Now showing 1 - 10 of 18
- PublicationExtreme spectral risk measures : an application to futures clearinghouse margin requirementsThis paper applies the Extreme-Value (EV) Generalised Pareto distribution to the extreme tails of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses tail estimators from these contracts to estimate spectral risk measures, which are coherent risk measures that reflect a user’s risk-aversion function. It compares these to VaR and Expected Shortfall (ES) risk measures, and compares the precision of their estimators. It also discusses the usefulness of these risk measures in the context of clearinghouses setting initial margin requirements, and compares these to the SPAN measures typically used.
- PublicationFinancial risks and the Pension Protection Fund : can it survive them?This paper discusses the financial risks faced by the UK Pension Protection Fund (PPF) and what, if anything, it can do about them. It draws lessons from the regulatory regimes under which other financial institutions, such as banks and insurance companies, operate and asks why pension funds are treated differently. It also reviews the experience with other government-sponsored insurance schemes, such as the US Pension Benefit Guaranty Corporation, upon which the PPF is modelled. We conclude that the PPF will live under the permanent risk of insolvency as a consequence of the moral hazard, adverse selection, and, especially, systemic risks that it faces.
- PublicationSpectral risk measures and the choice of risk aversion functiorSpectral risk measures are attractive risk measures as they allow the user to obtain risk measures that reflect their risk-aversion functions. To date there has been very little guidance on the choice of risk-aversion functions underlying spectral risk measures. This paper addresses this issue by examining two popular risk aversion functions, based on exponential and power utility functions respectively. We find that the former yields spectral risk measures with nice intuitiveproperties, but the latter yields spectral risk measures that can have perverse properties. More work therefore needs to be done before we can be sure that arbitrary but respectable utility functions will always yield ‘well-behaved’ spectral risk measures.
- PublicationSpectral risk measures : properties and limitationsSpectral risk measures (SRMs) are risk measures that take account of user risk aversion, but to date there has been little guidance on the choice of utility function underlying them. This paper addresses this issue by examining alternative approaches based on exponential and power utility functions. A number of problems are identified with both types of spectral risk measure. The general lesson is that users of spectral risk measures must be careful to select utility functions that fit the features of the particular problems they are dealing with, and should be especially careful when using power SRMs.
- PublicationEstimating financial risk measures for futures positions : a non-parametric approachThis paper presents non-parametric estimates of spectral risk measures applied to long and short positions in 5 prominent equity futures contracts. It also compares these to estimates of two popular alternative measures, the Value-at-Risk (VaR) and Expected Shortfall (ES). The spectral risk measures are conditioned on the coefficient of absolute risk aversion, and the latter two are conditioned on the confidence level. Our findings indicate that all risk measures increase dramatically and their estimators deteriorate in precision when their respective conditioning parameter increases. Results also suggest that estimates of spectral risk measures and their precision levels are of comparable orders of magnitude as those of more conventional risk measures.
- PublicationEvaluating the precision of estimators of quantile-based risk measuresThis paper examines the precision of estimators of Quantile-Based Risk Measures (Value at Risk, Expected Shortfall, Spectral Risk Measures). It first addresses the question of how to estimate the precision of these estimators, and proposes a Monte Carlo method that is free of some of the limitations of existing approaches. It then investigates the distribution of risk estimators, and presents simulation results suggesting that the common practice of relying on asymptotic normality results might be unreliable with the sample sizes commonly available to them. Finally, it investigates the relationship between the precision of different risk estimators and the distribution of underlying losses (or returns), and yields a number of useful conclusions.
- PublicationU.S. core inflation : a wavelet analysisThis paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation. Results suggest that wavelet-based measures perform better, and sometimes much better, than the Traditional approaches. These results suggest that wavelet methods are a promising avenue for future research on core inflation.
- PublicationExponential spectral risk measuresSpectral risk measures are attractive risk measures as they allow the user to obtain risk measures that reflect their subjective risk-aversion. This paper examines spectral risk measures based on an exponential utility function, and finds that these risk measures have nice intuitive properties. It also discusses how they can be estimated using numerical quadrature methods, and how confidence intervals for them can be estimated using a parametric bootstrap. Illustrative results suggest that estimated exponential spectral risk measures obtained using such methods are quite precise in the presence of normally distributed losses.
- PublicationExtreme measures of agricultural financial riskRisk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value at Risk (VaR), Expected Shortfall (ES) and Spectral Risk Measures (SRMs). We use Extreme Value Theory (EVT) to model the tail returns and present results for these three different risk measures using agricultural futures market data. We compare the estimated risk measures in terms of their size and precision, and find that they are all considerably higher than normal estimates; they are also quite uncertain, and become more uncertain as the risks involved become more extreme.