基于宏观因子的SVR-Black-Litterman资产配置模型及其实证研究

随着全球经济不稳定性的增强和我国利率市场化的深入推进,金融资产的波动性不断加剧,资产组合优化配置问题依然是金融投资理论研究和实务领域的核心问题。Black-Litterman模型因其解决了传统均值方差模型对参数敏感的问题,且允许将投资者观点融入模型中,已被广泛应用于资产配置实践中。然而投资者观点矩阵的确定一直是Black-Litterman模型理论和应用研究的难点。将宏观因子融入投资者观点矩阵,应用基于主成分分析法的支持向量回归(SVR)模型实现观点矩阵的估计,构建融入宏观因子的SVR-Black-Litterman资产配置模型。为检验模型的有效性,将该模型与经典模型进行比较。实证结果表明,所构建的模型具有较好的市场表现。     

基于GARCH模型的沪深两市波动性分析

资产收益率的波动问题是研究的焦点。我国股票市场还很年轻．对其波动性的研究一直是热点,目前研究的方法也很多。许多研究表明我国股票市场的波动性存在着一定的聚类现象．也即会存在条件异方差性。文章引用GARCH模型对中国股市的风险与收益进行实证研究．从对沪、深两市的各自分析着手,确定其关系,再结合两个市场的数据进行相关性的分析。两个市场的波动性有着密切的关系,以及中国股市将不断的有序、有效的发展。     

马柯维茨均值方差模型在MATLAB与EXCEL下的实现与讨论

深入分析马柯维茨均值方差模型以及在投资组合应用时的约束条件,在综合考虑投资收益与风险平衡的前提下,基于相关系数法分析不同投资组合之间的相关性,根据决策者的投资偏好,改进了马柯维茨均值方差模型的约束条件,计算出投资组合的有效市场边界,并通过选取不同资产在不同经济周期下的实际数据,基于MATLAB与EXCEL实现了该方法的模型计算,得出了最优资产配置组合,并通过与基准的对比验证了该方法在平衡投资风险与收益方面的有效性。     

虚拟社交与家庭风险资产配置

本文利用中国家庭微观调查数据,使用二值Probit模型以及左侧截断Tobit模型分析了互联网虚拟社交对家庭金融市场参与以及风险资产配置的影响。研究结果发现,与没有进行线上虚拟社交的家庭相比,线上虚拟社交会能够显著增强家庭参与金融市场,促进家庭投资风险资产。因此鼓励加强规范性各类社交网络平台以此增强线上虚拟社交,从而促进中国家庭金融市场参与以及风险资产配置。     

基于不同频率数据的股指期货主力合约转换的差异性和有效性研究

分别以日低频数据、5分钟高频数据和1分钟高频数据作为数据基础,以持仓量最大作为主力合约转换时点的判定标准,采用脉冲响应分析和方差分解方法,研究基于不同频率数据的股指期货主力合约转换时点的差异性和有效性。对我国沪深300股指期货市场连续10次主力合约转换进行实证分析,得到的结论是：以不同频率数据确定的主力合约转换时点存在明显的差异性,1分钟高频数据确定的主力合约转换的有效性最强,日低频数据确定的主力合约转换的有效性最弱。     

金融时序波动性和时变相关性分析

波动性和相关性分析是金融领域定量分析的基础，广泛用于投资组合选择、资产分配以及风险管理之中。本文采用多变量波动性模型来估计波动性和相关性，并就中国沪深股市收益数据加以实证分析，我们得到上证指数和深圳成指的日对数收益存在高度正相关和具有显著的时变相关性，且波动性的估计和预测较之于单变量情形时有较好的改善。     

农村居民金融素养对家庭金融资产配置的影响——基于CHFS微观数据的实证研究

本文以农村地区为研究视角,使用2017年CHFS微观数据,实证研究农村居民金融素养对家庭金融资产配置的影响。结果表明,金融素养对风险资产持有和风险资产占比的影响均显著为正。所接受教育程度越高、家庭收入越高、自营工商业以及地区经济越发达的农村居民,更有可能持有风险资产以及提高风险资产比重。据此,笔者提出了有关政策建议。     

股指期货的数据频率对统计套利绩效的影响研究——基于动态预测区间的择时交易机制

为了研究股指期货的数据频率对统计套利的绩效影响,本文提出了动态预测区间的择时交易机制。在实证检验中分别运用OLS恒定波动策略,GARCH时变波动策略与之比较。研究结果表明,在样本区间内外,动态预测区间模型各项指标均是最好的。其次,在确保交易过程中发出的信号始终处于有效状态的情况下,随着使用的数据频率趋向高频,可套利次数增多,同时持有期的收益率升高,而单次套利的最大回撤比例也有增加,说明采用高频数据套利将会牺牲一部分收益能力的稳定性来换取绝对收益量的增加。     

