Inverse Gaussian Distribution for Modeling Conditional Durations in Finance

The durations between market activities such as trades and quotes provide useful information on the underlying assets while analyzing financial time series. In this article, we propose a stochastic conditional duration model based on the inverse Gaussian distribution. The non-monotonic nature of the failure rate of the inverse Gaussian distribution makes it suitable for modeling the durations in financial time series. The parameters of the proposed model are estimated by an efficient importance sampling method. A simulation experiment is conducted to check the performance of the estimators. These estimates are used to compute estimated hazard functions and to compare with the empirical hazard functions. Finally, a real data analysis is provided to illustrate the practical utility of the models.     

Effectiveness of policy interventions during financial crises in China and Russia: Lessons for the COVID-19 pandemic

We test the hypothesis that policy interventions in crisis periods are less effective when markets are integrated, drawing on China and Russia’s experience during the global financial crisis. We conduct an event study to examine the response of stock market returns and volatility to intervention efforts using DCC-GARCH and Markov Regime Switching Models. We then estimate the extent of integration of China and Russia with the US market and assess its impact on policy interventions’ effectiveness based on a regression framework. We find that interventions were effective in China but failed in Russia, where greater global links were evident. Our findings provide important policy lessons to address the impact of the current COVID-19 pandemic, given the increasing global market linkages.     

择一定理与二层多目标规划的最优性条件

使用关于集值映射的择一定理,在锥一次类凸背景下,对二层多目标规划的最优性条件进行讨论,获得了几个必要条件和充分条件。     

Risk analysis in the brazilian stock market: copula-APARCH modeling for value-at-risk

Risk management of stock portfolios is a fundamental problem for the financial analysis since it indicates the potential losses of an investment at any given time. The objective of this study is to use bivariate static conditional copulas to quantify the dependence structure and to estimate the risk measure Value-at-Risk (VaR). There were selected stocks that have been performing outstandingly on the Brazilian Stock Exchange to compose pairs trading portfolios (B3, Gerdau, Magazine Luiza, and Petrobras). Due to the flexibility that this methodology offers in the construction of multivariate distributions and risk aggregation in finance, we used the copula-APARCH approach with the Normal, T-student, and Joe-Clayton copula functions. In most scenarios, the results showed a pattern of dependence at the extremes. Moreover, the copula form seems not to be relevant for VaR estimation, since in most portfolios the appropriate copulas lead to significant VaR estimates. It has found that the best models fitted provided conservative risk measures, estimates at 5% and 1%, in a scenario more aggressive.     

中国A股市场价值成长效应的边际条件随机占优分析

利用边际条件随机占优(MCSD)方法对中国A股市场进行了价值成长效应的检验,选取2000～2007年申银万国全市场A股股价指数日收益率作为市场核心组合的基准,分别选取市盈率和市净率指数作为价值/成长型股票的划分指标,将核心组合划分为互斥的子组合.运用MCSD两两比较子组合,发现依据E/P指标划分的价值型股票占优于成长型股票,市场组合存在显著的价值成长效应,且该效应仅在下降市场中显著;而依据B/P指标划分的成长型股票占优于价值型股票,且仅在2005年之后的上升市场中显著.研究结果表明,上市公司财务特征可以部分地作为投资者考察股票收益的一个思路,在不同的市场条件下,投资者应根据不同的财务指标进行投资判断.     

关于实质条件句的两个问题

实质条件句和蕴涵的关系如何？实质条件句与日常语言中的条件句的关系如何？这是关于实质条件句的两个基本问题。澄清了实质条件句和蕴涵的关系，指出不能将蕴涵混同于相应的实质条件句的真理性，并在此基础上分析了所谓的蕴涵悖论。应该将实质条件句在技术上的价值与它在日常语言中的对应物这两个问题分开处理，并论证了以实质条件句概念解释日常语言中的直陈单独条件句的适当性，指出两者之间的关系是卡尔纳普所谓之辩明关系。     

