基于时变Copula的中外石油市场风险联动性分析

采用了3种不同类型的时变Copula函数模型,对三大石油市场Brent(北大西洋布伦特原油),WTI(美国西德克萨斯轻质原油),Daq(大庆原油价格指数)之间的价格相关性进行分析。通过Egarch-t模型构建描述三大石油市场收益率的边缘分布模型,然后分别用两种不同的时变Copula模型对WTI-Daq,Brent-Daq两个相关市场之间的相依结构进行建模,刻画了国内外石油市场之间的风险相依结构。实证结果表明:与时变Clayton-Copula相比,时变SJC-Copula能够有效捕捉市场间的上下尾相依结构关系,且国外石油市场与国内石油市场间的下尾相依关系要明显强于上尾相依关系,尤其是北大西洋布伦特原油(Brent)价格波动对于我国石油市场价格影响显著。     

政府贫困治理的多元逻辑与精准扶贫的逻辑弥合

政府主导是中国贫困治理的重要经验和基本特征,理解精准扶贫实践中的偏离现象需要从政府贫困治理的逻辑入手。本文研究了从中央到地方的多层级政府在贫困治理当中遵循的主导行动逻辑及其在贫困治理中的主要表现。研究发现,不同层级间政府拥有差异化的贫困治理逻辑,其中中央与省级政府在贫困治理当中主要遵循政治逻辑,地市级地方政府主要遵循行政逻辑,而县乡(镇)地方政府主要遵循治理逻辑,不同的贫困治理逻辑在实践中形成紧张关系甚至是带来目标冲突,进而导致精准扶贫实践的偏离。因此,解决精准扶贫的实践偏离问题,本文认为需要系统地弥合多层级政府贫困治理的逻辑张力,形成责权利深度融合的贫困治理机制,强化对基层政府贫困治理逻辑的支持,避免行政逻辑对贫困治理逻辑的不当干预,以此形成政府贫困治理的强大合力,推动脱贫攻坚任务的早日完成。     

Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction

Multivariate count time series models are an important tool for analyzing and predicting the spread of infectious disease. We consider the endemic-epidemic framework, a class of autoregressive models for infectious disease surveillance counts, and replace the default autoregression on counts from the previous time period with more flexible weighting schemes inspired by discrete-time serial interval distributions. We employ three different parametric formulations, each with an additional unknown weighting parameter estimated via a profile likelihood approach, and compare them to an unrestricted nonparametric approach. The new methods are illustrated in a univariate analysis of dengue fever incidence in San Juan, Puerto Rico, and a spatiotemporal study of viral gastroenteritis in the 12 districts of Berlin. We assess the predictive performance of the suggested models and several reference models at various forecast horizons. In both applications, the performance of the endemic-epidemic models is considerably improved by the proposed weighting schemes.     

A measurement of affluence and poverty interdependence across countries: Evidence from the application of tail copula

This paper examines affluence and poverty interdependence across 185 countries and its evolution over 1969–2014. To estimate affluence and poverty interdependence and derive tail interdependences the tail copulae are applied to multivariate density function. The tail coefficients are estimated in the non-parametric way as in Schmidt and Stadtmüller (2006). The estimates show, that poverty is less interdependent and continue to decrease, while affluence has asymptotically high global dependence, meaning a higher global dependence on and sensitivity to the well-being of the affluent countries. The received results derive the pattern of the extreme interdependence and can help to identify poverty and affluence spill-over across countries and regions and calculate the average sensitivity of a country to these phenomena on the global level and can potentially help in poverty reduction strategies within the Sustainable Development Goal by the United Nations.     

Forecasting stock returns: Do less powerful predictors help?

This paper proposes a simple but efficient way to improve the predictability of stock returns. Instead of torturously constructing new powerful predictors, we readily select existing predictors that have low correlations and thus provide complementary information. Our forecasting strategy is to use the selected predictors based on a multivariate regression model. In our forecasting strategy, less powerful predictors are also useful for forecasting stock returns if they could provide complementary information. The empirical results show that our forecasting strategy outperforms not only the univariate regression models that use each predictor's information separately but also combination approaches that use all predictors jointly. We also document that our strategy extracts significantly more useful information from the complementary predictors than the competing models. In addition, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Furthermore, the evidence based on Monte Carlo simulations supports the feasibility of our forecasting strategy.     

