Robust inference in single firm/single event analyses

Single firm/single event (SFSE) studies are relevant in corporate finance. Since inference on abnormal returns in this context necessarily relies on the time series variance of these abnormal returns, the implied problem of heteroscedasticity is obvious, although hard to solve. We analyze robust inference in an SFSE setting using Monte Carlo and resampling experiments. Estimation is biased when the calibration and event period occur in different volatility regimes. We develop a unique specification test for these structural breaks. The most robust inference is obtained by using intraday data and a multiplicative component GARCH estimator.     

一种简化的拟蒙特卡洛-高斯粒子滤波算法

提出了一种简化的拟蒙特卡洛-高斯粒子滤波（QMC-GPF）算法（SQMC-GPF）,以解决将QMC方法应用于GPF时计算复杂度高、运算量大的问题。该算法中，在连续的迭代滤波过程开始之前，首先利用QMC采样产生单位拟高斯分布粒子集，然后用其线性变换产生GPF算法中需要的高斯分布粒子集，省去了重新进行QMC采样步骤。该算法简化了新粒子集的产生过程，减少了运算量和滤波时间，增强了算法的实时性。将粒子滤波算法（PF）、GPF 算法、QMC-GPF算法和SQMC-GPF算法用于单变量非静态增长模型（UNGM）和二维纯角度跟踪模型（BOT）的仿真结果表明，SQMC-GPF算法的滤波性能与QMC-GPF算法的滤波性能相近，但有更为明显的速度优势，具有重要的实际应用价值。     

Study on the effect of sample size on type I error,in the first,second and first-two digits excessmad tests

The first-two digits ExcessMAD test was created in 2016, allowing to evaluate whether a certain data set conforms to Benford’s Law (BL). The purpose of this study is to explore some questions that remained open: develop the exact and approximate mathematical formulation of the first and second digit ExcessMAD test and study the type I error of these tests when applied to different sample sizes conforming to BL and to the uniform distribution, due to its wide use in accounting data. The importance of this study is to make available to accountants, auditors and researchers the first and second digit ExcessMAD tests, which will make it possible to conduct further investigations involving BL, especially for smaller samples. In addition, the relevance of the type I error analysis stems from the reduction of unnecessary additional studies for the investigation of non-conformity, in the case of the erroneous rejection of the null hypothesis stated as conforming to BL. The application of the second digit ExcessMAD test in the uniform distribution reveals that the close proximity between the uniform and BL distributions can lead to misinterpretations. Based on the exact and approximate mathematical formulations of the three ExcessMAD tests and the use of the Monte Carlo simulation technique, samples were generated in accordance with the BL and uniform distributions, with sizes between 100 and 3,500 elements, which allowed the study of type I error and the comparison of the tests applied to those distributions. This paper seeks to cover three gaps in the literature on ExcessMAD tests. In the previous studies, the following approaches were not found: the exact and approximate mathematical formulation of the first and second digit ExcessMAD tests; the analysis of type I error in these tests and the comparison of such results in the BL and Uniform distributions.     

Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

This study investigates the remarkable comovements in U.S. equity returns during the COVID-19 pandemic. It constructs a dynamic factor model (DFM) to illuminate the sources of the comovements and their implications. Using the Markov Chain Monte Carlo (MCMC) estimation method, the study finds that the comovements had a weak daily oscillation pattern during the pandemic. With that pattern, the study also finds significant monetary policy effects on the equity returns of several key sectors. In addition, it estimates the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Among the positive monetary news shocks, the strongest were from interest rate policy surprises. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment.     

BOT项目特许权期决策方法研究

特许经营期长短的决定是高速公路BOT特许权经营的一个核心问题。由于在特许经营期的确定直接关系到东道国政府和项目公司的利益分配,如果特许权期限较长,东道国的利益受损;特许权期限较短则会影响项目公司的利益,这都会导致BOT项目的失败。本文分析了一些影响特许权经营期的因素,并分析了这些因素对特许权经营期决策的影响,建立了基于蒙特卡罗方法来确定特许经营期的模型,从而提供了更系统的理论方法来判断和决策BOT项目特许经营期。