, the capital requirements of commercial banks with trading activities are based on VaR estimates. Therefore, appropriately constructed tests for assessing the out-of-sample forecast accuracy of the VaR model (b...

Non–parametric volatility estimation

Non-parametric Estimation of Mean Customer Lifetime Value

This paper is about how to use data from a random sample of customer relationships to calculate an appropriate average Customer Lifetime Value (CLV). When the sample contains only completed relationships, the simple unweighted average is appropriate. When the sample contains a mix of active and completed relationships, the lifetimes of the active relationships are said to be right censored beca...

Estimation Risk Effects on Backtesting For Parametric Value-at-Risk Models

One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Thereby the correct specification of parametric VaR models became of crucial importance in order to provide accurate and reliable risk measures. If the underlying risk model is not correctly specified, VaR estimates under...

Notes on Non-parametric estimation

To tie up the discussion on the topic op estimation, more specifically spectral estimation, we derive here some tighter properties related to the direct use of the Fourier Transform, as contrasted to the properties of parametric methods such as the Levinson-Schur algorithm, where parameters of a model are estimated. We shall obtain a better insight in how well the Fourier Transform on discrete ...