开放式基金收益的EGARCH模型及其非对称性研究

通过对极少被研究的中国股票型、债券型和混合型开放式基金收益率的波动性进行了GARCH和EGARCH模型分析，结果表明：大部分基金存在非对称效应，并且由于基金条件波动序列的非平稳性，信息冲击曲线具有多样化特征，这为非平稳非线性模型的研究提供了新的方向；通过构建基金类型指数，对开放式基金整体波动性的研究发现，中国开放式基金收益率的波动存在ARCH效应，但不具有非对称性效应。     

基于GARCH族模型的分级基金波动性分析

文章选取中证稳健股票、进取股票和进取债基三大分级基金指数系列的日收益率序列作为研究样本,运用GARCH族模型研究不同类型分级基金收益波动特征.根据AIC准则确定各指数收益率的残差分布与最佳ARIMA-GARCH族模型,并在此基础上,用GARCH-M模型研究波动与收益的关系,用TGARCH模型与EGARCH模型研究波动的非对称性.结果表明:分级基金指数收益率均具有明显的波动聚集性,市场存在强烈冲击,稳健股基具有显著的反向杠杆效应,稳健股基与进取债基有正的风险溢价.     

中国股指期货与股票现货市场之间的风险传递效应研究

 为探索股指期货市场与股票现货市场之间的风险传递效应,本文从日间交易信息和隔夜信息两个角度对沪深300股指期货市场和沪深300指数现货市场进行了实证研究。实证结果显示：股指期货市场与股票现货市场之间的风险传递是双向的,股票现货对股指期货的风险溢出要大于股指期货对股票现货的风险溢出;并且,一市场收益对另一市场收益的影响具有正向杠杆效应,一市场风险对另一市场风险的冲击却具有反向杠杆效应;此外,尽管只有股指期货市场的隔夜信息对其日间收益具有预测能力,但任一市场的隔夜信息对另一市场的日间波动均存在显著的冲击效应。     

基于周内多重分形波动率的基金投资风格漂移风险测度研究

基金发生投资风格漂移是把双刃剑，在获得短期超额收益时，也隐藏着巨大的风格漂移风险。本文首先以我国79只开放式股票型基金为样本，在量化投资风格漂移的基础上，分析发现其收益序列存在多重分形特征，据此构建周内多重分形波动率测度来刻画投资风格漂移收益的复杂波动特征，并与传统的GARCH族波动率计量模型的测度能力进行比较分析，实证结果发现本文构建的周内多重分形波动率测度更加精确，能更好刻画序列的复杂波动特征；然后，进一步构建MFVW VaR模型对基金投资风格漂移风险进行量化测度，发现该模型比传统的参数与非参数VaR模型能更好地对风格漂移风险进行有效测度，基金普遍存在较大的风格漂移风险；最后，对我国开放式股票型基金的产品创新策略与投资风格漂移监管策略进行了一些有益探讨。     

CPPI、TIPP动态资产配置策略比较与绩效研究

一、引言动态资产配置是指在股票、债券、现金等不同类型资产之间进行分配资金,并且在投资期间不断调整各类资产配置比例的过程,其优点在于能够锁定投资组合整体价值的下跌风险,同时保留继续分享市场上涨收益的机会,从而有助于实现在风险控制为前提下投资组合的利润最大化。我国的保本型开放式基金已经开始运用CPPI、TIPP动态资产配置策略作为其基本投资策略。南方保本增值基金以参照优化后的CPPI机制对风险资产上限进行动态调整,以实现避险目的。银华保本基金和天同保本基金都强调以时间不变性投资组合保险策略对股票资产进行动态调整…     

中国开放式基金绩效的实证研究

文章通过实证分析得到中国开放式基金的收益、风险调整收益、基金经理人的时机和股票选择能力等方面的指标,并运用无量纲数据标准化处理方法和因子分析法进行综合评价。实证结果表明:中国的大部分开放式基金可以战胜市场,但没有足够证据显示中国的开放式基金经理人具有显著市场选择能力和证券选择能力。     

