Risk Capital Aggregation

This article presents a new approach to determine the total risk of a financial institution. The proposed model includes components for credit, market, operational and business risk. Moreover, it includes a component for the ownership risk that stems from holding a life insurance company. The approach may be characterized as a base-level aggregation method. However, due to lack of appropriate data, some of the aggregation steps are done on the top level instead. The economic risk factors used in the base-level aggregation are described by a multivariate GARCH model with Student's t-distributed innovations. The loss distributions for the different risk types are determined by non-linear functions of the fluctuations in the risk factors. Hence, these marginal loss distributions are indirectly correlated through the relationship between the risk factors. The model was originally developed for DnB NOR, the largest financial institution in Norway, and one of the largest ones in the Nordic region. Being adapted to the requirements in the Basel II regulations, it will play an important part in measuring and assessing the risk level of the institution.

[1]  L. Bauwens,et al.  Multivariate GARCH Models: A Survey , 2003 .

[2]  Til Schuermann,et al.  Risk Measurement, Risk Management, and Capital Adequacy in Financial Conglomerates , 2003 .

[3]  Gerry Salkin,et al.  Modeling and measuring operational risk , 1998 .

[4]  N. Shephard.  Statistical aspects of ARCH and stochastic volatility , 1996 .

[5]  Detlef Seese,et al.  A Multi-objective Approach to Integrated Risk Management , 2005, EMO.

[6]  Michael B. Gordy.  A Risk-Factor Model Foundation for Ratings-Based Bank Capital Rules , 2003 .

[7]  Mortgage Lending,et al.  Position Paper on the New Basel Capital Accord - Consultative Document from the Basel Committee on Banking Supervision (January 2001) , 2001 .

[8]  F. Longin,et al.  Extreme Correlation of International Equity Markets , 2000 .

[9]  C. Alexander.  The Present and Future of Financial Risk Management , 2004 .

[10]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[11]  T. Mikosch.  Is it really long memory we see in financial returns , 2004 .

[12]  Kjersti Aas,et al.  Integrated risk modelling , 2004 .

[13]  J. Rosenberg,et al.  A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risk , 2004 .

[14]  R. C. Merton,et al.  On the Pricing of Corporate Debt: The Risk Structure of Interest Rates , 1974, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[15]  A. Harvey,et al.  5 Stochastic volatility , 1996 .

[16]  Lisa S. Ward,et al.  Practical Application of the Risk-Adjusted Return on Capital Framework , 2002 .

[17]  F. Saita,et al.  Risk Capital Aggregation , 2007 .

[18]  S. Rachev,et al.  A Note on the Estimation of the Frequency and Severity Distribution of Operational Losses , 2004 .

[19]  P. Embrechts,et al.  Correlation: Pitfalls and Alternatives , 1999 .

[20]  J. Wooldridge,et al.  Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances , 1992 .

[21]  Eric S. Rosengren,et al.  Capital and Risk: New Evidence on Implications of Large Operational Losses , 2003 .

[22]  T. Bollerslev,et al.  Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model , 1990 .