On option pricing models in the presence of heavy tails

We propose a modification of the option pricing framework derived by Borland which removes the possibilities for arbitrage within this framework. It turns out that such arbitrage possibilities arise due to an incorrect derivation of the martingale transformation in the non-Gaussian option models which are used in that paper. We show how a similar model can be built for the asset price processes which excludes arbitrage. However, the correction causes the pricing formulas to be less explicit than the ones in the original formulation, since the stock price itself is no longer a Markov process. Practical option pricing algorithms will therefore have to resort to Monte Carlo methods or partial differential equations and we show how these can be implemented. An extra parameter, which needs to be specified before the model can be used, will give market makers some extra freedom when fitting their model to market data.     

Implicit expectiles and measures of implied volatility

It is well known that the normal distribution is inadequate in capturing the skewed and heavy-tailed behaviour of exchange rate returns. To this end, various flexible distributions that are capable of modelling the asymmetric and tailed behaviour of returns have been proposed. In this paper, we investigate the performance of the generalized lambda distribution (GLD) to capture the skewed and leptokurtic behaviour of exchange rate returns. We do this by conducting a comprehensive numerical study to compare the performance of the GLD against the performances of the skewed t distribution, the unbounded Johnson family of distributions and the normal inverse Gaussian (NIG) distribution. Our results suggest that in terms of the value-at-risk and expected shortfall, the GLD shows at least similar performance to the skewed t distribution and the NIG distribution. Considering the ease in GLD’s use for random variate generation in Monte Carlo simulations, we conclude that the GLD can be a good alternative in various financial applications where modelling of the heavy tail behaviour is critical.     

The impact of the choice of VaR models on the level of regulatory capital according to Basel II

The Basel II framework allows the calculation of the capital requirements for market risk with Value-at-Risk models. Since no special model is prescribed in the framework, banks may use simple models with questionable assumptions concerning their underlying distributions. Our numerical analysis reveals that simple VaR models that perform noticeably worse than comparable simple models with more realistic assumptions may lead to a lower level of regulatory capital for banks. For this reason, banks have a major incentive to implement bad models. This is obviously contrary to the interests of regulatory authorities.     

山西省综合配套试验改革背景下科技人才需求预测研究

依据山西省科技人才相关资料和数据,在分析科技人才队伍建设现状的基础上,运用GM(1,1)灰色预测模型、多元回归预测模型和趋势外推法,对山西省2011-2015年科技人才需求总量和需求结构进行预测,然后运用组合预测法对预测结果进行修正,并对预测结果进行分析,以便为山西省人才规划与决策提供科学的理论参考。     

Valuación de un producto estructurado de compra sobre el SX5E cuando la incertidumbre de los rendimientos está modelada con procesos log-estables

We report the participation level, we pricing a first generation's European call options on the Eurostoxx structured product, when returns’ uncertainty is modeled by log-stable processes, we present the basic statistics of the index's returns, we estimate the α-estable parameters, and we compare the structured products pricing by the both log-stable and log-Gaussian models using inputs of the debt markets. We conclude that investors get higher returns than debt markets using both models and returns’ differences depend of the participation level and the maturity.     

L‐moments skewness and kurtosis as measures of regional convergence and cohesion

The paper deals with the statistical modeling of convergence and cohesion over time with the use of kurtosis, skewness and L‐moments. Changes in the shape of the distribution related to the spatial allocation of socio‐economic phenomena are considered as an evidence of global shift, divergence or convergence. Cross‐sectional time‐series statistical modeling of variables of interest is to overpass the minors of econometric theoretical models of convergence and cohesion determinants. L‐moments perform much more stable and interpretable than classical measures. Empirical evidence of panel data proves that one pure pattern (global shift, polarization or cohesion) rarely exists and joint analysis is required.     

Moments expansion densities for quantifying financial risk

We propose a novel semi-nonparametric distribution that is feasibly parameterized to represent the non-Gaussianities of the asset return distributions. Our Moments Expansion (ME) density presents gains in simplicity attributable to its innovative polynomials, which are defined by the difference between the nth power of the random variable and the nth moment of the density used as the basis. We show that the Gram–Charlier distribution is a particular case of the ME-type of densities. The latter being more tractable and easier to implement when quadratic transformations are used to ensure positiveness. In an empirical application to asset returns, the ME model outperforms both standard and non-Gaussian GARCH models along several risk forecasting dimensions.     

Tail-risk protection trading strategies

Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.     

Effect of the fitting room environment on older clothing shoppers

Given the age-related physical challenges and social needs of older consumers, a randomized field experiment with 72 females aged 65+ was conducted using fitting room areas of two stores that varied in levels of Universal Design features and service. Based on ANCOVA, the fitting room area accommodation level had a significant effect on physical environment and social environment psychic costs and shopping satisfaction. Multiple regression confirmed the interaction effect of competence level and accommodation level on psychic costs and that psychic costs affected shopping satisfaction. Satisfaction influenced patronage intentions. The importance of the fitting room area should not be underestimated.     

Going public in the 1980s: Evidence from Sweden

The paper explores the going public decision in a sample of family-owned corporations in Sweden, 1970–1991. the issuers' motivations for going public are documented and contrasted with economic theory. We find that the average firm is old, that a significant portion of the shares are sold by existing shareholders, that most going public activity took place after an exceptionally sharp stock price increase, and that going public activity is not related to the business cycle. the findings suggest that firms were taken public by their owners who wanted to liquidate their investment to finance consumption or portfolio diversification. the findings strike the common view that firms go public to finance growth. Data from other European countries exhibit similar patterns and suggest that our findings for Sweden may extend to other markets as well.