Benchmark Invariancy,Seasonality and APM-Free Portfolio Performance Measures

The Jobson-Korkie (1981) Z score and the positive period weighting (PPW) score of Grinblatt and Titman (1989) are applied to various benchmarks of market and mimicking portfolios to study the benchmark invariancy problem. Significantly different portfolio performance inferences are found for a sample of 146 equity mutual funds depending on the mean-variance efficiency of the portfolio benchmarks (mimicking portfolios versus market indices). Portfolio performance inferences are affected significantly by the number of factors, nonsynchronous trading adjustment, and the sizes of the firms used for factor extraction. The returns of the portfolio benchmarks exhibit significant monthly seasonalities, which, in turn, significantly influence mutual fund performance inferences.     

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach

This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.     

Microstructure and Seasonality in the UK Equity Market

This paper explores seasonality in the UK stock market. It examines the impact of alternative company year-ends on returns as well as seasonality in bid-ask spreads and trading activity variables including volume, number and size of trades. Consistent with the evidence elsewhere, seasonal variation in stock returns and trading activity is established although there is little evidence of a seasonal pattern in relative bid-ask spreads. Trading rules based on the seasonal patterns do not suggest that seasonality can be exploited to earn excess profits.     

Seasonality, Market Timing and Performance Amongst Benchmarks and Mutual Fund Evaluation

Abstract:  Mutual fund performance is normally measured by comparing results of active management with those obtained by one or several benchmarks that should represent the fund's investment. In this context, this paper examines the effect on mutual fund assessment if a relevant benchmark is omitted. This effect is analysed in three elements of active management: stock selection, market timing, and seasonality. The latter is defined as fund management at specific moments of time with the objective of achieving positive abnormal returns to improve performance. For a sample of Spanish mutual funds, we find that the omission of style benchmarks, particularly that corresponding to small-cap stocks, leads to greater evidence of negative market timing and positive seasonality at year beginning. However, the positive abnormal returns of the seasonality at year end, month end and especially at the beginning of July hold regardless of benchmark omission. The paper therefore also analyses the relation between performance and seasonality, finding that positive seasonality at year beginning and at July beginning improves performance; however, at other moments it implies a possible window dressing strategy in mutual fund management.     

Seasonality in Fund Performance: An Examination of the Portfolio Holdings and Trades of Investment Managers

Abstract:  This study examines the extent to which seasonal variation arises across calendar months in the performance of active Australian equity managers. While it is well documented that there is seasonality in equity market returns, it is unknown whether calendar month variation in managed fund performance exists. Employing a unique database of monthly stock holdings, we find evidence consistent with systematic variation in the risk-adjusted performance of active investment managers over the calendar year. Specifically, we find fund performance is higher in the months when corporate earnings are announced. We also document that the performance of fund managers is lower in the months preceding the tax year-end. Finally, we report evidence that investment manager performance is greater than normal in December, possibly due to both window dressing and the Christmas holiday effect. These findings have important implications for investors attempting to exploit anomalies in fund returns by timing their entry and exit points from active equity funds.     

期货交易活跃度、波动性与投资者结构关系的实证研究

本文将期货市场上的投资者分为个人与机构两类,基于白糖、优质强筋小麦和PTA三个期货品种的样本数据,探讨了交易结构和持仓结构对期货交易活跃度和期货价格波动性的影响规律。研究结果表明:期货交易活跃度主要受交易结构的影响,而期货价格波动大小主要受持仓结构的影响;根据最优的弹性二次模型结果,两者的弹性均呈现出边际递减的经济学变化规律。     

Real Options,Volatility, and Stock Returns

We provide evidence that the positive relation between firm‐level stock returns and firm‐level return volatility is due to firms’ real options. Consistent with real option theory, we find that the positive volatility‐return relation is much stronger for firms with more real options and that the sensitivity of firm value to changes in volatility declines significantly after firms exercise their real options. We reconcile the evidence at the aggregate and firm levels by showing that the negative relation at the aggregate level may be due to aggregate market conditions that simultaneously affect both market returns and return volatility.     

做空机制对我国股市波动性影响的实证分析

我国融资融券业务于2010年3月31日正式启动,而作为一种资本市场机制,做空机 制一直以来都饱受理论界和实务界的争议,做空机制对市场波动的影响尚未达成一致结论。本 文考察了做空机制与市场波动性之间的关系。研究发现：（1）在样本期间内,市场波动与做空 机制之间存在长期的稳定关系;（2）买空交易会在一定程度上增加市场波动,而卖空交易会 在一定程度上降低市场波动,但是二者的影响均有限;（3）综合来看,做空机制并不会引起证 券市场的异常波动,即使市场出现了大幅度的震荡,也不是由于卖空机制本身造成的;（4）本 文认为进一步完善做空机制尤其是卖空交易机制有助于稳定市场。本文结论对于评估做空机 制对市场波动的影响,防范经济冲击风险以及加强市场监管具有重要启示。     

