汇率波动对股市波动的影响研究——基于向量区制转移模型对我国股市波动的实证研究

本文基于二元向量区制转移模型,分析股市和汇率波动率的区制关联性,并依据4种模型结构对我国股市波动性的预测效果进行比较.结果表明2005年7月汇改以来,汇率波动率的均值区制和股市波动率的方差区制间关联性通过显著性检验,汇率波动率的均值区制变化对股市波动率的方差区制有较大影响;汇率波动率的均值区制变化对股市波动的方差有超过 80%的解释力;二者的向量区制转移模型对股市波动率的拟合和预测效果均较好.     

股票市场国际联动与金融传染

为了更好地解释2020年以来中国股票市场与其他主要股票市场间的金融传染问题，本文采用内生状态转移的马尔可夫区制转移向量自回归模型作为统一分析框架，讨论了中国股票市场和美国、英国、法国、德国、日本股票市场间的金融传染特征，并为研究中国股票市场金融传染渠道提供了典型事实和经验证据。模型估计结果表明，相较于美国、法国、日本，中国股票市场是金融传染的接受者。中国股票市场与美国、英国、法国、德国、日本股票市场间存在着随状态大幅改变的同时性市场联动关系。当经济从不确定性较低的状态转移到不确定性高的状态时，同时性的市场联动变得更加剧烈。本文结论与金融传染病理论的预测相符，而且表明金融传染主要渠道会随经济状态的改变而改变。     

证券市场上流动性对波动的影响力分析

本文在Longin(1997)研究的基础上建立了一个简洁的理论模型,该模型很好地刻画了证券市场的流动性对价格波动的影响关系,然后利用模型的结论,针对我国股票市场做了实证分析.实证分析的结论表明,弱流动性指标有助于解释波动、是其重要的解释变量,波动与弱流动性正相关,即流动性越强波动降低.以GARCH模型刻画收益波动效应,我们发现在其条件方差方程中,所有弱流动性对收益波动的影响均显著为正,明显地降低了收益GARCH效应,但与国外市场实证结论存在着显著的不同,即弱流动性加入收益的条件方差方程后并不能完全消除GARCH效应.     

基于MCMC的SCD模型扩展研究

以SCD模型为研究对象,借鉴ACD-GARCH模型的建模思路,构建了价格交易持续期、超高频收益率的双因素模型SCD-GARCH模型。考虑到模型双随机变量给模型参数估计带来的困难,运用MCMC方法,对中国股票市场超高频数据进行实证分析,结果表明模型显著。运用DIC准则比较这2类模型,结果显示SCD-GARCH模型总体更优。     

基于MCMC的SCD模型扩展研究

以SCD模型为研究对象,借鉴ACD-GARCH模型的建模思路,构建了价格交易持续期、超高频收益率的双因素模型SCD-GARCH模型。考虑到模型双随机变量给模型参数估计带来的困难,运用MCMC方法,对中国股票市场超高频数据进行实证分析,结果表明模型显著。运用DIC准则比较这2类模型,结果显示SCD-GARCH模型总体更优。     

基于MCMC的SCD模型扩展研究

以SCD模型为研究对象,借鉴ACD-GARCH模型的建模思路,构建了价格交易持续期、超高频收益率的双因素模型SCD-GARCH模型。考虑到模型双随机变量给模型参数估计带来的困难,运用MCMC方法,对中国股票市场超高频数据进行实证分析,结果表明模型显著。运用DIC准则比较这2类模型,结果显示SCD-GARCH模型总体更优。〖HJ4mm〗     

生产者责任延伸制的SOP模型及激励机制研究

我国即将出台《循环经济法》,新法明确规定了生产者责任延伸制。本文对生产者责任延伸制进行了基于主体—对象—过程模型(SOP模型)的理论建构,并结合理论所揭示的该制度的本质特征,梳理了制度的系统激励要素,从制度适用性激励、责任推进性激励和实施策略性激励三个维度建立了生产者责任延伸制的激励机制模型。     

能源消费总量与结构预测分析——以江苏省为例

构建了能源生产和消费的灰色GM(1,1)模型和能源消费结构的马尔可夫结构转移模型,对江苏省能源消费总量和结构进行模拟和预测.研究表明,两个模型较好地模拟和预测了江苏省能源消费总量和结构的变化趋势;未来一段时期,江苏省一次能源供给率还将进一步减小,同时能源消费结构得到优化,石油消费比例基本保持,煤炭比例快速下降,天然气和可再生能源比例迅速上升,替代煤炭消费下降所造成的缺口.     

