Basel II and Operational Risk: Implications for risk measurement and management in the financial sector

This paper proposes a methodology to analyze the implications of the Advanced Measurement Approach (AMA) for the assessment of operational risk put forward by the Basel II Accord. The methodology relies on an integrated procedure for the construction of the distribution of aggregate losses, using internal and external loss data. It is illustrated on a 2x2 matrix of two selected business lines and two event types, drawn from a database of 3000 losses obtained from a large European banking institution. For each cell, the method calibrates three truncated distributions functions for the body of internal data, the tail of internal data, and external data. When the dependence structure between aggregate losses and the non-linear adjustment of external data are explicitly taken into account, the regulatory capital computed with the AMA method proves to be substantially lower than with less sophisticated approaches allowed by the Basel II Accord, although the effect is not uniform for all business lines and event types. In a second phase, our models are used to estimate the effects of operational risk management actions on bank profitability, through a measure of RAROC adapted to operational risk. The results suggest that substantial savings can be achieved through active management techniques, although the estimated effect of a reduction of the number, frequency or severity of operational losses crucially depends on the calibration of the aggregate loss distributions.operational risk management, basel II, advanced measurement approach, copulae, external data, EVT, RAROC, cost-benefit analysis.

Neonatology

The Treynor and Mazuy framework is a widely used return-based model of market timing. However, existing corrections to the regression intercept can be manipulated through derivatives trading. We propose an adjustment based on Merton's option replication approach. The linear and quadratic coefficients of the regression are exploited to assess the cost of the replicating option that yields similar convexity for a passive portfolio. A similar reasoning applies for various timing patterns and in multi-factor models. The proposed framework induces a potential rebalancing risk and involves the delicate issue of choosing the cheapest option. We show that these issues can be overcome for reasonable tolerance levels

The Market Timing Skills of Hedge Funds during the Financial Crisis

The performance of a market timer can be measured through the Treynor and Mazuy (1966) model, provided the regression alpha is properly adjusted by using the cost of an option-based replicating portfolio, as shown by Hübner (2010). We adapt this approach to the case of multi-factor models with positive, negative or neutral betas. This new approach is applied on a sample of hedge funds whose managers are likely to exhibit market timing skills. We stick to funds that post weekly returns, and analyze three hedge funds strategies in particular: long-short equity, managed futures, and funds of hedge funds. We analyze a particular period during which the managers of these funds are likely to magnify their presumed skills, namely around the financial and banking crisis of 2008. Some funds adopt a positive convexity as a response to the US market index, while others have a concave sensitivity to the returns of an emerging market index. Thus, we identify “positive”, “mixed” and “negative” market timers. A number of signs indicate that only positive market timers manage to acquire options below their cost, and deliver economic significant performance, even in the midst of the financial crisis. Negative market timers, by contrast, behave as if they were forced to sell options without getting the associated premium. We interpret this behavior as a possible result of fire sales, leading them to liquidate positions under the pressure of redemption orders, and inducing negative performance adjusted for market timing

The pricing of swaptions and caps under the Gaussian model of interest rate

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