Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

Forward Rates,Monetary Policy and the Economic Cycle

The short end of the yield curve incorporates essential information to forecast central banks' decisions, but in a biased manner. This article proposes a new method to forecast the Fed and the European Central Bank's decision rate by correcting the swap rates for their cyclical economic premium, using an affine term structure model. The corrected yields offer a higher out‐of‐sample forecasting power than the yields themselves. They also deliver forecasts that are either comparable or better than those obtained with a factor‐augmented vector autoregressive model, underlining the fact that yields are likely to contain at least as much information regarding monetary policy as a dataset composed of economic data series. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

This paper provides clear‐cut evidence that the out‐of‐sample VaR (value‐at‐risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime‐switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime‐switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality‐forecasting models be associated with real‐world trends in health‐related variables? Does inclusion of health‐related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle‐related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models. Copyright © 2014 John Wiley & Sons, Ltd.     

Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.     

基于时间序列的灰色预测技术在估产模型中的应用

在建立估产模型过程中，引进基于时间序列的灰色预测技术，通过对样本点建立基于时间序列的灰色预测模型和常规的多元线性回归气象模型的分析比较，试图找到一种计算简单，数据要求少而精度较高，时效性较好的建模方法，为时间序列预测在农作物估产方面的应用作出一点探索。     

祁连山地区高分辨率气温降水量分布模型

充分利用气温、降水量空间统计分析的结果，通过在模型中引入坡度（SL）、坡向（SD）变量，对祁连山中东部地区气温、降水量空间变化模式进行尺度下移，得到了该地区具有多尺度特性的高空间分辨率气温、降水量的分析模型：T=a＋bH—kT cos SD sin SL，P=（a＋b ln H＋C ln y）（1＋kp sin SL）；进而计算了该地区7月份和年平均气温的分布式模型和年降水量分布式模型，得到了相应的栅格地图．验证结果表明这两个模型可以与山地冰川和森林分布区的气候分析资料很好地匹配．根据年降水量栅格地图的统计分析，将本区域内的最大降水高度带确定在4500m．     

Bioaccumulation processes in ecosystems

The fate of environmental pollutants — the various isotopes of elements, and inorganic or organic compounds — is a fundamental aspect of ecology and ecotoxicology, and bioaccumulation is a phenomenon often discussed in this context. Human activities have drastically altered natural concentrations of many substances in the environment and added numerous new chemicals. An understanding of the processes of bioaccumulation is important for several reasons. 1) Bioaccumulation in organisms may enhance the persistence of industrial chemicals in the ecosystem as a whole, since they can be fixed in the tissues of organisms. 2) Stored chemicals are not exposed to direct physical, chemical, or biochemical degradation. 3) Stored chemicals can directly affect an individual's health. 4) Predators of those organisms that have bioaccumulated harmful substances may be endangered by food chain effects. While former theories on the processes of bioaccumulation focused on single aspects that affect the extent of accumulation (such as the trophic level within the food chain or the lipophilicity of the chemical), modern theories are based on compartmental kinetics and the integration of various environmental interactions. Concepts include results from quantitative structure-activity relationships (QSAR), pharmacokinetics, ecophysiology and general biology, molecular genetic aspects and selection, and finally the structure of communities and man-made alterations in them.