沪港台三地股市波动的风险传导效应研究

由于受到地理位置、经济文化等因素的影响,沪港台股市在收益率波动上存在相关性。利用Granger因果检验和Chi-plot图检验两种方法分别对沪港台股市波动进行了实证研究。结果一致表明：沪台股市的收益率波动存在双向传导效应,沪港两市的收益波动仅存在显著的港市对沪市的单向“溢出效应”,而沪台两市传导效应相对较弱。但沪港台三市的收益波动均存在明显的“杠杆效应”。     

开放式基金投资者的处置效应——基于中国面板数据的门限回归分析

在前景理论赎回模型分析的基础上,采用面板数据门限回归方法,重点探讨了开放式基金的业绩对投资者赎回的影响关系.实证结果表明,面板门限模型的解释力强于普通固定效应模型;基金收益对赎回影响始终为正,且收益越高越敏感,进一步证明了中国开放式基金的赎回是一种"处置效应";分红对赎回有促进作用,也存在门限转换特征,增加分红次数可以抑制赎回;市场收益越高,赎回越严重.     

基于函数型自适应聚类的股票收益波动模式比较

股票收益波动具有典型的连续函数特征,将其纳入连续动态函数范畴分析,能够挖掘现有离散分析方法不能揭示的深层次信息。本文基于连续动态函数视角研究上证50指数样本股票收益波动的类别模式和时段特征。首先由实际离散观测数据信息自行驱动,重构隐含在其中的本征收益波动函数。进一步,利用函数型主成分正交分解收益函数波动的主趋势,在无核心信息损失的主成分降维基础上,引入自适应权重聚类分析客观划分股票收益函数波动的模式类别。最后,利用函数型方差分析检验不同类别收益函数之间波动差异的显著性和稳健性,并基于波动函数周期性时段划分,图形展示和可视化剖析每一类别收益函数在不同时段波动的势能转化规律。研究发现：上证综指股票收益波动的主导趋势可以分解为四个子模式,50只股票存在五类显著的波动模式类别,并且5类波动模式的特征差异主要体现在本次研究区间的初始阶段。本文拓展了股票收益波动模式分类和差异因素分析的研究视角,能够为金融监管部门的管理策略制定和证券市场的投资组合配置提供实证支持。     

基于ARCH—M模型上证基金指数收益性与波动性的实证分析

以日收盘价指数的对数收益率为基础，采用ARCH—M类模型（GARCH—M，TGARCH—M和EGARCH—M）对上证基金指数的波动性与收益性进行了实证研究。结果表明基金波动性存在集聚性、波动率与收益率正相关．利空与利好消息对基金波动冲击存在不显著的杠杆效应。     

Conditional correlation in asset return and GARCH intensity model

In an asset return series, there is a conditional asymmetric dependence between current return and past volatility depending on the current return’s sign. To take into account the conditional asymmetry, we introduce new models for asset return dynamics in which frequencies of the up and down movements of asset price have conditionally independent Poisson distributions with stochastic intensities. The intensities are assumed to be stochastic recurrence equations of the GARCH type to capture the volatility clustering and the leverage effect. We provide an important linkage between our model and existing GARCH, explain how to apply maximum likelihood estimation to determine the parameters in the intensity model and show empirical results with the S&P 500 index return series.     

Using a genetic algorithm-based RAROC model for the performance and persistence of the funds

Assisting fund investors in making better investment decisions when faced with market climate change is an important subject. For this purpose, we adopt a genetic algorithm (GA) to search for an optimal decay factor for an exponential weighted moving average model, which is used to calculate the value at risk combined with risk-adjusted return on capital (RAROC). We then propose a GA-based RAROC model. Next, using the model we find the optimal decay factor and investigate the performance and persistence of 31 Taiwanese open-end equity mutual funds over the period from November 2006 to October 2009, divided into three periods: November 2006–October 2007, November 2007–October 2008, and November 2008–October 2009, which includes the global financial crisis. We find that for three periods, the optimal decay factors are 0.999, 0.951, and 0.990, respectively. The rankings of funds between bull and bear markets are quite different. Moreover, the proposed model improves performance persistence. That is, a fund's past performance will continue into the future.     

