A Multi-Criteria Portfolio Analysis of Hedge Fund Strategies

This paper features a tri-criteria analysis of Eurekahedge fund data strategy index data. We use nine Eurekahedge equally weighted main strategy indices for the portfolio analysis. The tri-criteria analysis features three objectives: return, risk and dispersion of risk objectives in a Multi-Criteria Optimisation (MCO) portfolio analysis. We vary the MCO return and risk targets and contrast the results with four more standard portfolio optimisation criteria, namely the tangency portfolio (MSR), the most diversi_ed portfolio (MDP), the global minimum variance portfolio (GMW), and portfolios based on minimising expected shortfall (ERC). Backtests of the chosen portfolios for this hedge fund data set indicate that the use of MCO is accompanied by uncertainty about the a priori choice of optimal parameter settings for the decision criteria. The empirical results do not appear to outperform more standard bi-criteria portfolio analyses in the backtests undertaken on our hedge fund index data

Phenomenology and diagnoses associated with psychogenic non-epileptic seizures

Introduction: Psychogenic non-epileptic seizures (PNES) are seizure resembling behavior without electrical correlates insidethe brain, often having a psychogenic etiology. However, there is a paucity of research into the phenomenology, and hence,there is frequent diagnostic dilemma. The phenomenology appears to be spread across multiple diagnostic categories in nonspecificmanner. The study aimed at finding the phenomenology and the other diagnoses associated with PNES. Materials andMethods: 50 consecutive patients presenting to the tertiary psychiatric center of Eastern India were enquired on semi-structuredproforma to assess the phenomenology (including the antecedent events or stressors, ictal details, and the post-ictal stage) andassociated diagnosis. Appropriate diagnostic tools were used to verify the associated diagnoses. Results: The patients of thissample mostly belonged mostly to low socioeconomic group, females from second decade who were unemployed and had poorsocioeconomic support. They mostly had ongoing stressors, were mute during spells showed non-stereotyped movements of bodyparts. Most of them also had other Axis I and Axis II diagnosis of which depression, anxiety, and personality disorder were common.Conclusion: PNES is a non-specific expression of various underlying psychopathologies, because it is seen most often with otherindependently diagnosable psychiatric conditions

Value at Risk Estimation Using Extreme Value Theory

A common assumption in quantitative financial risk modelling is the distributional assumption of normality in the asset’s return series, which makes modelling easy but proves to be inefficient if the data exhibit extreme tails. When dealing with extreme financial events like the Global Financial Crisis of 2007-2008 while quantifying extreme market risk, Extreme Value Theory (EVT) proves to be a natural statistical modelling technique of interest. Extreme Value Theory provides well established statistical models for the computation of extreme risk measures like the Return Level, Value at Risk and Expected Shortfall. In this paper we apply Univariate Extreme Value Theory to model extreme market risk for the ASX-All Ordinaries (Australian) index and the S&P-500 (USA) Index. We demonstrate that EVT can be successfully applied to Australian stock market return series for predicting next day VaR by using a GARCH(1,1) based dynamic EVT approach. We also show with backtesting results that EVT based method outperforms GARCH(1,1) and RiskMetricsTM based forecasts

Extreme Market Risk - An Extreme Value Theory Approach

The phenomenon of the occurrence of rare yet extreme events, “Black Swans” in Taleb’s terminology, seems to be more apparent in financial markets around the globe. This means there is not only a need to design proper risk modelling techniques which can predict the probability of risky events in normal market conditions but also a requirement for tools which can assess the probabilities of rare financial events; like the recent Global Financial Crisis (2007-2008). An obvious candidate, when dealing with extreme financial events and the quantification of extreme market risk is Extreme Value Theory (EVT). This proves to be a natural statistical modelling technique of relevance. Extreme Value Theory provides well established statistical models for the computation of extreme risk measures like the Return Level, Value at Risk and Expected Shortfall. In this paper we apply Univariate Extreme Value Theory to model extreme market risk for the ASX-All Ordinaries (Australian) index and the S&P-500 (USA) Index. We demonstrate that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and expected Return Level and also daily VaR using a GARCH(1,1) and EVT based dynamic approach

Evaluating Extremal Dependence in Stock Markets Using Extreme Value Theory

Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst others. Extreme Value Theory (EVT) that provides well established methods for univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. This paper uses nonparametric measures based on bivariate EVT to investigate asymptotic dependence and estimate the degree of tail dependence of the ASX-All Ordinaries daily returns with four other international markets, viz., the S&P-500, Nikkei-225, DAX-30 and Heng-Seng for both right and left tails of the return distribution in extreme quantiles. It is investigated whether the asymptotic dependence between these markets is related to the heteroskedasticity present in the logarithmic return series using GARCH filters. The empirical evidence from bivariate EVT methods show that the asymptotic dependence between the extreme tails of the stock markets does not necessarily exist and rather can be associated with the heteroskedasticity present in the financial time series of the various stock markets

Bank Risk: Does Size Matter?

The size of banks is examined as a determinant of bank risk. A wide range of banks are examined across four regions, including Australia, Canada, Europe and the USA. Four risk metrics are considered including Value at Risk (VaR), Conditional Value at Risk (CVaR, which measures risk beyond VaR), Probability of Default (PD) using Merton structural methodology, and Conditional Probability of Default (CPD, the author’s own model which measures risk based on extreme asset value fluctuations. Daily equity and asset value fluctuations are included in the analysis, including pre-GFC and GFC periods. In addition to examining size in isolation as a determinant of bank risk, the paper uses fixed effects panel data regression to examine the significance of size as a risk determinant in conjunction with a range of other independent variables. The study finds mixed results among the four regions with no conclusive evidence of significant association between size and risk

Prospective of Essential Oils of the Genus Mentha as Biopesticides: A Review

Mentha is a genus from the family Lamiaceae, whose essential oils has long been used in various forms such as in management of plant pathogens and insect pests, in traditional medicine as well as in culinary and cosmetics. Its major chemical components such as menthol, carvone have now been successfully commercialized in the industry as antimicrobials/insecticidal agents. Current review focuses on chemical composition of essential oils of some Mentha species from different geographical regions with their insecticidal (repellent, antifeedant, and ovicidal) and antimicrobial efficacies against bacterial, fungal plant pathogens and insects of stored products. Reports of the researchers on chemical analysis of essential oils of Mentha species revealed that most of the oils being rich in pulegone, menthon, menthol, carvone, 1, 8-cineole, limonene and β-caryophyllene. Reviewed literature revealed that, essential oils from different Mentha species possess potential antimicrobial activity against plant pathogens and have insecticidal activity against stored product insects. Thus, antimicrobial and insecticidal properties of essential oils of Mentha species offer the prospect of using them as natural pesticides with a commercial value, having social acceptance due to its sustainability and being environment friendly