Forecasting the Correlation Structure of German Stock Returns: A Test of Firm-Specific Factor Models

This paper evaluates the performance of various factor models with firm-specific variables in forecasting correlation matrices at the German stock market. We investigate forecasts of correlations for a comprehensive sample and a sample of blue chips and analyse the impact of stock market crashes on the forecasting accuracy. Our empirical results show that the multi-factor models do not generally produce better forecasts than 'naive' models. Specifically, the traditional industry mean model significantly outperforms all other techniques in most of the time periods.     

Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns

In this article we examine the structural stability of predictiveregression models of U.S. quarterly aggregate real stock returnsover the postwar era. We consider predictive regressions modelsof S&P 500 and CRSP equal-weighted real stock returns basedon eight financial variables that display predictive abilityin the extant literature. We test for structural stability usingthe popular Andrews SupF statistic and the Bai subsample procedurein conjunction with the Hansen heteroskedastic fixed-regressorbootstrap. We also test for structural stability using the recentlydeveloped methodologies of Elliott and Müller, and Baiand Perron. We find strong evidence of structural breaks infive of eight bivariate predictive regression models of S&P500 returns and some evidence of structural breaks in the threeother models. There is less evidence of structural instabilityin bivariate predictive regression models of CRSP equal-weightedreturns, with four of eight models displaying some evidenceof structural breaks. We also obtain evidence of structuralinstability in a multivariate predictive regression model ofS&P 500 returns. When we estimate the predictive regressionmodels over the different regimes defined by structural breaks,we find that the predictive ability of financial variables canvary markedly over time.     

An Examination of Alternative Factor Models in UK Stock Returns

This paper examines the mean-variance efficiency of a number offactor models in UK stock returns. The paper also explores, using theapproach of MacKinlay (1995), whether missing risk factors ornonrisk-based explanations best explain the pricing errors of thedifferent factor models. The evidence in the paper suggests that themean-variance efficiency of each factor model is rejected and missing riskfactors are unable to explain the pricing errors of any of the models.Some nonrisk-based explanations, which posit a wide spread in abnormalreturns, may be a more plausible source of explaining the pricing errorsof the factor models.     

经济政策不确定性对原油市场收益影响的分位关系研究

构建分位数分位数回归模型,依据1987年6月至2020年10月数据,考量经济政策不确定性对原油市场收益的异质性影响.结果表明:经济政策不确定性对原油市场收益在大多数分位点具有抑制效应,且这种影响在原油市场低迷时更加明显.在三类细分经济政策不确定性冲击中,货币政策不确定性和贸易政策不确定性对原油收益的影响在原油市场繁荣时占主导地位,而原油市场处于低迷状态时对财政政策不确定性的变化更加敏感.此外,经济政策不确定性对原油市场收益的影响程度在金融危机爆发后明显增强.鉴此,原油市场利益相关者在金融危机期间应对经济政策不确定的变化应更加谨慎.     

Some Tests of the Risk-Return Relationship Using Alternative Asset Pricing Models and Observed Expected Returns

This study examines the performance of three asset pricing models: the CAPM, the APT and the UAPT using observed expected returns from a three-phase dividend discount model with Value Line analyst estimates of future company-level earnings, dividends and growth rates. Our study is the first we know of to test the three major asset pricing models using observed expected returns. Our results are similar to prior research using ex post (realized) returns in that we find that the UAPT using macroeconomic factors is the best performing model, followed by the APT and the CAPM. However, our results also suggest that the importance of macroeconomic factors is much greater to expected returns than to realized returns, and the corresponding R2 values for models using expected returns are much higher than for models using realized returns. Combining our results for the UAPT with those of Marston and Harris (1993) for the CAPM suggests that these models are more successful in tests using observed expected returns than in tests using realized returns as proxies for expected returns. Unit root tests suggest that monthly observed expected returns follow the classic random walk without drift model while monthly realized returns do not.     

Arbitrage and the Evaluation of Linear Factor Models in UK Stock Returns

I examine the impact of the no arbitrage restriction on the estimation and evaluation of linear factor models in UK stock returns. The no arbitrage restriction reduces volatility and eliminates most of the negative values of the fitted stochastic discount factor models. All of the factor models are rejected and there are significant differences in the pricing performance between models under the no arbitrage restriction. The no arbitrage restriction can have a significant impact on both the parameter estimates and pricing errors for some models.     

CAPM, Higher Co‐moment and Factor Models of UK Stock Returns

In this paper we examine the variables that explain the cross‐section of UK stock returns. Previous studies have found that the CAPM beta has moderate or even insignificant explanatory power once the Fama French factors are included. However, we control for different realised risk premia in up and down markets by using the same methodology as Pettengill, Sundaram and Mathur (1995). Unlike previous work, we find that beta is highly significant in explaining the cross‐section of UK stock returns and more importantly remains significant even when the Fama French factors are included in the cross‐sectional regressions. We also investigate whether higher co‐moments (co‐skewness and co‐kurtosis) have any explanatory power but find that empirical support is weaker.     

