Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach

We study contagion between Real Estate Investment Trusts (REITs) and the equity market in the U.S. over four sub-samples covering January, 2003 to December, 2017, by using Bayesian nonparametric quantile-on-quantile (QQ) regressions with heteroskedasticity. We find that the spillovers from the REITs on to the equity market has varied over time and quantiles defining the states of these two markets across the four sub-samples, thus providing evidence of shift-contagion. Further, contagion from REITs upon the stock market went up during the global financial crisis particularly, and also over the period corresponding to the European sovereign debt crisis, relative to the pre-crisis period. Our main findings are robust to alternative model specifications of the benchmark Bayesian QQ model, especially when we control for omitted variable bias using the heteroskedastic error structure. Our results have important implications for various agents in the economy namely, academics, investors and policymakers.     

Administración del riesgo crediticio al menudeo en México: una mejora econométrica en la selección de variables y cambios en sus características

The early prediction of bad debtors in revolving loans in Mexico is a relevant current issue. The proposed econometric model of behavioral scoring considers the changes in the characteristics of consolidated clients and produces better results than those obtained with methodology used by the CNBV on provisions. The obtained results show the possibility of replacing the current model, minimizing the expected loss and increasing the ROA of Mexican financial institutions in 2.20% complying the methodology and statistical testing criteria according to the Unique Banking Dispositions and the guidelines of Basel II on credit risk.     

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

(G)ARCH-type models are frequently used for the dynamic modelling and forecasting of risk attached to speculative asset returns. While the symmetric and conditionally Gaussian GARCH model has been generalized in a manifold of directions, model innovations are mostly presumed to stem from an underlying IID distribution. For a cross section of 18 stock market indices, we notice that (threshold) (T)GARCH-implied model innovations are likely at odds with the commonly held IID assumption. Two complementary strategies are pursued to evaluate the conditional distributions of consecutive TGARCH innovations, a non-parametric approach and a class of standardized copula distributions. Modelling higher order dependence patterns is found to improve standard TGARCH-implied conditional value-at-risk and expected shortfall out-of-sample forecasts that rely on the notion of IID innovations.     

The shape of small sample biases in pricing kernel estimations

Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In the first step, we analyse theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader regarding potential computational issues coupled with statistical techniques. In the second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that estimated pricing kernels typically violate the commonly believed monotonicity at the centre even when the true pricing kernel fulfils these. This contributes to an alternative, statistical explanation for the puzzling shape in pricing kernel estimations.     

环境质量变化对游客福利的影响——基于旅行费用和条件行为联合数据

环境质量是影响游客旅游需求及福利的重要因素,但单一使用陈述性偏好法或揭示性偏好法仍存在诸多局限性。为探究环境质量改变对游客需求及福利变化的具体影响,结合旅行费用与条件行为数据,在明确现有环境质量状况下景区价值基础上,评估环境质量改善与恶化两种情景下,游客消费者剩余的变化。结果表明,环境质量改变直接影响旅行次数,从而增加或减少消费者剩余,但不会通过影响游客旅行费用间接改变消费者剩余,实际行为与条件行为数据偏好存在一致性;白洋淀景区当前消费者剩余为909.09元/次;环境质量改善使游客平均旅行次数增加1.45次/年,并使每位游客每年的消费者剩余增加1 318.18元;环境质量恶化使游客平均旅行次数降低1.41次/年,并使每位游客每年的消费者剩余减少1 281.81元。     

使用粒子群算法解决期权定价模型参数校准问题——以heston模型为例

期权定价模型的参数校准问题是一个常见的难题,以heston 模型为例,定价时需要估计6 个参数,参数估计问题实质上是高维非线性规划问题,由于估参函数的性质不好,一般的估参方法常常失效。使用粒子群（PSO）智能算法可以改善该模型的参数校准问题,因为粒子群算法具有内在随机性,因此参数估计中的局部极小值问题可以被较好地解决。使用2017 年12 月20 日的香港恒生指数期权作为估计样本,并对2017 年12 月25 日的期权进行样本外预测,数值结果表明使用heston 模型对期权进行定价并配合粒子群算法估计参数具有良好的定价效果。     

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.     

遥感卫星接收系统故障诊断专家系统设计

针对遥感卫星接收系统故障诊断智能化、自动化的技术需求,分析了遥感卫星接收系统的故障特征,给出了故障诊断专家系统的架构。通过对系统设备监测信息及任务流程的分析、故障树的建立与分析,利用产生式的知识表示方式,建立了故障诊断专家系统的知识库;采用启发式的正向推理模式,实现故障的定位。该故障诊断专家系统已用于实际工程项目,提高了遥感卫星接收系统故障诊断的效率。     

宽带随机混沌雷达高速目标速度估计与成像

随机混沌具有真随机性、对初值敏感、易于产生和控制等特点,频率步进信号易于工程实现和处理,结合两者的优势,提出了一种载频随机步进的随机混沌信号(RSCFSCS)模型,用于高速目标的速度估计和距离维高分辨成像。首先,通过非周期函数激励非线性系统,产生不可预测的随机混沌信号(SCS),经频率调制后用作基带子脉冲。同时,将SCS通过映射变换得到跳频编码(FHC),用来决定调频脉冲串的载频步进。RSCFSCS 速度估计包括粗搜索和精搜索,粗搜索采用固定步长,保证速度偏差小于速度分辨单元,而精搜索采用黄金分割搜索算法可得到精确的速度估计。最后,子脉冲经相干合成形成宽带信号,实现高分辨距离成像。数值仿真表明提出的信号模型和处理算法性能良好。     

Economic contribution of essential air service flights on small and remote communities

The Essential Air Service Program (EAS) has attracted considerable criticism and has been a target for either modification or complete termination almost since its inception through the Airline Deregulation Act in 1978. Although its opponents emphasize the program's inefficiency, its supporters claim that the program is crucial to accessing small and remote communities, which helps them develop economically and socially. This paper demonstrates the economic contributions of EAS flights to small and remote communities. Using a two-stage least squares estimation, the major findings indicate that a 1% increase in air passenger traffic in EAS airports with a minimum annual air passenger traffic of 1000 likely leads to a 0.12% increase in per capita income of the community served by that airport. Our results also suggest that EAS communities that are able to sustain their subsidized flights experienced higher per capita income growth in the 1999–2011 period than did ex-EAS communities that lost their flights as a result of non-eligibility.