Price delay and post-earnings announcement drift anomalies: The role of option-implied betas

In this study, we used informational advantage in the options market to investigate whether the option-implied equity risk developed by Chen, Chung, and Tsai (2016) - viewed as a type of time-varying beta - can help explain both the Hou and Moskowitz (2005) price delay premium and post-earnings announcement drift (PEAD). Our empirical results revealed a clear association between quintile portfolios with greater price delay premiums and higher option-implied betas, while the Fama-MacBeth regressions showed that the implied betas are positively related to future delay-based portfolio returns. Regarding the PEAD, we discerned a general increase in the mean of portfolio option-implied betas with standardized unexpected earnings portfolio drift. Our regression results support the notion that a portfolio’s PEAD can be viewed as compensation for the variations in option-implied betas.     

Instability and predictability of factor betas of industrial stocks: The Flexible Least Squares solutions

By using the Flexible Least Squares (FLS) method, this study traces out month-to-month alterations in factor betas. The time variation paths of factor betas reveal time-varying correlations between different factor betas. Moreover, the FLS method is found to be able to produce more accurate and stable forecasts of industry costs of equity than rolling regressions and other methods, in terms of smaller forecasting errors and standard deviation of forecasting errors, thanks to the better estimates of factor betas.     

Alternative econometric implementations of multi-factor models of the U.S. financial markets

This paper analyzes the empirical performance of two alternative ways in which multi-factor models with time-varying risk exposures and premia may be estimated. The first method echoes the seminal two-pass approach introduced by Fama and MacBeth (1973). The second approach is based on a Bayesian latent mixture model with breaks in risk exposures and idiosyncratic volatility. Our application to monthly, 1980–2010 U.S. data on stock, bond, and publicly traded real estate returns shows that the classical, two-stage approach that relies on a nonparametric, rolling window estimation of time-varying betas yields results that are unreasonable. There is evidence that most portfolios of stocks, bonds, and REITs have been grossly over-priced. On the contrary, the Bayesian approach yields sensible results and a few factor risk premia are precisely estimated with a plausible sign. Predictive log-likelihood scores indicate that discrete breaks in both risk exposures and variances are required to fit the data.     

Forecasting systemic impact in financial networks

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside risks on systemic distress. So-called systemic risk betas account for a company’s position within the network of financial interdependencies, in addition to its balance sheet characteristics and its exposure to general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks, and forecast the systemic relevance on a quarterly basis. Our empirical findings reveal time-varying risk channels and firms’ specific roles as risk transmitters and/or risk recipients.     

Aggregate volatility expectations and threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.     

Jumps and betas: A new framework for disentangling and estimating systematic risks

We provide a new theoretical framework for disentangling and estimating the sensitivity towards systematic diffusive and jump risks in the context of factor models. Our estimates of the sensitivities towards systematic risks, or betas, are based on the notion of increasingly finer sampled returns over fixed time intervals. We show consistency and derive the asymptotic distributions of our estimators. In an empirical application of the new procedures involving high-frequency data for forty individual stocks, we find that the estimated monthly diffusive and jump betas with respect to an aggregate market portfolio differ substantially for some of the stocks in the sample.     

An adjustment procedure for predicting systematic risk

This paper looks at the currently available beta adjustment techniques and suggests a multiple root-linear model to adjust for the regression tendency of betas. Our empirical investigate on indicates that cross-sectional betas are not normally distributed, but their distribution tends to normal after a square-root transformation. The evidence from the Box-Cox regression model and the multivariate normality observed among betas after the transformation, make the functional form of our model correct. Also, we observe that the disturbance term of the multiple root-linear model is well behaved. These findings make the ordinary least squares estimates unbiased and efficient. Finally, the mean square and extreme errors are found to be lower when our adjustment procedure is used vis-à-vis the existing procedures.     