生产者责任延伸制的SOP模型及激励机制研究

我国即将出台《循环经济法》，新法明确规定了生产者责任延伸制。本文对生产者责任延伸制进行了基于主体-对象-过程模型（SOP模型）的理论建构，并结合理论所揭示的该制度的本质特征，梳理了制度的系统激励要素，从制度适用性激励、责任推进性激励和实施策略性激励三个维度建立了生产者责任延伸制的激励机制模型。     

中国经济增长与环境污染--基于广义脉冲响应函数法的实证研究

本文运用基于VAR模型的广义脉冲响应函数法与方差分解法,考察了我国1985-2003年期间6类环境污染指标与人均GDP之间的长期动态影响特征.冲击响应分析结果表明,一方面经济增长是影响我国污染排放的重要原因,另一方面环境污染对经济增长也存在着反向作用,然而后者具有一定滞后效应.方差分解结果显示人均GDP是解释各类污染排放的预测方差的重要变量,然而污染排放对经济增长的预测方差贡献度则相对较小.     

REIT Stock Market Volatility and Expected Returns

We study the relation between REIT stock volatility and future returns, focusing particularly on the financial crisis period of 2007–2009. There is ongoing debate about whether stock volatility can forecast future returns. Our findings suggest that REIT‐implied volatility is negatively related to contemporaneous stock returns; there is a significant positive relationship between REIT implied volatility and future stock volatility; and there is a significant negative relation between REIT implied volatility and future stock returns. Lastly, we develop trading rules based on REIT implied volatility to test whether these relationships are exploitable. The result suggests a potentially profitable trading strategy.     

股票场外交易市场的制度构建应遵循五项原则

《首次公开发行股票并在创业板上市管理暂行办浩》的出台，意味着我国股票场内多层次交易市场的制度体系构建基本完成。然而，在我国股票场内交易市场的发展取得了巨大成就的同时，场外交易市场的发展却没有实质性进展，因而，在后创业板时代，应当构建股票场外交易市场制度体系以推动其迅速发展。文章认为，股票场外交易市场制度应当遵循法律定位的差异化、上柜企业的平民化、投资者的机构化、交易方式的创新化、监管制度的高效化这五项原则。     

Correlation Dynamics and Determinants in International Securitized Real Estate Markets

We find the correlation movements among eight developed securitized real estate markets and among their stock markets are quite synchronized over the period from 1995 through 2012. There is a high degree of correlation dependence with many of the realized correlation series subject to regime switching. Moreover, international correlations of public property returns could be significantly explained by five real estate variables that include global real estate securities market volatility, co‐existence of real estate investment trust (REIT) influence, underlying direct real estate return performance differential, real estate securities volatility differential and real estate securities market size differential after controlling for macroeconomic influence and stock market effect. The importance of the control and real estate variables in explaining the return correlations varies across the economies examined.     

Price changes and trading volume relationship in the Hong Kong stock market

Studies on the relationship between price changes and trading volume can provide insight into the structure of the financial market. In this paper, we will study the above topic and concentrate on the stock market of Hong Kong. The correlation between price changes and trading volume as well as that between the magnitude of price changes and trading volume will be examined. We will also check the asymmetry of the price changes and volume relationship. Moreover, we will investigate the relationship between the variance of return and trading volume. Finally, the Granger causality test of price changes and volume will be performed.     