A State‐Price Volatility Index for China's Stock Market

This study derives a volatility index for China's stock market with similar properties to the Chicago Board Options Exchange Volatility Index (the ‘VIX’). A long‐term benchmark of historic volatility expectations is here presented for China from 1996 to 2011, called the ‘China‐ State‐Price Volatility (SPV)’. Construction of this index involves the use of SPV methodology, using implied volatility calculated from options on the Hang Seng China Enterprise Index (HSCEI). Historic open–high–low–close volatility on the Shanghai Composite Index (SHCI) is also used to extend the benchmark prior to the availability of HSCEI options data. The China‐SPV successfully forecasts realised volatility for the Shanghai Stock Exchange. It also serves as a ‘fear gauge’ in that it monitors daily movements of the SHCI in the same way that the VIX monitors the S&P 500 index (Whaley, 2009). The China‐SPV evidences an increasing relation with the US market in terms of the dynamic correlation of levels and changes with the VIX since 2004.     

基于Copula的股票市场波动溢出分析

对于动态投资组合与风险管理来说,测定波动溢出效应是非常重要的。已有的研究是建立在不同金融市场之间的波动是线性相关的,而线性相关并不能描述金融市场之间的非线性关系。借用Copula技术来描述股票市场之间的非线性关系、SV模型来刻画股票市场数据的边缘分布,并引入波动变结构论分析判断波动溢出,实证分析验证了方法是可行的。     

Corporate Bond Returns and Volatility

Recent literature emphasizes the relation of stock volatility to corporate bond yields. We demonstrate that during 1996–2005 corporate bond excess return volatility is directly related to contemporaneous corporate bond excess returns. In fact, the decompositions of aggregate bond volatility have a higher contemporaneous correlation with bond yields in comparison to idiosyncratic stock risk. Additionally, bond volatility and idiosyncratic risk are significant predictors of corporate three‐month and six‐month ahead bond excess returns. We also find that corporate bond volatility contains both slow moving and time‐varying components.     

基于高频数据的金融市场波动溢出分析

在讨论"已实现"波动率、"已实现"协方差基础上,针对金融市场的高频数据,引入"已实现"波动变结构,分阶段计算"已实现"波动率的相关系数,检验"已实现"波动率相关系数,判断在变结构点前后是否发生显著变化,从而分析金融市场之间的波动溢出效应,并进行实证分析。     

中国股市开、收盘波动性的比较研究

利用EGARCH模型,对2000年1月至2007年4月间沪深两市具有代表性的股票及指数的开收盘收益率的波动性进行实证分析,结果表明收益率序列有明显的ARCH效应,其波动性具有显著的非对称性的冲击的持续性;在样本期内,上交所的个股和指数未能观察到开盘波动性高于收盘波动性的现象,而深交所个股在2006年7月实施收盘集合竞价机制之后比较明显地观察到开盘波动性高于收盘波动性的现象。     

Sequential Learning,Predictability, and Optimal Portfolio Returns

This paper finds statistically and economically significant out‐of‐sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time‐varying volatility, and time‐varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time‐varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.     

Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns

This article investigates empirically the comovements of the conditional mean and volatility of stock returns. It extends the results in the literature by demonstrating the role of the commercial paper—Treasury yield spread in predicting time variation in volatility. The conditional mean and volatility exhibit an asymmetric relation, which contrasts with the contemporaneous relation that has been tested previously. The volatility leads the expected return, and this time series relation is documented using offset correlations, short-horizon contemporaneous correlations, and a vector autoregression. These results bring into question the value of modeling expected returns as a constant function of conditional volatility.     

Ambiguous Information,Portfolio Inertia,and Excess Volatility

I study the effects of risk and ambiguity (Knightian uncertainty) on optimal portfolios and equilibrium asset prices when investors receive information that is difficult to link to fundamentals. I show that the desire of investors to hedge ambiguity leads to portfolio inertia and excess volatility. Specifically, when news is surprising, investors may not react to price changes even if there are no transaction costs or other market frictions. Moreover, I show that small shocks to cash flow news, asset betas, or market risk premia may lead to drastic changes in the stock price and hence to excess volatility.     

Stock Returns and the Business Cycle

This article examines whether movements in economic factors dictated by the dividend discount model can explain broad movements in stock returns over the business cycle. As anticipated, stock returns decrease throughout economic expansions and become negative during the first half of recessions. Returns are largest during the second half of recessions, suggesting an important role for expected earnings. These results are consistent with the notion that expected stock returns vary inversely with economic conditions, yet suggest that realized returns are especially poor indicators of expected returns prior to turning points in the business cycle.     

The Dynamic Relations between Market Returns and Two Types of Risk with Business Cycles

We examine the dynamic relations among market returns, market (MV), and idiosyncratic (IV) around business cycles. Compared to the conventional view, which treats MV and IV separately, we first find that excess return on the market anticipates negative MV and IV, suggesting market return's role as an economic indicator, with the relation stronger in recessions. Second, IV helps predict positive MV, mainly in early part of recessions, suggesting a dynamic evolution from IV to MV. Third, MV helps predict negative IV, suggesting MV may substitute IV to some extent.