24 Pages Posted: 23 Nov 2016
Date Written: November 23, 2016
Operational risk models commonly employ maximum likelihood estimation (MLE) to fit loss data to heavy-tailed distributions. Yet several desirable properties of MLE (e.g., asymptotic normality) are generally valid only for large sample sizes, a situation that is rarely encountered in operational risk. In this paper, we study how asymptotic normality does, or does not, hold for common severity distributions in operational risk models. We then apply these results to evaluate errors caused by failure of asymptotic normality in constructing confidence intervals around the MLE fitted parameters.
Keywords: asymptotic normality, heavy-tailed distributions, maximum likelihood estimation (MLE), operational risk models, loss distribution approach (LDA)
Suggested Citation: Suggested Citation
Larsen, Paul, Operational Risk Models and Asymptotic Normality of Maximum Likelihood Estimation (November 23, 2016). Journal of Operational Risk, Vol. 11, No. 4, 2016. Available at SSRN: https://ssrn.com/abstract=2874768
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