Forecasting value at risk with intra-day return curves

Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods such as those based on realized volatility depend primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this study, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast value at risk (VaR). Our methods are based on the recently proposed functional generalized autoregressive conditionally heteroscedastic (GARCH) models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can be used to forecast intra-day VaR curves, which we considered and studied with companion backtests to evaluate the quality of these intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we forecast the one-day-ahead daily and intra-day VaR with the proposed methods and various benchmark models. The empirical results suggested that the functional GARCH models estimated based on the overnight cumulative intra-day return curves exhibited competitive performance with benchmark models for daily risk management, and they produced valid intra-day VaR curves.     

Comparing forecasts from fixed and variable coefficient models: The case of money demand

In this paper we introduce a class of tentatively plausible, fixed-coefficient models of money demand and evaluate their forecast performance. When these models are reestimated allowing all coefficients to vary over time, the forecasting performance improves dramatically. Aside from offering insights about improved methods of analyzing time series data, the most promising direct use for point estimates derived from time-varying coefficients is as an aid in calibrating proposed models of the kind discussed here.     

操纵性应计模型的一个改进——基于动态面板估计的实证分析

使用动态面板对操纵性应计模型进行估计可以克服截面数据模型的内生性估计偏误。采用1999—2011年中国A股市场相关数据,对动态面板模型和其他模型的检测能力进行比较,结果表明：在对第Ⅱ类错误进行检验时,动态面板模型检测能力显著优于其他模型;在对第1类错误、审计师意见检验时,动态面板模型检测能力与其他模型接近。     

Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall

We estimate several GARCH- and Extreme Value Theory (EVT)-based models to forecast intraday Value-at-Risk (VaR) and Expected Shortfall (ES) for S&P 500 stock index futures returns for both long and short positions. Among the GARCH-based models we consider is the so-called Autoregressive Conditional Density (ARCD) model, which allows time-variation in higher-order conditional moments. ARCD model with time-varying conditional skewness parameter has the best in-sample fit among the GARCH-based models. The EVT-based model and the GARCH-based models which take conditional skewness and kurtosis (time-varying or otherwise) into account provide accurate VaR forecasts. ARCD model with time-varying conditional skewness parameter seems to provide the most accurate ES forecasts.     

股权结构与公司绩效多模型比较与分析

目前关于股权结构与公司绩效关系的各种研究结论差异巨大，这与研究样本选取、指标选择、乃至研究方法和模型选择都有很大关系。本文选用2004年1091个A股上市公司数据，尝试了多种模型和指标进行了比较分析，结果发现：公司绩效与国有股比例、股权集中度呈显著的“U”形相关，但与“一股独大”的程度（用第一大股东持股比例与第二大股东持股比例的比值衡量）负相关。     

Technical efficiency in competing panel data models: a study of Norwegian grain farming

Estimation of technical efficiency is widely used in empirical research using both cross-sectional and panel data. Although several stochastic frontier models for panel data are available, only a few of them are normally applied in empirical research. In this article we chose a broad selection of such models based on different assumptions and specifications of heterogeneity, heteroskedasticity and technical inefficiency. We applied these models to a single dataset from Norwegian grain farmers for the period 2004–2008. We also introduced a new model that disentangles firm effects from persistent (time-invariant) and residual (time-varying) technical inefficiency. We found that efficiency results are quite sensitive to how inefficiency is modeled and interpreted. Consequently, we recommend that future empirical research should pay more attention to modeling and interpreting inefficiency as well as to the assumptions underlying each model when using panel data.     

Structural analysis of portfolio risk using beta impulse response functions

We estimate the data generating process of daily excess returns of 20 major German stocks in a CAPM framework with time varying betas. Our sample spans a 23 year period from 1974 to 1996. An asymmetric dependence of volatility on lagged innovations is taken into account. We introduce beta impulse response functions to shed light on the structural implications of systematic risk associated with competing volatility models. The dependence of beta on news is characterized with respect to different sources (asset specific vs. market general news). The empirical results suggest that negative news emerging from the market involve a stronger impact on beta relative to positive news. Concerning firm specific news the opposite relation is found for the majority of the analysed data sets.     