Stock market surveillance and market performance: The case of Taiwan

In this paper we investigate the effect of warning announcements on the price behaviour of stocks. We also study the effects of various corrective treatments (trading halts, call trading and 100% payment/delivery with order) on the performance of stocks in the Taiwan stock market. It is found that warning announcements serve to improve the performance of stocks, except in the case of consecutive warnings. Generally speaking, the corrective measures taken after the consecutive warnings are able to reduce the volatility, the abnormal returns and the excess liquidity of the stocks.The authors are Professors from the National Sun Yat-sen University, and the Securities and Exchange Commission respectively.     

国际能源价格波动对中国股市的影响——基于计量模型的实证检验

本文从中国整体股市—沪深分市场股指—分行业股指三个层次,利用GARCH(1,1)-M模型研究了国际能源价格波动对中国股票市场的影响。研究结果表明,国际能源价格波动对中国股市的整体影响不显著,但对沪、深分市场股指的影响是显著的,并且对沪市收益率的影响大于对深市收益率的影响。分行业来看,国际能源价格波动对化工制品、石油和天然气、基础资源、建筑和材料、食品和饮料、汽车和零件、个人和家庭用品等7行业股指收益率的影响是显著的,其他行业的股票收益率对国际能源价格波动则没有显著响应。     

ECONOMIC APPRAISAL OF TRANSPORT PROJECTS: A Manual With Case Studies by Hans A. Adler

A challenge to developing data-driven approaches in finance and trading is the limited availability of data because periods of instability, such as during financial market crises, are relatively rare. This study applies a stability-oriented approach (SOA) based on statistical tests to compare data for the current period to a past set of data for a stable period, providing higher reliability due to a more abundant source of data. Based on an SOA, this study uses an artificial neural network (ANN), which is one of the commonly applied machine learning algorithms, for simultaneously forecasting the volatility and classifying the level of market stability. In addition, this study develops a dynamic target volatility strategy for asset allocation using an ANN to enhance the ability of a target volatility strategy that is established for automatically allocating capital between a risky asset and a risk-free cash position. In order to examine the impact of the proposed strategy, the results are compared to the buy-and-hold strategy, the static asset allocation strategy, and the conventional target volatility strategy using different volatility forecasting methodologies. An empirical case study of the proposed strategy is simulated in both the Korean and U.S. stock markets.     

Why Do Sellers Hold Out in the Housing Market? An Option‐Based Explanation

In the residential housing market, home owners are reluctant to sell in a declining market. We build a model which focuses on the embedded call option associated with home ownership that allows owners to delay the (irreversible) sale. When prices are low, the (opportunity) cost of a sale, i.e., a higher implied gain from a future sale, likely exceeds its immediate trade benefit and an owner is better off waiting for market conditions to improve. The model also highlights the importance of supply conditions: a more constrained supply is associated with a longer delay. Using state‐level residential housing data, we find evidence consistent with the model. Transaction volume is increasing (decreasing) in the rental growth rate (volatility) in the cross section; their effects are amplified in areas with low supply elasticities, and in times with low market prices. Overall, this paper provides a rational explanation for delayed trading decisions in the housing market.     

高频视角下创业板市场的非对称尾部风险度量

创业板汇集了大批高成长性的科技企业,面临着高度不确定的市场风险。本文考虑创业板市场的非对称性和厚尾性,在偏t分布下利用5分钟高频数据构建已实现GARCH模型衡量其波动率,同时考虑到微观结构噪声影响,在RV的基础上引入对噪声稳健的BPV已实现测度,从而提升高频VaR模型的预测准确性。对创业板指和深证成指的实证结果表明,已实现GARCH模型在参数显著性和模型预测准确性方面均优于低频下的ARMA-fGARCH-VaR模型;基于RV和BPV的已实现模型没有显著差异;相比于深市主板,创业板面临的尾部风险更大。同时,为更好地监测与管控创业板市场风险,从政府和创业企业两个层面提出对策建议。     

Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset-Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code–level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk–return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density.