流动性和条件资产定价:理论和实证检验

选择适当的工具一直是条件资产定价研究的中心,目前国内外尚没有研究从这个角度探讨流动性对资产定价的影响。本文在Breeden-Lucas随机折现因子框架下建立了以市场流动性为工具的条件CAPM,并使用1996．1．2-2004．12．31期间的沪深A股日度交易数据构造了Amihud（2002）的非流动性测度、Farm-French组合、定价因子等。一阶段GMM估计表明,该滞后工具可有效捕获资产回报的可预测变化。模型解释这种变化的能力显著优于Fama-French三因子模型和CAPM．且几乎没有统计显著的残留规模效果和价值效果。     

日经指数期货与现货市场波动关联性研究

以日收益率作为分析对象,考察了日经225指数期货推出对日本股票市场指数波动性的影响,研究样本期间为1984.01.04～2009.09.30.分别采用EGARCH、TGARCH和DCC-GARCH3种模型对日收益率的条件方差及市场波动性之间的关系进行实证分析,研究结果表明:(1)日经225指数期货在大阪证券交易所(OSE)的推出加剧了股票现货市场的波动性;(2)好坏消息对股指波动率的影响存在非对称性,即利空消息对收益率波动率的影响比利好消息大;(3)日经225指数期货与标的指数之间的波动性具有较强的联动性和传递性,且2007年次贷危机的爆发加强了两者间的相关性.     

EXTREME VALUE AT RISK: A SCENARIO FOR RISK MANAGEMENT

This paper uses the generalised extreme value (GEV) distribution to model the extreme losses that are likely to occur during market crashes, in the case of an investor who has long positions in stocks and currencies. The null hypothesis – which tests for normality of asset returns – is rejected due to asymmetry of these returns. We assume that the asymmetric behaviour and volatility of the returns are captured by the shape and scale parameters, respectively, of a GEV distribution. The data set includes stock indices for the United States, Japan, the United Kingdom, Germany, France and South Africa, and the South African rand exchange rates against the US dollar observed from 3 January 2005 to 30 December 2009. In addition, we divide this sample period into two periods: the pre‐crisis period, from 3 January 2005 to 31 December 2007 and the crisis period, from 1 January 2008 to 30 December 2009. We compared the estimates of value at risk (VaR) using an extreme value theory (EVT) model, with the estimates derived from the traditional variance–covariance method and found that during the crisis the 99% extreme VaR estimates are more reliable as they lie within the Basel II green zone. These results suggest that, at higher quintiles, the VaR estimates based on EVT are reliable and more accurate than estimates from the traditional method.     

Constrained Mean-Variance Portfolio Optimization with Alternative Return Estimation

This paper studies the problem of asset allocation in a mean-variance framework. The theoretical model of portfolio optimization is specified and then applied to a long panel data set from historic to most recent times, March 1990 – March 2013. The paper contributes in three ways. First, an alternative asset return model is proposed that combines the historical returns, capital asset pricing model (CAPM) and returns estimated based on firm fundamentals. These return estimates enter the optimization problem. The second contribution is the application of an improved covariance matrix estimator that has superior properties compared to the typical sample covariance estimator. Third, the paper proposes two investments strategies. The first proposition suggests always choosing the maximized Sharpe ratio portfolio and the second one, the portfolio with the highest information ratio. The nature of both strategies is designed for investors with different appetites for risk. The performance of these choices is analyzed in light of four types of constraints: upper/lower investment limits, group constraints and transaction costs. The one-period optimal investment portfolio is rebalanced at quarterly intervals. Both strategies are benchmarked against an alternative investment choice such as holding the S&P 500 index, or investing in a risk-free asset such as a bond. Portfolio analysis and backtesting reveal that the strategies are superior to simply holding an equally weighted portfolio, a risk-free asset or the S&P 500 index.     

Monetary Policy and Risk-Based Asset Allocation

This paper focuses on the effects of the Fed’s monetary policy on stock and bond returns co-movement and their implications to risk-based asset allocation. Using a regime-switching model that controls for the economic effects of monetary policy we identify three co-movement regimes. We document that risk-based portfolio strategies poorly perform in the low correlation regime which features inflation shocks. We find outperformance evidence under the negative correlation regime with a high stock market risk and a very accommodating Fed policy. Less effectiveness is demonstrated under the positive correlation regime where bonds are regarded as risky assets and interest rate volatility is fueled by monetary policy.     