高龄人口死亡率预测模型的比较与选择

高龄人口死亡率预测模型是人口预测、养老金成本和债务评估以及长寿风险度量与管理的基础。我国大陆地区高龄人口死亡数据量少、数据波动性大,如何选择适合我国高龄数据特点的死亡率预测模型,是重要的研究课题。本文在归纳总结死亡率预测模型研究进展的基础上,先采用数据较为充分的台湾地区高龄死亡数据,选用Lee-Carter、CBD、贝叶斯分层模型等八种死亡率模型,对模型的拟合效果、预测效果和稳健性做出比较。在此基础上,基于修正和平滑后的我国大陆人口死亡数据,采用CBD模型和贝叶斯分层模型建模和预测。结果显示:贝叶斯分层模型能捕捉我国大陆高龄死亡率数据的历史波动,预测区间能够涵盖全部死亡率的真实值,但预测区间过宽,生存曲线不收敛;相比之下,CBD模型对我国大陆地区高龄死亡率的拟合和预测较好,预测区间和生存曲线合理。在长寿风险度量中,建议采用CBD模型。     

Assessing the Real Estate Pricing Puzzle: A Diagnostic Application of the Stochastic Discounting Factor to the Distribution of REIT Returns

This article applies a general asset-pricing framework and the volatility bounds methodology of Hansen and Jagannathan (1991) to REIT returns. The state of real estate asset pricing remains somewhat of a puzzle relative to the identification of state variables and the structural form of models. This article offers a framework whereby real estate asset-pricing models and data can be diagnosed to answer questions about the shortcomings. In addition, several nominated discount processes are investigated for success in pricing real estate securities. Although the nominated specifications demonstrate some success in satisfying the restrictions on the first and second moments of the real estate returns distribution, they do not successfully price the securities under a no-arbitrage condition. This result calls into question previous real estate performance studies that employ these risk-adjustment processes.     

The Stability of Factor Models of Interest Rates

The daily term structure of interest rates is filtered to reducethe influence of cross-correlations and autocorrelations onits factors. A three-factor model is fitted to the filtereddata. We perform statistical tests, finding that factor loadingsare unstable through time for daily data. This finding is notdue to the presence of outliers nor to the selected number offactors. Such an instability problem can be solved when applyingthe factor analysis on multivariate scaled residuals, filteredusing a nonparametric technique based on functional gradientdescent.     

Forecasting models for the Chinese macroeconomy in a data-rich environment: Evidence from large dimensional approximate factor models with mixed-frequency data

Our paper contributes to the nascent literature on forecasting the Chinese macroeconomy in a data-rich environment. We perform a horse race among a large set of traditional models and two classes of factor models with 251 monthly and 34 quarterly macroeconomic variables over the period from January 2002 to June 2018. We find evidence that mixed-frequency factor models provide superior forecasts of the CPI, RPI, investment, and consumption when compared to the simple benchmark. During the Global Financial Crisis period, we find little evidence of superiority of factor models over the simple benchmark AR(p) model.     

An application of a random level shifts model to the volatility of Peruvian stock and exchange rate returns

The literature has shown that the volatility of stock and forex rate market returns shows the characteristic of long memory. Another fact that is shown in the literature is that this feature may be spurious and volatility actually consists of a short memory process contaminated with random level shifts (RLS). In this paper, we follow recent econometric approaches estimating an RLS model to the logarithm of the absolute value of stock and forex returns. The model consists of the sum of a short-term memory component and a component of level shifts. The second component is specified as the cumulative sum of a process that is zero with probability ‘1-alpha’ and is a random variable with probability ‘alpha’. The results show that there are level shifts that are rare, but once they are taken into account, the characteristic or property of long memory disappears. Also, the presence of General Autoregressive Conditional Heteroscedasticity (GARCH) effects is eliminated when included or deducted level shifts. An exercise of out-of-sample forecasting shows that the RLS model has better performance than traditional models for modelling long memory such as the models ARFIMA (p,d,q).     

Evaluating the effectiveness of model specifications and estimation approaches for empirical accounting-based valuation models

This study considers the effectiveness of different model specifications and estimation approaches for empirical accounting-based valuation models in the UK. Primarily, we are interested in the accounting determinants of market value and, in particular, whether accounting-based valuation models can be estimated that not only have in-sample explanatory power but also potentially can be used as a tool of financial statement analysis in developing useful estimates of value out-of-sample. This requires models to be estimated on one sample, and tested for effectiveness on a different sample. Then, issues of model specification arise, together with choosing between methods of estimating the empirical models, in identifying the effectiveness of each combination. Using the criteria of bias and accuracy to capture effectiveness, we suggest estimation methods and models that, overall, provide the most effective models in this context.     

FX volatility forecasts and the informational content of market data for volatility

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of ‘pure’ time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from ‘backing out’ volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the ‘mixed’ models incorporating market data for currency volatility perform best most of the time.     

基于人工智能的区域现金需求预测模型研究——以南阳市为例

当前,我国非现金支付对现金支付的替代作用日益显著,但现金支付在居民日常支付中仍占据重要地位。在此背景下,如何精确地预测区域现金需求,保证满足区域现金流通需要已成为一项重要课题。通过借鉴国内外机器学习、神经网络等人工智能技术,基于地区现金投放历史数据,使用岭回归、MLP和LSTM三种模型,构建RR-MLP-LSTM加权组合模型实现了区域现金需求预测,对于统筹货币发行工作、积极防范和化解货币供应风险具有较为重要的借鉴意义。     

Constructing and Testing Alternative Versions of the Fama–French and Carhart Models in the UK

This paper constructs and tests alternative versions of the Fama–French and Carhart models for the UK market with the purpose of providing guidance for researchers interested in asset pricing and event studies. We conduct a comprehensive analysis of such models, forming risk factors using approaches advanced in the recent literature including value‐weighted factor components and various decompositions of the risk factors. We also test whether such factor models can at least explain the returns of large firms. We find that versions of the four‐factor model using decomposed and value‐weighted factor components are able to explain the cross‐section of returns in large firms or in portfolios without extreme momentum exposures. However, we do not find that risk factors are consistently and reliably priced.