In practice, there is a massive time lag between data loss and its cause identification. The existing techniques perform it comprehensively, but they consume too much time, so there is a need for fast and reliable methods. The article’s purpose is to develop a rapid methodology to assess the...

The purpose of this study is to assess model risk with respect to parameter estimation for a simple binary logistic regression model applied as a predictive model. The assessment is done by comparing the effectiveness of eleven different parameter estimation methods. The results from the...

This paper studies some seemingly anomalous results that arise in possibly misspecified and unidentified linear asset-pricing models estimated by maximum likelihood and one-step generalized method of moments (GMM). Strikingly, when useless factors (that is, factors that are independent of the...

This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset-pricing models estimated by maximum likelihood and one-step generalized method of moments. Strikingly, when spurious factors (that is, factors that are uncorrelated with...

Classical interval estimation ignores misspecification uncertainty that is almost inevitable in practice. This paper proposes an approach to construct an uncertainty interval that incorporates misspecification based on an $f$-divergence. We construct the uncertainty interval estimators using...

This paper studies the averaging GMM estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is the sample analog of an infeasible optimal weight. We...

A common problem in asset and portfolio risk and performance analysis is that the manager has such a short history of asset returns that risk and performance measure estimates are quite unreliable. But the manager has available long histories of many risk factors and can use a subset of them to...

In this paper we theoretically derive the risk of Zellner's extended minimum expected loss function estimator. Using artificial data, we then calculate the risks of known nested estimators that include simple minimum expected loss function, two stage least squares and ordinary least squares. The...

This paper answers two research questions: what is the appropriate modeling tool for NPL study? and whether the NPL rates in Thailand show improving or deteriorating trend? NPL is of interests to management decision makers because it serves as an indicator for assessing risk in commercial loans....

This paper derives two new improved risk metrics LAPVaR and LAPSF. Traditional VaRDeltaNormal valuation exaggerates market and liquidity risks to the point it could be larger than the actual portfolio value. Put VaR – PVaR – as well as Put Shortfall – PSF – uses option theory to solve...