Model averaging estimation for conditional volatility models with an application to stock market volatility forecast

This paper is concerned with model averaging estimation for conditional volatility models. Given a set of candidate models with different functional forms, we propose a model averaging estimator and forecast for conditional volatility, and construct the corresponding weight-choosing criterion. Under some regulatory conditions, we show that the weight selected by the criterion asymptotically minimizes the true Kullback–Leibler divergence, which is the distributional approximation error, as well as the Itakura–Saito distance, which is the distance between the true and estimated or forecast conditional volatility. Monte Carlo experiments support our newly proposed method. As for the empirical applications of our method, we investigate a total of nine major stock market indices and make a 1-day-ahead volatility forecast for each data set. Empirical results show that the model averaging forecast achieves the highest accuracy in terms of all types of loss functions in most cases, which captures the movement of the unknown true conditional volatility.     

破产准则下修正Heston波动的最优再保-投资决策

为了研究修正Heston随机波动率下保险公司最优再保-投资策略问题,在盈余水平服从扩散过程的假设下,运用随机动态规划原理,建立最小化破产概率准则的HJB方程,通过求解方程得到最优再保-投资策略和最小化破产概率的显式解,并分析了随机波动率对最优投资决策,最优比例再保险策略和最小破产概率的影响。     

快速路网实时OD预测的时间颗粒度选择

常见的基于实测数据的origin-destination(OD)预测方法分为两类:一类基于历史信息,即根据上一天或上一周同一日相同时段的数据进行预测,简称同比预测法;另一类则是根据同一天相邻时间段的数据预测本时段的OD,简称环比预测法.预测所用基础数据的时段长度称为时间颗粒度.时间颗粒度的大小对OD预测结果的稳定性、准确性具有重要影响.针对上海快速路网,采用ADF单位根检验和KMeans聚类分析方法,研究时间颗粒度对预测结果的影响,提出了时间颗粒度选择的建议,同比预测方法相比环比预测法更容易得出稳定、合理的预测结果,30~60min的时间颗粒度预测效果较好.     

股票价格模型的实证研究

研究股票价格的模型,对上海及深圳证券交易所１９９７年和１９９８年全年的综合指数和成份指数进行了分析研究,对模型进行了实证研究,估计了参数,最后作了模拟分析。     

随机波动率下最优投资问题的逼近解

该文讨论随机波动率下的最优投资问题,随机波动率为马尔科夫扩散过程函数．股票价格的波动不但受到其本身价格的影响,还受到各种市场因子的影响．通过Legendre变换以及逼近分析,求得了原问题的近似显式解,从而得到了投资问题的0级最优策略．     

Evaluating volatility and interval forecasts

A widely used approach to evaluating volatility forecasts uses a regression framework which measures the bias and variance of the forecast. We show that the associated test for bias is inappropriate before introducing a more suitable procedure which is based on the test for bias in a conditional mean forecast. Although volatility has been the most common measure of the variability in a financial time series, in many situations confidence interval forecasts are required. We consider the evaluation of interval forecasts and present a regression‐based procedure which uses quantile regression to assess quantile estimator bias and variance. We use exchange rate data to illustrate the proposal by evaluating seven quantile estimators, one of which is a new non‐parametric autoregressive conditional heteroscedasticity quantile estimator. The empirical analysis shows that the new evaluation procedure provides useful insight into the quality of quantile estimators. Copyright © 1999 John Wiley & Sons, Ltd.     

Performance of GARCH models in forecasting stock market volatility

This paper studies the performance of GARCH model and its modifications, using the rate of returns from the daily stock market indices of the Kuala Lumpur Stock Exchange (KLSE) including Composite Index, Tins Index, Plantations Index, Properties Index, and Finance Index. The models are stationary GARCH, unconstrained GARCH, non‐negative GARCH, GARCH‐M, exponential GARCH and integrated GARCH. The parameters of these models and variance processes are estimated jointly using the maximum likelihood method. The performance of the within‐sample estimation is diagnosed using several goodness‐of‐fit statistics. We observed that, among the models, even though exponential GARCH is not the best model in the goodness‐of‐fit statistics, it performs best in describing the often‐observed skewness in stock market indices and in out‐of‐sample (one‐step‐ahead) forecasting. The integrated GARCH, on the other hand, is the poorest model in both respects. Copyright © 1999 John Wiley & Sons, Ltd.     

国内外石油价格波动溢出效应实证分析——以大庆原油价格和布伦特原油价格为例

采用大庆原油价格和布伦特原油价格2001～2010年的周数据,运用BEKK-GARCH模型分析了国内外石油市场的波动溢出效应以及国内石油市场与国际石油市场波动的动态相关性。实证结果表明,国外市场对国内市场产生了显著的波动溢出,国内市场对国外市场具有一定的影响,但效果并不明显;信息传导方向是从国外市场到国内市场,且国内石油市场波动滞后于国外市场。进一步的研究发现,国内外石油价格波动的动态相关性不断加强。     

基于前景理论的波动不对称性

波动不对称性是金融市场中普遍存在的一种现象,也是金融理论和实践所关注的焦点.借鉴行为金融理论中关于前景理论的研究成果,构造一类基于前景理论决策框架的投资者,并在此假设基础上推导出相应的资产价格均衡模型. 通过数值模拟发现,前景理论不仅可以很好地解释金融市场的波动不对称现象, 而且,在利空消息出现频繁的市场中, 相对于利好消息对价格的向上推动作用,利空消息对价格下挫的影响更加突出, 引入私房钱效应后,价格波动更加明显. 可见,上述结论都从不同角度有力地解释了金融市场的波动不对称性.     

随机波动和跳跃下的短期利率动态

在短期利率模型中引入随机波动和跳跃两种因素,利用序贯参数更新思想和粒子滤波方法实现模型的估计,并对中国银行间短期利率进行实证分析. 研究结果显示短期利率存在显著的随机波动和跳跃特征,表明CIR-SV-J模型在描述短期利率动态中拟合效果最优,而且忽视随机波动或跳跃因素会使拟合变差,影响对利率均值回复特征的描述,并证明了随机波动因素在模拟中比跳跃因素起着更为重要的作用.