Twice a day or continuously? Observation frequency and inference on foreign exchange volatility persistence

This paper examines how observation frequency alters inference on cross-locational volatility dependencies in the foreign exchange market. When volatility proxies are based on tick-by-tick exchange rate quotes, this paper finds evidence of statistically significant own-region volatility persistence as well as symmetric interregional dependence. In addition, this paper detects strong day-of-the-week effects, with Mondays displaying the lowest volatility. These results differ from previous studies which based volatility estimates on only two daily observations per region.     

Understanding the inflation–output nexus for China

In this article we examine several hypotheses relating to output and inflation dynamics in China. The hypotheses tests are based on the exponential generalised autoregressive conditional heteroskedasticity (EGARCH) model of Nelson [Nelson, D. (1991). Conditional heteroskedasticity in asset return: A new approach, Econometrica, 59, 347–370]. Our findings suggest that Chinese output–inflation behaviour is consistent with the hypothesis that increased inflation uncertainty lowers average inflation; the hypothesis that inflation volatility reduces economic growth and the hypothesis that higher output volatility increases economic growth. However, we find no support for the hypothesis that higher output volatility increases the average inflation rate.     

Strategic Asset Allocation for China＇s Foreign Reserves： A Copula Approach

In this paper, we study strategic asset allocation for China＇s foreign reserves using a risk- based approach. Four aspects of the risk management are investigated： an investment universe, dependence structure, allocation strategies under risk minimization and trade-off between risks and returns. A regime-switching copula model is developed to investigate the dynamic dependence between assets. One regime emphasizes a short-term safe asset and the other regime emphasizes a long-term safe asset. The optimal allocation is derived following two strategies： risk minimization and trade-off between risks and returns in utility maximization with disappointment avoidance, lf the central bank focuses solely on risk minimization, the asymmetries in the asset return dependence encourage the flight to safety. However, if higher risks are allowed in exchange for higher returns, even the exchange is very conservative, and the asymmetries would discourage the flight to safety. Therefore, we suggest that China should mitigate its flight to safety after 2008 and increase holdings of short-term bank deposits, long-term treasury bonds and euro bonds.     

资产价格波动差异:由股市与房价观察

我国股票市场和住宅市场的市盈率与货币幻觉代理变量通货膨胀率、名义利率呈现明显的反向关系,即在高通胀时,市场被低估;在低通胀时,市场被高估。通货膨胀通过货币幻觉,影响资产估值高低。股市的市盈率波动幅度远大于住宅市场,股票价格波动主要来自估值倍数变化;而住宅的估值倍数波动小,房价波动更多地受到了估值倍数与租金变化的综合影响。     

Do contagion effects exist in capital flow volatility?

The cross-border transmission of a financial shock has been a subject of rich literature. While a large number of studies have focused on the phenomenon of strong co-movements of asset prices and capital flows in the event of financial stress, very few discussed the contagion or spillover effects in terms of capital flow volatility. This paper is one of the first attempts to assess, empirically, whether or not there is a global and regional spillover effect in the volatility of capital flows to emerging and developing countries. Based on the sample of 49 emerging and developing countries for the period 1980–2009, the empirical results suggest strong and significant contagion effects in the volatility of capital flows to individual economies. The magnitudes of contagion vary depending on the type of capital flows, whether it is foreign direct investment (FDI) or portfolio and other investment (mostly bank lending). The findings also suggest the volatility dynamics of gross flows is different from that of net flows. The volatility of net inflows is more exposed to intra-regional contagion compared to that of gross inflows.     

Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy

In a similar way to the stock market, the housing market in China has often been portrayed as highly speculative, giving rise to “bubble” concerns. Over the last decade, residential prices increased every year on average by double digits in Beijing or Shanghai. However many observers and researchers argue that fundamentals of the housing sector, both sector-specific and macroeconomic, may have been the driving force behind housing price volatility. While existing empirical work exclusively relies on the government housing prices which may suffer from the well-documented downward bias, this paper uses original high frequency unit price as well as transaction series for the residential resale housing markets of Beijing and Shanghai between January 2005 and December 2010 to test alternative hypotheses about housing prices volatility.We propose a sequential strategy in five steps integrating several techniques previously developed in a piecemeal and scattered way. First, we construct daily hedonic prices. Second, in order to search for the possible presence of bubbles on such high-frequency data, we propose using recently developed tests of an explosive root as an alternative to the unit root hypothesis. The third step is generated by the necessity of handling microstructure noise present at a daily frequency, thus filtering the raw data to extract a random walk component. The fourth step extracts a slowly changing monthly volatility component from the filtered daily hedonic real estate data. Finally, in so far as the presence of bubbles does not seem to characterize the residential housing market in major Chinese cities, such as Beijing and Shanghai, in a fifth step we show that fundamentals are able to explain slowly changing volatility, as well as transaction volumes in these first‐tier cities.