The financial crisis across the globe has prompted researchers to study the various aspects of credit in the system. In the first paper of this issue, “Vulnerability of Risk Management Systems in Credit Spread Widening Scenarios”, the author, Aldo Letizia, observes that during 2008, the sudden widening of credit spreads led to a rapid decrease in the value of many financial assets, revealing a general shortage of capital for many financial institutions, with some critical peaks that required fund injection and public bailouts. The evidence of a substantial underestimation of the risk related to a general credit spread widening leads the author to investigate the reason why risk management systems, in the early stage of the financial crisis, were not able to capture the accumulation of such a high potential of losses. Primarily, the author questions whether the most unexpected event was the magnitude of the spread widening or, rather, the extent of the price reaction to that factor. The paper mainly focuses on price reaction due to credit spread widening. In particular, it explores the possibility of including in the pricing techniques of financial instruments a treatment of expected losses that is aligned with the most common methodologies for credit risk evaluation. The refinement of the cash flow mapping techniques leads to detect how, in the phases of severe credit spread widening, modified duration could result in an inaccurate measurement of interest rate risk, but primarily it does not recognize the spread risk in the floater component of a portfolio.

A slight revision of the evaluation models allows identifying two specific sensitivity measures, to interest rate and credit spread changes, both functional to the improvement of risk management systems, in order to make them highly sensitive to the spread risk effect.

In the second paper, “Enhancements in CreditRisk+ Model”, the author, Satendramani Tiwari, suggests a parallel computing-based Monte-Carlo simulation, for a very large portfolio using the CreditRisk+ infrastructure, to calculate the portfolio loss distribution. According to Basel II guidelines, all financial institutions should have proper quantitative tools to manage and control the credit risk. CreditRisk+ model developed by Credit Suisse Financial Products is one of the most widely used models for calculating credit value-at-risk. CreditRisk+ is a good choice for many banks for calculating portfolio credit value-at-risk, because this model requires limited amount of inputs and is formulated with limited assumptions.

In the next paper, “Downside Risk of Derivative Portfolios with Mean-Reverting Underlyings”, the author, Patrick L Leoni, analyzes the sensitivity of the downside risk of a standard derivatives portfolio to a change in the mean-reversion level of its underlyings. From Monte-Carlo simulation, the author finds that the higher the intensity of mean-reversion, the lower the probability of reaching a predetermined loss level. This phenomenon appears to be more statistically significant in the case of large loss levels.

The author also observes that the higher the mean-reversion intensity of the underlyings, the longer the expected time to reach the given loss levels. The simulations suggest that selecting underlyings with high mean-reversion effect is a natural way of reducing the downside risk of the widely traded assets, without involving costly and restrictive managerial intervention.

In the last paper of this issue, “Can Technical Analysis Predict the Movement of Futures Prices?”, the authors, Noor Azlinna Azizan and Jacinta Chan Phooi M’ng, present a study of technical analysis trading rules that generate abnormal returns for futures prices. It reports abnormal returns over and above that generated by the passive buy-and-hold policy for FKLI, FCPO, Soyoil, Soybean and Corn futures for the periods tested. This research devises a new technical analysis trading model, Bollinger Bands Z-Test (BBZ), using standard deviation. It proposes that BBZ attempts to capture large price movements which happen beyond 1 standard deviation. The mechanical buy signal is above +1 standard deviation and the sell signal is below –1 standard deviation. The main conclusion is that trends exist in time series. This study demonstrates that, FKLI price changes are not random; some mechanical technical trading systems like moving average(s) and BBZ can outperform the passive buy-and-hold policy; and BBZ is a robust mechanical technical trading system that can be optimized to fit the time series for better performance.

The application of these findings is to program the algorithm and trading rules for BBZ into an algorithm trading system, that computes mechanically generated trading signals and executes the trades automatically. Algorithm trading programs are popularly employed by professional model trading desks of large financial institutions. These algorithms like BBZ are validated by quantitative analysts before being programmed into the algorithm trading system.

- - Nupur Hetamsaria
Consulting Editor