The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients

The US housing market has experienced significant cyclical volatility over the last twenty-five years due to major structural changes and economic fluctuations. In addition, the housing market is generally considered to be weak form inefficient. Houses are relatively illiquid, exceptionally heterogeneous, and are associated with large transactions costs. As such, past research has shown that it is possible to predict, at least partially, the time path of housing prices. The ability to predict housing prices is important such that investors can make better asset allocation decisions, including the pricing and underwriting of mortgages. Most of the prior studies examining the US housing market have employed constant coefficient approaches to forecast house price movements. However, this approach is not optimal as an examination of data reveals substantial sub-sample parameter instability. To account for the parameter instability, we employ alternative estimation methodologies where the estimated parameters are allowed to vary over time. The results provide strong empirical evidence in favor of utilizing the rolling Generalized Autoregressive Conditional Heteroskedastic (GARCH) Model and the Kalman Filter with an Autoregressive Presentation (KAR) for the parameters time variation. Lastly, we provide out-of-sample forecasts and demonstrate the precision of our approach.     

Macroeconomic influences on optimal asset allocation

We develop a tactical asset allocation strategy that incorporates the effects of macroeconomic variables. The joint distribution of financial asset returns and the macroeconomic variables is modelled using a VAR with a multivariate GARCH (M-GARCH) error structure. As a result, the portfolio frontier is time varying and subject to contagion from the macroeconomic variable. Optimal asset allocation requires that this be taken into account. We illustrate how to do this using three risky UK assets and inflation as a macroeconomic factor. Taking account of inflation generates portfolio frontiers that lie closer to the origin and offers investors superior risk-return combinations.     

Estimating unknown join points: Determination of the yen-dollar exchange rate

We propose a Bayesian procedure to estimate a switching regression in which the number of switching points (i.e. join points) is not known. We apply the Bayesian procedure to a regression model for the yen-dollar exchange rate using monthly data from January 1973 to June 1992. We identify three join points in January 1978, September 1988, and March 1990. We compare the post-sample forecast performances of our switching regression model to those of other regression models. The post-sample forecasts show that the Bayesian switching model performs better than the other models.     

中国股票市场价格波动与信息流关系的实证分析

本文运用广义自回归条件异方差(GARCH)模型对中国股票市场的价格波动与信息流之间的关系进行了实证检验。结果发现,把即期交易量作为信息流的替代指标能够显著降低价格波动的持续性,说明在中国股票市场,信息流是产生资产回报ARCH效应的根源,这也验证了国际上流行的量价关系的混合分布假说(MDH)理论。     

金融风险的概率调整度量方法及应用

基于概率调整的金融风险度量方法是从保险业中针对保险风险发展起来的一种方法。它通过对风险的真实概率分布进行修正，来给予高风险事件更大的权重，也就是说，投资者通过调整对右侧尾部风险的主观认识来表明自己对风险的回避程度，最终得到对风险的评价。文章在Choquet积分这一框架下对VaR和TCE风险度量的概率调整表示方法以及一些新的风险度量方法进行了归纳和总结，并通过理论和数值分析对这些不同风险度量的特征和相互关系进行了研究。     

基于GARCH模型的认购权证与认沽权证波动率比较研究

波动性是经济和金融研究的热点问题。本文分别采用无条件波动度量方法和条件波动模型对我国权证市场上具有代表性的六支权证的波动性进行估计,得出以下几个结果:1、六支权证基本上都存在不同程度的波动聚类现象。2、认沽权证的市场有效性弱于认购权证。3、认购权证的波动持续性大于认沽权证,说明认沽权证投机性更强,风险更大。4、认购权证的风险收益补偿大概是认沽权证的6倍。最后,结合本文研究,将给广大投资者一些投资建议。     

证券市场行业间的信息流传导效应——基于沪市A股市场MGARCH波动模型的实证研究

同一时点证券市场行业间的收益率波动传导会对投资者的证券组合产生重要影响,因此,本文选用1997年至2008年沪市A股行业的日收益率数据,利用MGARCH模型对中国证券市场行业指数间的波动传导关系进行了实证研究。研究结果显示:股票市场行业间日收盘指数收益率波动存在显著的行业传导效应,而且传导可能会存在持续性。投资者可以依据股票市场行业间的波动规律,实施短期的行业间套期保值策略。     

企业债券市场波动性研究—基于ARMA-GARCH模型的实证分析

基于交易所企债指数,利用ARMA-GARCH模型分析市场波动性。结果表明ARMA（1,1）-GARCH（1,1）模型可以合理解释企业债券市场波动存在的持续性、丛集性和杠杆效应。场外冲击对条件方差的影响具有持久性,反向冲击比等量的正向冲击会导致更高的下一期的条件方差。EGARCH（1,1）-M模型回归结果表明,市场存在正的风险溢价,但是预期条件波动对预期收益仅有微弱补偿。     

企业债券市场波动性研究——基于ARMA-GARCH模型的实证分析

基于交易所企债指数,利用ARMA-GARCH模型分析市场波动性。结果表明ARMA(1,1)-GARCH(1,1)模型可以合理解释企业债券市场波动存在的持续性、丛集性和杠杆效应。场外冲击对条件方差的影响具有持久性,反向冲击比等量的正向冲击会导致更高的下一期的条件方差。EGARCH(1,1)-M模型回归结果表明,市场存在正的风险溢价,但是预期条件波动对预期收益仅有微弱补偿。     

基于CARR模型和GARCH模型的股市波动性预测研究

金融资产波动性建模和预测是金融理论与实践中的一个重要课题,已经有了许多建模与预测方法。本文利用我国股市的高频数据进行实证研究,分别运用CARR模型和GARCH模型进行波动性预测,进而对两个模型的预测能力进行对比,结果表明:CARR模型在波动性预测方面比GARCH模型的效果更好。