An intertemporal CAPM with higher-order moments

We propose an intertemporal asset pricing model that incorporates both preference for higher-order moments and stochastic investment opportunities and encompasses a wide range of existing models. We provide supporting evidence from the U.S. stock market and find that, not only is systematic skewness negatively priced, an extra return premium is also required for accepting high systematic risk associated with a rise in risk aversion. Our findings suggest that considering both skewness preference and intertemporal hedging demands improves the estimated risk-return trade-off, and that cross-sectional anomalies such as value, momentum, and failure probability puzzles can be partially explained by our model.     

Regional variation in productivity: a study of the Danish economy

We develop a model of labor productivity as a combination of capital-labour ratio, vintage of capital stock, regional externalities, and total factor productivity (TFP). The skewness of TFP distribution is related to different growth theories. While negative skewness is consistent with the neo-Schumpeterian idea of catching up with leaders, zero skewness supports the neoclassical view that deviations from the frontier reflect only idiosyncratic productivity shocks. We argue that positive skewness is consistent with an economy where exogenous technology is combined with non-transferable knowledge accumulated in specific sectors and regions. This argument provides the framework for an empirical model based on stochastic frontier analysis. The model is used to analyse regional and sectoral inequalities in Denmark.

On the monitoring of multi-attributes high-quality production processes

Over the last decade, there have been an increasing interest in the techniques of process monitoring of high-quality processes. Based upon the cumulative counts of conforming (CCC) items, Geometric distribution is particularly useful in these cases. Nonetheless, in some processes the number of one or more types of defects on a nonconforming observation is also of great importance and must be monitored simultaneously. However, there usually exist some correlations between these two measures, which obligate the use of multi-attribute process monitoring. In the literature, by assuming independence between the two measures and for the cases in which there is only one type of defect in nonconforming items, the generalized Poisson distribution is proposed to model such a problem and the simultaneous use of two separate control charts (CCC & C chats) is recommended. In this paper, we propose a new methodology to monitor multi-attribute high-quality processes in which not only there exist more than one type of defects on the observed nonconforming item but also there is a dependence structure between the two measures. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewnesses. Then, we estimate the transformed mean vector and covariance matrix and apply the well-known χ² control chart. In order to illustrate the proposed method and evaluate its performance, we use two numerical examples by simulation and compare the results. The results of the simulation studies are encouraging.     

Implied volatility skews and stock return skewness and kurtosis implied by stock option prices

The Black-Scholes* option pricing model is commonly applied to value a wide range of option contracts. However, the model often inconsistently prices deep in-the-money and deep out-of-the-money options. Options professionals refer to this well-known phenomenon as a volatility ‘skew’ or ‘smile’. In this paper, we examine an extension of the Black-Scholes model developed by Corrado and Su that suggests skewness and kurtosis in the option-implied distributions of stock returns as the source of volatility skews. Adapting their methodology, we estimate option-implied coefficients of skewness and kurtosis for four actively traded stock options. We find significantly nonnormal skewness and kurtosis in the option-implied distributions of stock returns.     

Hedge fund return,volatility asymmetry,and systemic effects: A higher-moment factor-EGARCH model

We investigate whether: (i) co-skewness and co-kurtosis are significant factors in modeling hedge fund (HF) returns, (ii) HF return volatility displays clusters, asymmetry and shock persistence, (iii) volatility clusters of HF styles drive volatility clusters of one another, major asset classes, and major banking organizations, (iv) HF return and volatility patterns changed after the financial crises of 1998 and 2007–2009. A higher-moment EGARCH model and monthly data over January 1993–April 2014 period on 13 HF styles are employed. Out-of-sample forecasts are generated over the period of May 2014–April 2016. Results show: (i) most of the co-skewness and co-kurtosis coefficients are statistically significant, strongly supporting the higher-moment return generating models; (ii) there is strong evidence in favor of EGARCH specification, volatility clustering, asymmetry, and shock persistence; (iii) there were distinct effects on the returns and volatilities of HFs during the 1998 Russian bond crisis, Long-Term Capital Management crisis, and the 2007–2009 financial crisis; and (iv) shocks to volatility clusters of a HF style do spillover to other HF styles, major banking firms, and key asset classes. Our findings have major implications for regulators, investors, HF managers and hedging strategists.     

市场摩擦对特质风险溢价的影响效应——基于A股主板市场的实证分析

与国外发达市场相比,我国A股主板市场的市场摩擦因素对市场微观结构和资产定价的影响更大。在防范和化解系统性风险的过程中,进一步分析市场摩擦如何作用于特质风险定价效应的问题具有重要的理论和现实意义。本文通过采用多维市场摩擦指标来代理信息不对称、交易成本、买卖限制、卖空限制、风险对冲和外部冲击,检验中国股市特质风险和预期收益率的关系,并判断出市场摩擦因素间的差异性影响机制。回归发现,市场摩擦和特质风险因子(特质波动率和特质偏度)都具有定价效应。各维度市场摩擦因素降低了股票流动性,进而增强了特质波动率的负向定价效应,部分解释了“特质波动率之谜”,但市场摩擦对特质偏度因子溢价的影响较为微弱。同时,基于特质波动率和特质偏度因子的投资策略能够产生超越CAPM、三因子和五因子模型的绝对收益,并印证了市场摩擦对特质风险因子绝对收益的影响作用。