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。     

信息技术投资、价格搜索成本与市场均衡分析

从一个基本模型出发,考察了有无IT投资的零售市场定价、市场份额和利润,研究了不同进货成本的零售商IT投资的条件及其市场均衡,运用数理方法分析了以上三种市场情况．研究表明,不考虑IT投资,零售商r1和r2对于同质产品定价相同,获得相同的市场份额与利润;考虑r1和r2非均衡的IT投资,则二者的利润都低于投资IT前的情形,零售商缺乏内在的投资激励;考虑r1和r2的进货成本不同,成本低的零售商有投资IT的可能。     

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．     

基于信息不对称的企业并购融资方式选择模型

在企业内部和外部信息不对称情况下,讨论了企业并购的融资方式选择问题,并建立了一个决策模型．通过该模型可以看到,企业若估计并购整合后的新公司期望产出水平相对较低,就会选择股权融资方式进行并购融资;若估计新公司期望产出水平相对较高,则会选择负债融资方式;新公司的、期望产出水平若介于二者之间,企业则不会对外融资,仅选择内部融资．在我国当前资本市场不够发达,融资方式相对简单和落后的情况下,该模型对于帮助企业选择合理的并购融资方式具有一定的现实意义．     

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.     

电子商务信用缺失的博弈分析

从信息不对称的角度建立电子商务信用行为的博弈模型,分析了目前我国电子商务信用缺失的原因,并提出了现时期改善我国电子商务信用问题的一些途径。电子商务信用缺失的主要原因有两点:一是没有形成完善的信用奖惩机制;二是在我国信息传递的难度相当大,电子商务获取信用信息的成本高昂。解决我国电子商务信用问题,首先应当加快我国信用市场体系的建设,建立电子商务信用的相关法律和地方法规;进一步完善现代电子商务制度,建立电子商务信用管理机制;建立信用信息系统,完善市场交易的信息沟通网络。