Methods for Scalar‐on‐Function Regression

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images and so on are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorising the basic model types as linear, non‐linear and non‐parametric. We discuss publicly available software packages and illustrate some of the procedures by application to a functional magnetic resonance imaging data set.     

Statistical Issues in fMRI for Brain Imaging

Functional magnetic resonance imaging is a technique developed in the last decade and used in the fields of cognitive psychology and neuroscience, among others, to study the processes underlying the working of the human brain. In this paper we examine some of the statistical issues in functional magnetic resonance imaging for brain research. We start by giving a brief introduction to the physics of magnetic resonance imaging. Using a psychological experiment as a case study, we then describe questions of design and statistical analysis. The data obtained from functional magnetic resonance imaging studies are of a highly complex nature, displaying both spatial and temporal correlation, as well as high levels of noise from different sources. Given this, the scope for statistics is vast, and is not limited to simple analysis of the data, once collected.     

How Do Alphas and Betas Move? Uncertainty,Learning and Time Variation in Risk Loadings*

I employ a parsimonious model with learning, but without conditioning information, to extract time‐varying measures of market‐risk sensitivities, pricing errors and pricing uncertainty. The evolution of these quantities has interesting implications for macroeconomic dynamics. Parameters estimated for US equity portfolios display significant low‐frequency fluctuations, along patterns that change across size and book‐to‐market stocks. Time‐varying betas display superior predictive accuracy for returns against constant and rolling‐window OLS estimates. As to the relationship of betas with business‐cycle variables, value stocks’ betas move pro‐cyclically, unlike those of growth stocks. Investment growth, rather than consumption, predicts the betas of value and small‐firm portfolios.     

中国城市正在向功能专业化转型吗?——基于跨国公司区位战略的透视

发达国家城市正向功能专业化转型,企业组织形式变革直接导致了城市的功能转型。在功能专业化背景下,城市间将出现更多的信息联系、决策联系以及资本联系,更需要面对面的交流。一些城市专业化高端功能,成为区域的控制中心,另一些城市则承担价值链的低端功能。本文基于全球500强跨国公司在华投资数据,探讨中国城市功能专业化趋势。在1979～2008年间,跨国公司在地理和功能上都显著地进行了扩张。跨国公司相同功能和互补功能集聚在相同城市,跨国公司的渐进式多次投资导致了公司内不同功能在相同城市的地理集聚。本文结果表明,中国城市尤其是在城市体系高端的城市具有吸引价值链高端功能的竞争力,呈现一定功能专业化趋势。跨国公司在东道国内部的功能布局提供了考察城市经济转型的独特视角。     

An analysis of the capital asset pricing model in the Egyptian stock market

This paper offers evidence confirming the validity of applying modern portfolio theory and capital asset pricing models to the emerging stock market of Egypt. The results indicate that market risk, as measured by beta and preference for skewness, seems to play a significant role in the returns dynamics in the Egyptian stock market. There is a significant and positive premium for companies with positive skewness. With regard to the return-risk trade off, the results indicate that a portfolio that was based on consumer staples and financial companies (mainly banks) with low betas had outperformed a portfolio containing construction, materials, hotels, and weaving companies with larger betas. Historically, the government's nationalizations that took place, between the mid fifties to the mid sixties, had adversely affected companies in the industrial and construction sectors more than consumer staples companies and banks. This could explain why lower beta companies were observed more in consumer staples, banks, and pharmaceuticals.     

The association of default risk factors with the systematic risk of corporate bonds

This study examines the association between bond betas and default risk factors. We find that both long-term debt and the relative ratio of long-term debt to short-term debt increase the bond beta; two measures of profitability, net income/total assets and EBIT/total assets and a cash flow measure of cash flow from operations/total assets decrease the bond beta. A proxy measure of standard deviation of returns is also significantly negatively related to bond betas, confirming the prediction from the option pricing model. In addition, by using new cash flow measures in the discriminant analysis, we improve on the successful prediction rate of bond ratings.