A multivariate Markov chain stock model

ABSTRACT

We propose a dividend stock valuation model where multiple dividend growth series and their dependencies are modelled using a multivariate Markov chain. Our model advances existing Markov chain stock models. First, we determine assumptions that guarantee the finiteness of the price and risk as well as the fulfilment of transversality conditions. Then, we compute the first- and second-order price-dividend ratios by solving corresponding linear systems of equations and show that a different price-dividend ratio is attached to each combination of states of the dividend growth process of each stock. Subsequently, we provide a formula for the computation of the variances and covariances between stocks in a portfolio. Finally, we apply the theoretical model to the dividend series of three US stocks and perform comparisons with existing models. The results could also be applied for actuarial purposes as a general stochastic investment model and for calculating the initial endowment to fund a portfolio of dependent perpetuities.     

China's liberalizing stock market,crude oil,and safe-haven assets: A linkage study based on a novel multivariate wavelet-vine copula approach

China is the world's largest oil importer, and therefore the correlations between stock indices and highly volatile oil prices deserve close examination when investing in China's gradually liberalizing stock market. Another concern for international investors is whether safe-haven assets can reduce portfolio risks for investment in China. The paper makes two main contributions. First, we develop a novel method of examining a multivariate dependence structure by combining wavelet analysis with the vine copula model. Second, we apply the proposed methodology to study the correlations between China's liberalizing stock market, petroleum, and safe-haven assets at different frequencies. We find that the multidimensional dependence of these assets has been altered as a result of the 2008 global financial crisis. Moreover, the vine structures exhibit dependence patterns that vary over time horizons, indicating that the multidimensional dependence is sensitive to time scales.     

Forecasting market risk of portfolios: copula-Markov switching multifractal approach

This paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christoffersen [1998. “Evaluating Interval Forecasts.” International Economic Review 39: 841–862], the GMM duration-based test by Candelon et al. [2011. “Backtesting Value at Risk: A GMM Duration-based Test.” Journal of Financial Econometrics 9: 314–343] and the superior predictive ability (SPA) test by Hansen [2005. “A Test for Superior Predictive Ability.” Journal of Business and Economic Statistics 23, 365–380] we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance–covariance, RiskMetrics, copula-GARCH and constant conditional correlation GARCH (CCC-GARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction.     

Modeling cause-of-death mortality using hierarchical Archimedean copula

Studying changes in cause-specific (or competing risks) mortality rates may provide significant insights for the insurance business as well as the pension systems, as they provide more information than the aggregate mortality data. However, the forecasting of cause-specific mortality rates requires new tools to capture the dependence among the competing causes. This paper introduces a class of hierarchical Archimedean copula (HAC) models for cause-specific mortality data. The approach extends the standard Archimedean copula models by allowing for asymmetric dependence among competing risks, while preserving closed-form expressions for mortality forecasts. Moreover, the HAC model allows for a convenient analysis of the impact of hypothetical reduction, or elimination, of mortality of one or more causes on the life expectancy. Using US cohort mortality data, we analyze the historical mortality patterns of different causes of death, provide an explanation for the ‘failure’ of the War on Cancer, and evaluate the impact on life expectancy of hypothetical scenarios where cancer mortality is reduced or eliminated. We find that accounting for longevity improvement across cohorts can alter the results found in existing studies that are focused on one single cohort.     

Destinations strategic groups via Multivariate Competition-based IPA

This paper aims at developing a sound methodology for both extending Importance-Performance Analysis (IPA) to the consideration of many tourism destinations simultaneously, and defining the prioritization of core attributes in each. Multivariate Competition-based IPA (MCIPA) allows to provide detailed information of particular utility for destination management, through a synthetic representation, accounting also for the geographical and administrative context.The conceptual framework of strategic groups, applied for the first time to tourist destinations, is the interpretative key of MCIPA. It makes the comparison of importance and performance, of various attributes in many areas, feasible and synthetic. It leads to interpret combinations of importance values in terms of target segment's preferences, defining destinations with similar performances on the same target segment as direct rivals. Based on a set of very broadly applicable statistical techniques, MCIPA helps addressing some methodological and interpretative drawbacks of IPA. An application to Italian provinces is shown.