服务出口与实际汇率动态关系分析:基于美国数据的考察

基于代表性家庭追求效用最大化,文中构建了一个由外国实际收入、汇率水平、货物出口以及经GARCH(1,1)模型估计所得条件方差作为汇率风险代理变量组成的服务出口方程.采用自回归分布滞后估计方程参数,并将汇率变动影响货物出口进而传导至服务出口的间接效应纳入分析框架.估计结果显示,汇率水平变动与服务出口之间存在负相关关系,而...     

The derivation of the NPV variance of a risky capital investment project with first-order autoregressive cash flows and autoregressive conditional heteroscedastic variances

In this article, the authors develop a closed-form solution for assessing the capital investment project NPV variance when cash flows obey a first-order autoregressive process. A distinction is established between static and dynamic solutions as the authors focus on the case involving partial positive dependence between cash flows. Under a Markovian process, the NPV solution is stationary in mean but not strictly in variance. Constraining the process to become fully stationary will overestimate the NPV variance. Finally, the authors show that the Markovian NPV variance closed-form solution is robust to the introduction of autoregressive conditional heteroscedastic variances complying with a GARCH(1,1) process; it will, however, have its value increased and consequently the riskiness of the capital investment project.     

中国碳排放权交易市场收益率波动性特征研究

以北京、上海、广东、湖北和重庆碳排放权交易市场为研究对象,运用GARCH族模型研究中国碳排放权交易市场收益率波动性特征。结果表明,5个碳排放权交易市场收益率的波动聚集性、持续性表现不完全一致;北京、上海和重庆存在负向的杠杆效应,广东和湖北不存在杠杆效应;从波动溢出效应关系看,5个碳排放权交易市场间的整体联动性不强;运用方差比率检验法得出,5个碳排放权交易市场均未达到弱势有效市场。这些特征反映出中国碳排放权交易市场的运行机制仍然存在缺陷,建议加强顶层设计,完善碳排放权交易体系。     

基于遗传算法的中国股市波动性研究

本文介绍了金融市场波动性过程的长期记忆性特征的分整自回归条件异方差模型——FIGARCH（p,d,q）,并介绍了一种建立FIGARCH模型的新方法——遗传算法。对上证综合指数进行实证分析,对其收益率建立自回归模型（AR模型）,由Eviews软件可知模型的残差项具有明显的异方差效应,应用遗传算法的基本步骤进行C语言编程,由此求解FIGARCH模型的各项参数值,从而建立FIGARCH模型,实证结果表明中国股市波动性过程具有明显的长记忆性。     

ARCH and GARCH models vs. martingale volatility of finance market returns

ARCH and GARCH models assume either i.i.d. or ‘white noise’ as is usual in regression analysis, while also assuming memory in a conditional mean square fluctuation with stationary increments. We will show that ARCH/GARCH is inconsistent with uncorrelated increments, violating the i.i.d. and ‘white’ assumptions, and violating finance data and the efficient market hypothesis as well.     

印花税率调整对A股市场波动性影响的实证分析

本文以印花税率变动日05年1月23号及07年5月30日前后三十天数据为样本,进行Brown-Forsythe、GARCH的检验和模拟。根据实际结果对沪市A股采取Brown-Forsythe结果,对深市A股采取GARCH结果,得出结论:税率上调对于股市波动性造成的影响要大于税率下调造成的影响,税率过高会增加市场波动性。     

The impact of FIFA’s official announcements on the stock market of Qatar: The case of the 2022 World Cup

This study examines the impact of the FIFA’s official announcements on Doha Stock Exchange (DSE) of Qatar with respect to the 2022 World Cup. Using the abnormal unsystematic volatility method of Hilliard and Savickas (2002), our empirical findings reveal that the DSE market is sensitive to FIFA’s announcements about the 2022 World Cup. We find that four out of six FIFA announcements act as primary drivers to the DSE market volatility. The significant reactions of the DSE market to these announcements unveil the investors’ sentiments about the fate of the governmental and private expenditures on medium- and long-term projects undertaken in anticipation of hosting the 2022 World Cup. The results have some implications to investors in this newly emerging market related to this global sporting event. Any future announcements, good or bad, are likely to impact share prices in DSE market and trigger portfolio reallocation by local and international investors, leading to increased volatility.     

Central bank interventions and jumps in double long memory models of daily exchange rates

In this paper, we estimate ARFIMA–FIGARCH models for the major exchange rates (against the US dollar) which have been subject to direct central bank interventions in the last decades. We show that the normality assumption is not adequate due to the occurrence of volatility outliers and its rejection is related to these interventions. Consequently, we rely on a normal mixture distribution that allows for endogenously determined jumps in the process governing the exchange rate dynamics. This distribution performs rather well and is found to be important for the estimation of the persistence of volatility shocks. Introducing a time-varying jump probability associated to central bank interventions, we find that the central bank interventions, conducted in either a coordinated or unilateral way, induce a jump in the process and tend to increase exchange rate volatility.     

Modeling Conditional Skewness in Stock Returns

Abstract

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the monthly postwar US stock returns. Small positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH-M model not allowing for conditional skewness is found to systematically overpredict conditional variance and average excess returns.