Estimating stochastic volatility models using integrated nested Laplace approximations

Volatility in financial time series is mainly analysed through two classes of models; the generalized autoregressive conditional heteroscedasticity (GARCH) models and the stochastic volatility (SV) ones. GARCH models are straightforward to estimate using maximum-likelihood techniques, while SV models require more complex inferential and computational tools, such as Markov Chain Monte Carlo (MCMC). Hence, although provided with a series of theoretical advantages, SV models are in practice much less popular than GARCH ones. In this paper, we solve the problem of inference for some SV models by applying a new inferential tool, integrated nested Laplace approximations (INLAs). INLA substitutes MCMC simulations with accurate deterministic approximations, making a full Bayesian analysis of many kinds of SV models extremely fast and accurate. Our hope is that the use of INLA will help SV models to become more appealing to the financial industry, where, due to their complexity, they are rarely used in practice.     

Incomes and Inequality in the Long Run: The Case of German Elderly

Abstract. We use German Sample Survey income data to examine the income distribution for elderly individuals during the period from 1978 to 2003. The elderly population, defined as people of age 55 and older, is decomposed by people resident in the Old and New Federal States. Further, we distinguish between persons receiving old‐age pensions and persons who do not. Inequality estimates are decomposed by income components, and the bootstrap method is used to test for statistical significance of results.     

Who provides the capital for Chinese growth: the public or the private sector?

We focus on the role of the government in the provision of investment in China, through the medium of a Dynamic Stochastic General Equilibrium model of the economy in which the form of the production function reflects this governmental role. Using indirect inference, we estimate and test for the elasticity of substitution between government and nongovernment capital in both Constant Elasticity of Substitution (CES) and Cobb–Douglas technologies. The results underscore the strong substitution relationship between government and nongovernment capital from 1949, supporting CES rather than the Cobb–Douglas technology. They also show that the orientation of public investment changed after the start of the ‘Socialist Market Economy’ in 1992: government capital became more complementary to nongovernment capital as it focused more on infrastructure and withdrew from industrial production, intervening only in times of crisis, for stabilization purposes, indirectly via the state banks.     

基于贝叶斯推断模型的中国商业银行内部欺诈研究

文章就中国商业银行操作风险内部欺诈损失样本数据较小的情况,采用贝叶斯推断增加推断效果。损失频率以泊松分布和负二项分布为例,对损失强度以对数正态分布和极值分布为例,利用先验信息分析损失的联合先验分布,其中,极值分布没有共轭分布,针对参数分别推导后验估计。并进行了实证分析,得出四种分布的联合分布。结果表明,由于采用了先验知识,能够比较准确地进行推断,极值分布对损失强度尾部估计较好,中国商业银行内部欺诈损失巨大,采用贝叶斯方法有助于解决操作风险损失数据,尤其是年度极值数据不足问题。     

基于结构熵权—模糊推理法的区域生态经济发展度研究——以青海省为例

基于生态经济学理论基础构建了区域生态经济发展度指标体系(即生态经济结构、生态经济效益、生态安全三大模块层要素和24项具体指标),结合结构熵权—模糊推理法对各类指标权重进行确定,进而计算省域各地区三大模块指标要素值,再通过建立模糊推理系统进行综合测评。同时以青海省为例对其2009年各地区生态经济发展度进行实证研究,进而提出生态经济发展建议。     

Skill uncertainty and social inference

Research in psychology indicates that individuals often make inferences regarding unknown individual qualities based on potentially irrelevant (but socially observable) information. This paper explores occupational choices when individuals receive imprecise signals regarding ability and use the observable characteristics of previously successful individuals to infer own ability. Individuals who fail to observe successful predecessors of their same type may underestimate their potential for success in the occupation. We discuss the role of these biases in light of the literature on affirmative action and firm incentives.     

Robustness of the inference procedures for the global minimum variance portfolio weights in a skew-normal model

In this paper, we study the influence of skewness on the distributional properties of the estimated weights of optimal portfolios and on the corresponding inference procedures derived for the optimal portfolio weights assuming that the asset returns are normally distributed. It is shown that even a simple form of skewness in the asset returns can dramatically influence the performance of the test on the structure of the global minimum variance portfolio. The results obtained can be applied in the small sample case as well. Moreover, we introduce an estimation procedure for the parameters of the skew-normal distribution that is based on the modified method of moments. A goodness-of-fit test for the matrix variate closed skew-normal distribution has also been derived. In the empirical study, we apply our results to real data of several stocks included in the Dow Jones index.     

Processing consistency in non-Bayesian inference

We propose a coherent inference model that is obtained by distorting the prior density in Bayes’ rule and replacing the likelihood with a so-called pseudo-likelihood. This model includes the existing non-Bayesian inference models as special cases and implies new models of base-rate neglect and conservatism. We prove a sufficient and necessary condition under which the coherent inference model is processing consistent, i.e., implies the same posterior density however the samples are grouped and processed retrospectively. We further show that processing consistency does not imply Bayes’ rule by proving a sufficient and necessary condition under which the coherent inference model can be obtained by applying Bayes’ rule to a false stochastic model.     

Time-variation,multiple testing,and the factor zoo

Bayesians circumvent the need for significance threshold correction when multiple testing and we recommend controlling the Type-S (sign), rather than the Type-1, error rate because it yields more reliable frequency properties for inferences. Our unified Bayesian framework, with theory-informed priors, identifies two breaks (2001 and 2008) in our 1980–2018 sample period. After each break the set of characteristics changes, and only market beta is selected in all regimes. In a portfolio application, the method generates significantly larger Sharpe ratios after transaction costs than a range of benchmark methods, including the same model that uses a Type-1 (not Type-S) error framework.