Longitudinal network models and permutation-uniform Markov chains

Consider longitudinal networks whose edges turn on and off according to a discrete-time Markov chain with exponential-family transition probabilities. We characterize when their joint distributions are also exponential families with the same parameter, improving data reduction. Further we show that the permutation-uniform subclass of these chains permit interpretation as an independent, identically distributed sequence on the same state space. We then apply these ideas to temporal exponential random graph models, for which permutation uniformity is well suited, and discuss mean-parameter convergence, dyadic independence, and exchangeability. Our framework facilitates our introducing a new network model; simplifies analysis of some network and autoregressive models from the literature, including by permitting closed-form expressions for maximum likelihood estimates for some models; and facilitates applying standard tools to longitudinal-network Markov chains from either asymptotics or single-observation exponential random graph models.     

Selection of linear mixed-effects models for clustered data

We consider model selection for linear mixed-effects models with clustered structure, where conditional Kullback–Leibler (CKL) loss is applied to measure the efficiency of the selection. We estimate the CKL loss by substituting the empirical best linear unbiased predictors (EBLUPs) into random effects with model parameters estimated by maximum likelihood. Although the BLUP approach is commonly used in predicting random effects and future observations, selecting random effects to achieve asymptotic loss efficiency concerning CKL loss is challenging and has not been well studied. In this paper, we propose addressing this difficulty using a conditional generalized information criterion (CGIC) with two tuning parameters. We further consider a challenging but practically relevant situation where the number, $m$, of clusters does not go to infinity with the sample size. Hence the random-effects variances are not consistently estimable. We show that via a novel decomposition of the CKL risk, the CGIC achieves consistency and asymptotic loss efficiency, whether $m$ is fixed or increases to infinity with the sample size. We also conduct numerical experiments to illustrate the theoretical findings.     

Combining evidence from clinical trials in conditional or accelerated approval

Conditional (European Medicines Agency) or accelerated (U.S. Food and Drug Administration) approval of drugs allows earlier access to promising new treatments that address unmet medical needs. Certain post-marketing requirements must typically be met in order to obtain full approval, such as conducting a new post-market clinical trial. We study the applicability of the recently developed harmonic mean ${\chi }^2$-test to this conditional or accelerated approval framework. The proposed approach can be used both to support the design of the post-market trial and the analysis of the combined evidence provided by both trials. Other methods considered are the two-trials rule, Fisher's criterion and Stouffer's method. In contrast to some of the traditional methods, the harmonic mean ${\chi }^2$-test always requires a post-market clinical trial. If the $p$-value from the pre-market clinical trial is $\ll 0.025$, a smaller sample size for the post-market clinical trial is needed than with the two-trials rule. For illustration, we apply the harmonic mean ${\chi }^2$-test to a drug which received conditional (and later full) market licensing by the EMA. A simulation study is conducted to study the operating characteristics of the harmonic mean ${\chi }^2$-test and two-trials rule in more detail. We finally investigate the applicability of these two methods to compute the power at interim of an ongoing post-market trial. These results are expected to aid in the design and assessment of the required post-market studies in terms of the level of evidence required for full approval.     

Parental unconditional acceptance: An antidote to parental conditional regard

Studies around the world revealed that children's performance is a significant source of parents’ self-worth. This phenomenon is particularly salient among Chinese parents because of the emphasis on interdependent self-construal in a face culture. With child-based worth, many parents feel compelled to use conditional regard to minimize children's failure and maximize success. Yet, parental conditional regard is notorious for its undermining effects on children's well-being. This study aimed to investigate the antecedents and consequences of parental unconditional acceptance to children, an antidote to parental conditional regard. We conducted a priming experiment with 145 parents randomly assigned to child-based worth or unconditional self-acceptance conditions. Results showed that child-based worth was an inhibitive factor, whereas unconditional self-acceptance was a facilitative factor to parents’ provision of unconditional acceptance to children. Parents primed with unconditional self-acceptance had more intention to provide unconditional acceptance to their children, and in turn had more authoritative responses to their children's undesirable behavior. Our findings have practical implications for parent education.