Implications of alternative operational risk modeling techniques

Quantification of operational risk has received increased attention with the inclusion of an explicit capital charge for operational risk under the new Basle proposal. The proposal provides significant flexibility for banks to use internal models to estimate their operational risk, and the associated capital needed for unexpected losses. Most banks have used variants of value at risk models that estimate frequency, severity, and loss distributions. This paper examines the empirical regularities in operational loss data. Using loss data from six large internationally active banking institutions, we find that loss data by event types are quite similar across institutions. Furthermore, our results are consistent with economic capital numbers disclosed by some large banks, and also with the results of studies modeling losses using publicly available “external” loss data.Bank capital ; Risk management ; Basel capital accord

Generative-Discriminative Complementary Learning

Majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and can generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data

Does "skin in the game" reduce risk taking? Leverage, liability and the long-run consequences of new deal financial reforms

We examine how the Banking Acts of the 1933 and 1935 and related New Deal legislation influenced risk taking in the financial sector of the U.S. economy. Our analysis focuses on contingent liability of bank owners for losses incurred by their firms and how the elimination of this liability influenced leverage and lending by commercial banks. Using a new panel data set that compares balance sheets of state and national banks, we find contingent liability reduced risk taking, particularly when coupled with rules requiring banks to join the Federal Deposit Insurance Corporation. Leverage ratios are higher in states with limited liability for bank owners. Banks in states with contingent liability converted each dollar of capital into fewer loans, and thus could sustain larger loan losses (as a fraction of their portfolio) than banks in limited liability states. The New Deal replaced a regime of contingent liability with stricter balance sheet regulation and increased capital requirements, shifting the onus of risk management from banks to state and federal regulators. By separating investment banks from commercial banks, the Glass-Steagall Act left investment banks to manage their own leverage, a feature of financial regulation that, in part, depended on their partnership structur

Behavioral Finance

Behavioral finance as a subdiscipline of behavioral economics is finance incorporating findings from psychology and sociology into its theories. Behavioral finance models are usually developed to explain investor behavior or market anomalies when rational models provide no sufficient explanations. To understand the research agenda, methodology, and contributions, this survey reviews traditional finance theory first. Then, this survey shows how modifications (e.g. incorporating market frictions) can rationally explain observed individual or market behavior. In the second section, the survey will explain the behavioral finance research methodology -how biases are modeled, incorporated into traditional finance theories, and tested empirically and experimentally- using one specific subset of the behavioral finance literature, the overconfidence literature.

Modeling catastrophe claims with left-truncated severity distributions (extended version)

In this paper, we present a procedure for consistent estimation of the severity and frequency distributions based on incomplete insurance data and demonstrate that ignoring the thresholds leads to a serious underestimation of the ruin probabilities. The event frequency is modelled with a non-homogeneous Poisson process with a sinusoidal intensity rate function. The choice of an adequate loss distribution is conducted via the in-sample goodness-of-fit procedures and forecasting, using classical and robust methodologies.Natural catastrophe; Property insurance; Loss distribution; Truncated data; Ruin probability;

Is Area Yield Insurance Competitive with Farm Yield Insurance?

This article compares risk reduction from MPCI and GRP crop insurance contracts. The analysis extends and improves on the existing area-yield insurance literature in four important respects. First, the geographical scope greatly exceeds that of previous work. Second, unlike previous efforts, the area is not assumed to consist only of those farms included in the analysis. Third, the analysis is based on the actual GRP indemnity function rather than the area-yield indemnity function commonly used in the literature. Fourth, the analysis avoids the questionable assumption that GRP scale can be optimized at the individual farm level. Even with a number of conservative assumptions favoring MPCI relative to GRP, results indicate that at least for some crops and regions GRP is aviable alternative to MPCI.area yield insurance, Multiple Peril Crop Insurance, risk reduction, Risk and Uncertainty,