Scope for Credit Risk Diversification

This paper considers a simple model of credit risk and derives the limit distribution of losses under different assumptions regarding the structure of systematic risk and the nature of exposure or firm heterogeneity. We derive fat-tailed correlated loss distributions arising from Gaussian risk factors and explore the potential for risk diversification. Where possible the results are generalised to non-Gaussian distributions. The theoretical results indicate that if the firm parameters are heterogeneous but come from a common distribution, for sufficiently large portfolios there is no scope for further risk reduction through active portfolio management. However, if the firm parameters come from different distributions, then further risk reduction is possible by changing the portfolio weights. In either case, neglecting parameter heterogeneity can lead to underestimation of expected losses. But, once expected losses are controlled for, neglecting parameter heterogeneity can lead to overestimation of risk, whether measured by unexpected loss or value-at-risk

Firm Heterogeneity and Credit Risk Diversification

This paper considers a simple model of credit risk and derives the limit distribution of losses under different assumptions regarding the structure of systematic and idiosyncratic risks and the nature of firm heterogeneity. The theoretical results obtained indicate that if firm-specific risk exposures (including their default thresholds) are heterogeneous but come from a common parameter distribution, for sufficiently large portfolios there is no scope for further risk reduction through active credit portfolio management. However, if the firm risk exposures are draws from different parameter distributions, say for different sectors or countries, then further risk reduction is possible, even asymptotically, by changing the portfolio weights. In either case, neglecting parameter heterogeneity can lead to underestimation of expected losses. But, once expected losses are controlled for, neglecting parameter heterogeneity can lead to overestimation of risk, whether measured by unexpected loss or value-at-risk. The theoretical results are confirmed empirically using returns and credit ratings for firms in the U.S. and Japan across seven sectors. Ignoring parameter heterogeneity results in far riskier credit portfolios.risk management, correlated defaults, heterogeneity, diversification, portfolio choice

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Recent Developments in Financial and Insurance Mathematics and the Interplay with the Industry

The workshop brought together leading experts from all over the world to exchange and discuss the latest developments in mathematical finance and actuarial mathematics. Researchers from the industry had the opportunity to circulate their problems among mathematicians. The participants gained from a fruitful interaction between mathematical methods and practitioner’s problems as well as from the interaction between finance and actuarial mathematics

Multi-scale Volatility in Option Pricing

This PhD thesis investigated the influence of kaolin and bentonite clays in the ore on flotation, filtration and centrifugal concentration. The results showed that the presence of particularly bentonite in the ore had a detrimental effect on flotation and filtration. The information generated from this work will advance our knowledge as well as provide important information for plant metallurgists. The project, therefore, is essential for the mineral industry that process clay-containing ores

Heuristic Strategies in Finance – An Overview

This paper presents a survey on the application of heuristic optimization techniques in the broad field of finance. Heuristic algorithms have been extensively used to tackle complex financial problems, which traditional optimization techniques cannot efficiently solve. Heuristic optimization techniques are suitable for non-linear and non-convex multi-objective optimization problems. Due to their stochastic features and their ability to iteratively update candidate solutions, heuristics can explore the entire search space and reliably approximate the global optimum. This overview reviews the main heuristic strategies and their application to portfolio selection, model estimation, model selection and financial clustering.finance, heuristic optimization techniques, portfolio management, model selection, model estimation, clustering