Structural effects and spillovers in HSIF, HSI and S&P500 volatility

We adopt a BEKK-GARCH framework and employ a systematic approach to jointly examine structural breaks in the Hong Kong cash index and index futures volatility and volatility spillovers from the S&P 500 cash and futures. Multiple switching dummy variables are included in the variance equations to test for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market. Abolishment of the up-tick rule, increase of initial margins and electronic trading of the Hang Seng Index Futures (HSIF) are found to have significant impact when US market spillovers are excluded from a restricted model. Volatility spillovers from the US market are found to have a significant impact and account for some mis-specification in the restricted model.     

Multivariate Modeling of Daily REIT Volatility

This paper examines volatility in REITs using a multivariate GARCH based model. The Multivariate VAR–GARCH technique documents the return and volatility linkages between REIT sub-sectors and also examines the influence of other US equity series. The motivation is for investors to incorporate time-varyng volatility and correlations in their portfolio selection. The results illustrate the differences in results when higher frequency daily data is tested in comparison to the monthly data that has been commonly used in the existing literature. The linkages both within the REIT sector and between REITs and related sectors such as value stocks are weaker than commonly found in monthly studies. The broad market would appear to be more influential in the daily case.     

Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model

This paper is the first study to apply the multivariate factor stochastic volatility model (MFSVM) for analyzing the correlations among six cryptocurrencies. We use MFSVM with the Bayesian estimation procedure for the period from August 8, 2015, to January 1, 2020. According to the findings, there is a significant positive correlation between price volatility values of Bitcoin and Litecoin. Besides, the volatility values of Ethereum have a positive correlation with both Ripple and Stellar. There is also a positive correlation between the volatility values of Ripple and Dash. These findings are robust to consider different correlation networks. The evidence implies that Bitcoin is mainly related to Litecoin, but Ethereum is associated with other cryptocurrencies.     

Multivariate time series analysis of traffic congestion measures in urban areas as they relate to socioeconomic indicators

Traffic congestion has significant adverse implications for the environment and economy. Many state and local transportation agencies have implemented traffic congestion management practices to alleviate the negative implications of urban traffic. One of the major drawbacks of traffic congestion management practices is that they do not account for socio-demographic and economic factors, which have a significant impact on traffic congestion. Understanding the influence of these factors is very crucial because they can help to communicate the system's performance management and target setting. Only a few studies analyzed the relationship between traffic conditions (e.g., traffic demand and vehicular traveling speed) with a limited number of socio-economic factors. Moreover, most of the existing models ignore the temporal and spatial autocorrelations of traffic congestion, which may significantly limit their reliability and effectiveness. This study is developed with the purpose of identifying the most relevant external factors that affect traffic congestion performance measures. To conduct the research, we have used three urban congestion performance measures collected from 51 metropolitan areas across the U.S. over a four-year period, 2013–2016: travel time index, planning time index, and congested hours. We have used multivariate time series models to account for the complex inter-relationships among the performance measures and socioeconomic factors to identify the most influential factors affecting system performance. We have finally developed predictive models to estimate the traffic congestion measures using these factors. The results of rigorous modeling show that the factors influencing the traffic congestion measures are monthly average daily traffic (MADT), the number of employed, rental vacancy rate, building permits, fuel price index, and Economic Conditions Index (ECI). The prediction models indicated that the effects of these factors are statistically significant and could be used to forecast future trends in three performance measures accurately.     

Improving international diversification benefits for US investors

There are two main questions that have attracted considerable attention in the financial literature over the last few years: whether international diversification benefits are still substantial in the current context of increasing market correlations and which approach provides better results in terms of out-of-sample returns and risk. In this context, the aim of this study is to provide empirical evidence about the economic gains that a US investor could obtain with a dynamic strategy based on the use of time varying returns and volatility forecasts from a multivariate VAR–DCC approach for the exchange trade funds of US, UK and Japan which are the most actively traded on the New York Stock Exchange in recent years. These findings are relevant not only for academics, but also for practitioners, especially for professional portfolio managers.     

Industry co-movement and cross-listing: Do home country factors matter?

This paper investigates the co-movement of American depositary receipts (ADRs) and the industry returns of home and U.S. markets with a focus on industry co-movement by applying time-varying and constant copulas model specifications. We also examine the impacts of country-specific factors on the industry co-movement in cross-listing issue. Co-movements are found in relation to the industry returns and economic asymmetry. The industry co-movement of ADR is higher with home country than that with the U.S., supporting the fundamental view. The results pertaining to the ADR-home pairs are as follows: (1) during economic recessions, the factors of influencing industry co-movement include import and the number of listed companies; (2) in contrast, during economic booms, industry co-movement is significantly influenced by energy import, export, import, the number of listed companies, and high-tech export. The factors that impact the industry co-movement of ADR-US contain foreign direct investment, reserve, cash surplus, energy import, and the number of listed companies.     

Impacts of Economic Integration on Stock Market Dependence Without Jump Effects

This article investigates the impacts of the Closer Economic Partnership Arrangement (CEPA) on stock market dependence between Hong Kong and China. To avoid the influence of unusual events on stock market dependence, the mixed generalized autoregressive conditional heteroscedastic with the autoregressive jump intensity (GARJI) margin model was modified to exclude jump innovations. The t copula was chosen to estimate the unknown dependence break and measure the average dependence level change. The stock market dependence break occurred about one and a half years after CEPA became effective, and the CEPA increased stock market dependence between Hong Kong and China. Moreover, this article shows the influence of stock market jump effects in the case of CEPA.     

How many,how often,and how new? A multivariate profiling of mobile app users

Despite enormous demand for and explosive growth of mobile phone apps in recent years, few studies have been conducted to arrive at a multi-faceted valuation of app users. Our paper addresses this important gap in literature. Drawing on Household Production Theory and Hedonic and Utilitarian Consumption Theory, we investigate how mobile app users behave in the dimensions of possession quantity, usage Frequency, and acquisition Recency. We propose a multivariate model to examine these behaviors jointly and calibrate it using data from a survey of app users. We take a Bayesian MCMC computational approach for model calibration. The results are consistent with our theoretically derived expectations. The significance of the findings is discussed.     

关于开放式基金VaR风险的比较——基于半参数与非参数法

在刻画和估计资产联合损失分布函数的基础上对开放式基金VaR风险进行比较分析。在极值情况下有两种算法：一种是半参数法,采用GPD分布来拟合损失分布的尾部和核密度分布拟合损失分布的中间部分,运用copula函数来刻画资产损失的相依结构;另一种是非参数法,用Bootstrapping和FHS方法对收益率进行抽样和模拟。经过实证分析发现在较低置信水平下,宜于采用非参数法;而在较高置信水平下,采用半参数法则更合适,这也充分说明半参数法适合在更极值情形下对开放式基金估计VaR风险。     

基于pair—copula的社保基金投资组合风险测度研究

社保基金是社会保障事业健康发展的基石,风险管理是社保基金保值增值的关键问题之一。提出了pair-copula—GARCH-EVT模型以测度社保基金投资组合风险,与传统的n维copula—GARCH-EVT模型相比,该模型不仅考虑了维数的影响,而且还能灵活地选择copula的类型。实证研究发现,基于pair-copula-GARCH-EVT的模型测度社保基金投资组合风险的准确性要高于传统的copula—GARCH—EVT模型。