Analysis of mutual funds’ management styles: a modeling,ranking and visualizing approach

A method to rank mutual funds according to their investment style measured with respect to the returns of a reference portfolio (benchmark) is introduced. It is based on a style analysis model estimating a mutual fund portfolio composition as well as the benchmark one. Starting from such compositions, it computes a proximity measure based on the L ₁ or L ₂ norm to assess the similarity between each mutual fund portfolio returns and the benchmark returns as well as between the returns of each benchmark constituent and that of the corresponding mutual fund constituent. To this purpose the mean integrated absolute error and the mean integrated squared error are computed to derive both a global ranking of mutual fund management styles and partial rankings expressing the over- (under-) weighting of each portfolio constituent. A visual inspection of the results emphasizing main differences in management styles is provided, using a parallel coordinates plot. Since a modeling, a ranking and a visualizing approach are integrated, the method is named MoRaViA. From the practitioners’ point of view, it allows the identification of a specific management style for each mutual fund, discriminating active management funds from passive management ones. To evaluate the effectiveness of MoRaViA, many sets of artificial portfolios are generated and an application on a set of equity funds operating in the European market is presented.     

The Structural Break in the Equity Premium

This article uses Bayesian marginal likelihood analysis to compare univariate models of the stock return behavior and test for structural breaks in the equity premium. The analysis favors a model that relates the equity premium to Markov-switching changes in the level of market volatility and accommodates volatility feedback. For this model, there is evidence of a one-time structural break in the equity premium in the 1940s, with no evidence of additional breaks in the postwar period. The break in the 1940s corresponds to a permanent reduction in the general level of stock market volatility. Meanwhile, there appears to be no change in the underlying risk preferences relating the equity premium to market volatility. The estimated unconditional equity premium drops from an annualized 12% before to the break to 9% after the break.     

Exponential GARCH Modeling With Realized Measures of Volatility

We introduce the realized exponential GARCH model that can use multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to 27 stocks and an exchange traded fund that tracks the S&P 500 index and find specifications with multiple realized measures that dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.     

基金业绩分析——基于有向学习网络的研究

本文使用LASSO算法构建了基于基金持股数据的基金间动态学习网络,将基金研究中传统的无向网络扩展为有向网络,并检验了正向学习与反向学习两种不同的学习模式(信息利用方式) 对基金业绩的影响,进而探讨了其背后的经济含义。实证结果表明:当基金作为被学习者(信息被观测方)时,被正向学习会显著提高其业绩,被反向学习会显著降低其业绩;当基金作为主动学习者(信息观测方)时,无论是正向学习还是反向学习均不会对其业绩造成显著影响;对基金学习动机的分析表明,基金参与学习是为了提升相对自己上期的业绩、防止业绩倒退,且反向学习相对更加有效。本文分析了信 息传递方向、信息利用方式对基金业绩的影响,为如何将统计学习方法应用于金融问题的分析提供了一个新的视角。     

基于广义双曲线SV模型的极值风险度量研究

在金融风险的度量中,拟合分布的选取直接影响到风险度量的精度问题。针对金融收益序列的动态变化,在SV模型中引入广义双曲线学生偏t分布(SV-GHSKt)拟合金融收益序列的尖峰厚尾、不对称以及杠杆效应等特征,通过马尔科夫蒙特卡洛模拟的方法将收益率序列转化为标准残差序列,然后用极值理论的POT模型拟合标准残差序列尾部分布,进而建立一种新的金融风险度量模型———基于SV-GHSKt-POT的动态VaR模型。用该模型对上证综合指数做实证研究,结果表明,SV-GHSKt-POT的动态VaR模型能很好地模拟金融收益序列的尖峰厚尾性、波动集聚性及杠杆效应,并且能够合理有效地提高风险测度的精度,尤其在高的置信水平下表现更好。