Collateral-Enhanced Default Risk

Changes in collateralization have been implicated in significant default (or near-default) events during the financial crisis, most notably with AIG. We have developed a framework for quantifying this effect based on moving between Merton-type and Black-Cox-type structural default models. Our framework leads to a single equation that emcompasses the range of possibilities, including collateralization remargining frequency (i.e. discrete observations). We show that increases in collateralization, by exposing entities to daily mark-to-market volatility, enhance default probability. This quantifies the well-known problem with collateral triggers. Furthermore our model can be used to quantify the degree to which central counterparties, whilst removing credit risk transmission, systematically increase default risk.Comment: 12 pages; 5 figure

Markov-Switching GARCH Modelling of Value-at-RisK

This paper proposes an asymmetric Markov regime-switching (MS) GARCH model to estimate value-at-risk (VaR) for both long and short positions. This model improves on existing VaR methods by taking into account both regime change and skewness or leverage effects. The performance of our MS model and single-regime models is compared through an innovative backtesting procedure using daily data for UK and US market stock indices. The findings from exceptions and regulatory-based tests indicate the MS-GARCH specifications clearly outperform other models in estimating the VaR for both long and short FTSE positions and also do quite well for S&P positions. We conclude that ignoring skewness and regime changes has the effect of imposing larger than necessary conservative capital requirements

How volatilities nonlocal in time affect the price dynamics in complex financial systems

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.Comment: 16 pages, 7 figure

The effect of realised volatility on stock returns risk estimates

In this paper, we estimate minimum capital risk requirements for short, long positions and three investment horizons, using the traditional GARCH model and two other GARCH-type models that incorporate the possibility of asymmetric responses of volatility to price changes; and, most importantly, we analyse the models performance when realised volatility is included as an explanatory variable into the models' variance equations. The results suggest that the inclusion of realised volatility improves the models forecastability and their capacity to calculate accurate measures of minimum capital risk requirements

Risk Management of Precious Metals

This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested, and the best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs. The risk-minimizing portfolio weights and dynamic hedge ratios between different metal groups are also analyzed.Precious metals; conditional volatility; risk management; value-at-risk

Risk management of precious metals

This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested, and the best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs. The risk-minimizing portfolio weights and dynamic hedge ratios between different metal groups are also analyzed.risk management;value-at-risk;conditional volatility;precious metals

Exploiting a Goal-Decomposition Technique to Prioritize Non-functional Requirements

Business stakeholders need to have clear and realistic goals if they want to meet commitments in application development. As a consequence, at early stages they prioritize requirements. However, requirements do change. The effect of change forces the stakeholders to balance alternatives and reprioritize requirements accordingly. In this paper we discuss the problem of priorities to non-functional requirements subjected to change. We, then, propose an approach to help smooth the impact of such changes. Our approach favors the translation of nonoperational specifications into operational definitions that can be evaluated once the system is developed. It uses the goal-question-metric method as the major support to decompose non-operational specifications into operational ones. We claim that the effort invested in operationalizing NFRs helps dealing with changing requirements during system development. Based on\ud this transformation and in our experience, we provide guidelines to prioritize volatile non-functional requirements