Document Type

Conference Proceeding

Publisher

Edith Cowan University

Faculty

Business and Law

School

School of Accounting, Finance and Economics/Finance, Economics, Markets and Accounting Research Centre

RAS ID

10693

Comments

This article was originally published as: Allen, D. E., Powell, R. , & Singh, A. (2010). Using Quantile Regression to Estimate Capital Buffer Requirements for Japanese Banks. Proceedings of Globalization, Monetary Integration and Exchange Rate Regimes in East Asia. (pp. 23p.). Perth. Edith Cowan University. Original article available here

Abstract

This paper investigates the impact of extreme fluctuations in bank asset values on the capital adequacy and default probabilities (PD) of Japanese Banks. We apply quantile regression analysis to the Merton structural credit model to measure how capital adequacy and PDs fluctuate over a 10 year period incorporating the Global Financial Crisis (GFC). Quantile regressions allow modelling of the extreme quantiles of a distribution, as opposed to focussing on the mean, which allows measurement of capital and PDs at the most extreme points of an economic downturn. Understanding extreme risk is essential, as it is during these extreme circumstances when banks are most likely to fail. We find highly significant variances in bank capital adequacy and default probabilities between quantiles, and show how these variances can assist banks and regulators in calculating capital buffers which will sustain banks through volatile times. Quantile regression has been successfully applied to the measurement of extreme market (share price) risk by a number of studies, and this paper develops unique and innovative techniques for extending this approach to structural credit risk modelling.

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