The stability of estimates is critical when applying advanced measurement approaches (AMA) such as loss distribution approach (LDA) for operational risk capital modeling. Recent studies have identified issues associated with capital estimates by applying the maximum likelihood estimation (MLE) method for truncated distributions: significant upward mean-bias, considerable uncertainty about the estimates, and non-robustness to both small and large losses. Although alternative estimation approaches have been proposed, there has not been any comprehensive study of how alternative approaches perform compared to the MLE method. This paper is the first comprehensive study on the performance of various potentially promising alternative approaches (including minimum distance approach, quantile distance approach, scaling-based bias correction, upward scaling of lower quantiles, and right-truncated distributions) as compared to MLE with regards to accuracy, precision and robustness. More importantly, based on the properties of each estimator, we propose a right-truncation with probability weighted least squares method, by combining the right-truncated distribution and minimizing a probability weighted distance (i.e., the quadratic upper-tail Anderson–Darling distance), and we find it significantly reduces the bias and volatility of capital estimates and improves the robustness of capital estimates to small losses near the threshold or moving the threshold, demonstrated by both simulation results and real data application.

Recent research shows that unsuccessful tender offers may affect target share returns for two years past the offer's announcement. This note examines target returns in the interim between the announcement and one year after the offer's withdrawal. Analyzing a recent sample of targets that did not get another bid in the year following a failed tender offer, this study reaches two conclusions. First, all of an offer's premium disappears by the time failure becomes public. Second, excess returns are zero in the post‐failure year. An explanation that is based on the causes of the tender offers' failures is presented.

Recent research shows that unsuccessful tender offers may affect target share returns for two years past the offer's announcement. This note examines target returns in the interim between the announcement and one year after the offer's withdrawal. Analyzing a recent sample of targets that did not get another bid in the year following a failed tender offer, this study reaches two conclusions. First, all of an offer's premium disappears by the time failure becomes public. Second, excess returns are zero in the post-failure year. An explanation that is based on the causes of the tender offers' failures is presented.

Statistical arbitrage strategies are typically based on models of returns. We introduce a new statistical arbitrage strategy based on dynamic factor models of prices. Our objective in this paper is to exploit the mean-reverting properties of prices reported in the literature. We do so because, to capture the same information using a return-based factor model, a much larger number of lags would be needed, leading to inaccurate parameter estimation. To empirically test the relative performance of return-based and price-based models, we construct portfolios (long-short, long-only, and equally weighted) based on the forecasts generated by two dynamic factor models. Using the stock of companies included in the S&P 500 index for constructing portfolios, the empirical analysis statistically tests the relative forecasting performance using the Diebold–Mariano framework and performing the test for statistical arbitrage proposed by Hogan et al. (2004). Our results show that prices allow for significantly more accurate forecasts than returns and pass the test for statistical arbitrage. We attribute this finding to the mean-reverting properties of stock prices. The high level of forecasting accuracy using price-based factor models has important theoretical and practical implications.

1. Introduction. 2. Pricing of Bonds. 3. Measuring Yield. 4. Bond Price Volatility. 5. Factors Affecting Bond Yields and the Term Structure of Interest Rates. 6. Treasury and Agency Securities Markets. 7. Corporate Debt Instruments. 8. Municipal Securities. 9. Non-U.S. Bonds. 10. Residential Mortgage Loans. 11. Mortgage Pass-Through Securities. 12. Collateralized Mortgage Obligations and Stripped Mortgage-Backed Securities. 13. Commercial Mortgage-Backed Securities. 14. Asset-Backed Securities. 15. Collateralized Debt Obligations. 16. Analysis of Bonds with Embedded Options. 17. Analysis of Residential Mortgage-Backed Securities. 18. Analysis of Convertible Bonds. 19. Active Bond Portfolio Management Strategies. 20. Indexing. 21. Liability Funding Strategies. 22. Bond Performance Measurement and Evaluation. 23. Interest-Rate Futures Contracts. 24. Interest-Rate Options. 25. Interest-Rate Swaps and Agreements. 26. Credit Derivatives. Index.

Frank J. Fabozzi, Eileen Moran, Christopher K. Ma, Market Uncertainty and the Least-Cost Offering Method of Public Utility Debt: A Note, The Journal of Finance, Vol. 43, No. 4 (Sep., 1988), pp. 1025-1034