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Factor Investing: A Bayesian Hierarchical Approach 要素投资:贝叶斯层次方法

ERN: Forecasting Techniques (Topic) Pub Date : 2019-01-31 DOI: 10.2139/ssrn.3326617

Guanhao Feng, Jingyu He

引用次数: 12

A Novel Cluster HAR-Type Model for Forecasting Realized Volatility 一种预测已实现波动率的聚类har模型

ERN: Forecasting Techniques (Topic) Pub Date : 2019-01-24 DOI: 10.2139/ssrn.3342090

Xingzhi Yao, M. Izzeldin, Zhenxiong Li

引用次数: 9

Forecasting and Nowcasting Emerging Market GDP Growth Rates: The Role of Latent Global Economic Policy Uncertainty and Macroeconomic Data Surprise Factors 预测和预测新兴市场GDP增长率:潜在的全球经济政策不确定性和宏观经济数据意外因素的作用

ERN: Forecasting Techniques (Topic) Pub Date : 2018-12-10 DOI: 10.2139/ssrn.3298924

Oğuzhan Çepni, I. Guney, Norman R. Swanson

{"title":"Forecasting and Nowcasting Emerging Market GDP Growth Rates: The Role of Latent Global Economic Policy Uncertainty and Macroeconomic Data Surprise Factors","authors":"Oğuzhan Çepni, I. Guney, Norman R. Swanson","doi":"10.2139/ssrn.3298924","DOIUrl":"https://doi.org/10.2139/ssrn.3298924","url":null,"abstract":"In this paper, we assess the predictive content of latent economic policy uncertainty and data surprises factors for forecasting and nowcasting GDP using factor-type econometric models. Our analysis focuses on five emerging market economies, including Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse-race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models (DFMs) that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122929617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Break Risk 破坏的风险

ERN: Forecasting Techniques (Topic) Pub Date : 2018-08-24 DOI: 10.2139/ssrn.3238226

Simon C. Smith, A. Timmermann

引用次数: 23

Forecasting Commodity Futures Returns: An Economic Value Analysis of Macroeconomic vs. Specific Factors 预测商品期货收益:宏观经济与特定因素的经济价值分析

ERN: Forecasting Techniques (Topic) Pub Date : 2018-07-26 DOI: 10.2139/ssrn.3225611

Massimo Guidolin, Manuela Pedio

{"title":"Forecasting Commodity Futures Returns: An Economic Value Analysis of Macroeconomic vs. Specific Factors","authors":"Massimo Guidolin, Manuela Pedio","doi":"10.2139/ssrn.3225611","DOIUrl":"https://doi.org/10.2139/ssrn.3225611","url":null,"abstract":"We test whether three well-known commodity-specific variables (basis, hedging pressure, and momentum) may improve the predictive power for commodity futures returns of models otherwise based on macroeconomic factors. We compute recursive, out-of-sample forecasts for fifteen monthly commodity futures return series, when estimation is based on a stepwise model selection approach under a probability-weighted regime-switching regression that identifies different volatility regimes. Comparisons with an AR(1) benchmark show that the inclusion of commodity-specific factors does not improve the forecasting power. We perform a back-testing exercise of a mean-variance investment strategy that exploits any predictability of the conditional risk premium of commodities, stocks, and bond returns, also taking into account transaction costs caused by portfolio rebalancing. The risk-adjusted performance of this strategy does not allow us to conclude that any forecasting approach outperforms the others. However, there is evidence that investment strategies based on commodity-specific predictors outperform the remaining strategies in the high-volatility state.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122325037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Commodity Price Movements and Banking Crises 商品价格变动与银行危机

ERN: Forecasting Techniques (Topic) Pub Date : 2018-07-01 DOI: 10.5089/9781484366776.001

M. Eberhardt, A. Presbitero

引用次数: 17

Stochastic Volatility Models with ARMA Innovations an Application to G7 Inflation Forecasts 具有ARMA创新的随机波动率模型在G7通胀预测中的应用

ERN: Forecasting Techniques (Topic) Pub Date : 2018-06-28 DOI: 10.2139/ssrn.3222423

Bo Zhang, J. Chan, Jamie L. Cross

引用次数: 27

On Attempts to Use Models Incorporating Long-Range Dependence in Long-Term Volatility Forecasting 在长期波动率预测中尝试使用包含长期依赖的模型

ERN: Forecasting Techniques (Topic) Pub Date : 2018-05-25 DOI: 10.2139/ssrn.3185118

Nicholas Reitter

引用次数: 0

The Economic Origin of Treasury Excess Returns: A Cycles and Trend Explanation 国债超额收益的经济根源:周期与趋势解释

ERN: Forecasting Techniques (Topic) Pub Date : 2018-05-23 DOI: 10.2139/ssrn.3183653

R. Rebonato, Takumi Hatano

{"title":"The Economic Origin of Treasury Excess Returns: A Cycles and Trend Explanation","authors":"R. Rebonato, Takumi Hatano","doi":"10.2139/ssrn.3183653","DOIUrl":"https://doi.org/10.2139/ssrn.3183653","url":null,"abstract":"In this paper we try to understand the economic explanation of the difference in predictability afforded by the old and the new-generation return-predicting factors. To do so, first we show that the Cieslak-Povala (2010) approach can be expressed in terms of a conditional prediction of where the level and the slope of the yield curve should be, given long-term inflation. We then explore whether this interpretation is valid, or whether, as Cochrane (2015) argues, the Cieslak-Povala factor simply owes its effectiveness to its acting as a de-trender. We answer this question by decomposing excess returns into low- and high-frequency components; by showing that the old and new return-predicting factors capture very different periodicities of the return power spectrum; and by showing that a high speed of mean-reversion is required for the high-frequency part of the spectrum. We conclude that creating strongly mean-reverting cycles is key to predicting excess returns effectively, and explore to what extent the Cieslak-Povala approach may be 'special' in this respect. \u0000We give a financial interpretation to the low- and high-frequency sources of excess returns, and, based on the understanding this decomposition affords, we show how to build almost by inspection a whole class of extremely parsimonious, robust and financially-motivated return-predicting factors which forecast in- and out-of-sample returns as well or better than factors built using many more variables.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126900169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Stock Market Prediction Using Time Series Analysis 股票市场预测使用时间序列分析

ERN: Forecasting Techniques (Topic) Pub Date : 2018-02-07 DOI: 10.2139/ssrn.3168423

Kamalakannan J, I. Sengupta, Snehaa Chaudhury

引用次数: 3