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A Note on the Robustness of Diebold and Li's Forecasting Results 关于Diebold和Li预测结果稳健性的说明
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-05-21 DOI: 10.2139/ssrn.3181355
A. Simonsen, João Marco Braga da Cunha
{"title":"A Note on the Robustness of Diebold and Li's Forecasting Results","authors":"A. Simonsen, João Marco Braga da Cunha","doi":"10.2139/ssrn.3181355","DOIUrl":"https://doi.org/10.2139/ssrn.3181355","url":null,"abstract":"The paper by Diebold and Li (2006) has become a benchmark in the yield curve forecasting literature, mostly owing to its excellent out-of-sample results. In this note we investigate the robustness of these outcomes in two different ways: (i) in terms of the arbitrary choices in their forecasting experimental design, and (ii) in updated samples up to 2015. In both cases, the fragility of their out-of-sample results became evident. In addition, we propose a novel decomposition method, which reveals that their positive results are unlikely to be caused by any intentionally built feature within their model. Overall, the evidence suggests that the forecasting ability of Dielbold and Li's model is questionable.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82649627","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: 0
Stock Return Predictability and Seasonality 股票收益的可预测性和季节性
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-05-18 DOI: 10.2139/ssrn.3180992
Keunsoo Kim, Jinho Byun
{"title":"Stock Return Predictability and Seasonality","authors":"Keunsoo Kim, Jinho Byun","doi":"10.2139/ssrn.3180992","DOIUrl":"https://doi.org/10.2139/ssrn.3180992","url":null,"abstract":"An examination of the Shiller cyclically adjusted pricing-earnings (CAPE) ratio reveals its forecasting power for 12-month CRSP equally weighted (EW) excess returns and value weighted (VW) excess returns. The 12-month EW excess returns following low CAPE ratios are, on average, 20.7% higher than those following high CAPE ratios for the period of 1927-2016. This dichotomy in the Shiller CAPE ratio has a more reliable predictability than the January barometer. Previous studies report that the Halloween indicator was weak or negative in the US stock market prior to the 1950s. We find that the Halloween effect is strongly present following high CAPE ratios, even for the period of 1926-1971. Our results recommend a practical investment strategy. More specifically, if the CAPE ratio in September is lower than the 36-month median of the CAPE ratio, invest in stock markets from November to October of the following year; otherwise, invest for six months from November to April and sell in May and go away.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77410075","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: 1
A Heuristic for Approximating Extreme Negative Price Returns in Financial Markets 金融市场中逼近极端负价格收益的启发式方法
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-05-11 DOI: 10.2139/ssrn.2907459
J. Manhire
{"title":"A Heuristic for Approximating Extreme Negative Price Returns in Financial Markets","authors":"J. Manhire","doi":"10.2139/ssrn.2907459","DOIUrl":"https://doi.org/10.2139/ssrn.2907459","url":null,"abstract":"This paper argues that one can calculate the probability of an asset's price displacement in a specific direction assuming the asset complies with the physical principle of least action. It first suggests that the price displacement of a financial asset is essentially dampened harmonic motion and then applies physical principles such as the Lagrangian and stationary action to analyze this motion. From this analysis, the paper constructs a method to predict the probability of an asset's price displacement in both magnitude and direction. Initial tests show that the method produces accurate probability predictions.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73846946","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: 1
Long Horizon Predictability: An Asset Allocation Perspective. 长期可预测性:资产配置的视角。
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-04-30 DOI: 10.2139/ssrn.2433440
Abraham Lioui, Patrice Poncet
{"title":"Long Horizon Predictability: An Asset Allocation Perspective.","authors":"Abraham Lioui, Patrice Poncet","doi":"10.2139/ssrn.2433440","DOIUrl":"https://doi.org/10.2139/ssrn.2433440","url":null,"abstract":"We analyze the effects of asset return predictability at various horizons on an individual's portfolio strategy and welfare gains as measured by a certainty equivalent return rate, for long term investors. We use a method to account for long horizon predictability that does not make violence to the data, and two alternative OLS procedures that allow investors to capture the differential information contained in various period returns. More specifically, our second procedure exploits the information present in the term structure of \"forward\" equity risk premia. We show that, adopting this procedure, the investor's welfare gain may be substantial relative to that obtained from using short horizon predictability only. Consequently, investors are better off by simultaneously using information in short and long horizon returns.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83519928","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
The Equity Risk Premium and the Low Frequency of the Term Spread 股票风险溢价与期限价差的低频率
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-04-03 DOI: 10.2139/ssrn.3030760
Gonçalo Faria, Fabio Verona
{"title":"The Equity Risk Premium and the Low Frequency of the Term Spread","authors":"Gonçalo Faria, Fabio Verona","doi":"10.2139/ssrn.3030760","DOIUrl":"https://doi.org/10.2139/ssrn.3030760","url":null,"abstract":"We extract cycles in the term spread (TMS) and study their role for predicting the equity risk premium (ERP) using linear models. The low frequency component of the TMS is a strong and robust out-of-sample ERP predictor. It obtains out-of-sample R-squares (versus the historical mean benchmark) of 1.98% and 22.1% for monthly and annual data, respectively. It forecasts well also during expansions and outperforms several variables that have been proposed as good ERP predictors. Its predictability power comes exclusively from the discount rate channel. Contrarily, the high and business-cycle frequency components of the TMS are poor out-of-sample ERP predictors.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78510135","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: 2
High Frequency Trading and Co-Movement in Financial Markets 金融市场的高频交易与协同运动
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-03-23 DOI: 10.2139/ssrn.2827148
Laura Malceniece, Kārlis Malcenieks, Tālis J. Putniņš
{"title":"High Frequency Trading and Co-Movement in Financial Markets","authors":"Laura Malceniece, Kārlis Malcenieks, Tālis J. Putniņš","doi":"10.2139/ssrn.2827148","DOIUrl":"https://doi.org/10.2139/ssrn.2827148","url":null,"abstract":"Using the staggered entry of Chi-X in 12 European equity markets as a source of exogenous variation in high frequency trading (HFT), we find that HFT causes significant increases in co-movement in returns and in liquidity. About one-third of the increase in return co-movement is due to faster diffusion of market-wide information. We attribute the remaining two-thirds to correlated trading strategies of HFTs. The increase in liquidity co-movement is consistent with HFT liquidity providers being better able to monitor other stocks and adjust their liquidity provision accordingly. Our findings suggest a channel by which HFT impacts the cost of capital.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72843396","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: 70
Forecasting Economic Time Series Using Score-Driven Dynamic Models with Mixed-Data Sampling 用混合数据抽样的分数驱动动态模型预测经济时间序列
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-03-21 DOI: 10.2139/ssrn.3145072
P. Gorgi, S. J. Koopman, Mengheng Li
{"title":"Forecasting Economic Time Series Using Score-Driven Dynamic Models with Mixed-Data Sampling","authors":"P. Gorgi, S. J. Koopman, Mengheng Li","doi":"10.2139/ssrn.3145072","DOIUrl":"https://doi.org/10.2139/ssrn.3145072","url":null,"abstract":"Abstract We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87000189","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: 11
A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data 高频外汇数据的协同预测模型
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-03-19 DOI: 10.24818/18423264/52.1.18.18
S. Ebrahimijam, Cahit Adaoglu, Korhan K. Gokmenoglu
{"title":"A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data","authors":"S. Ebrahimijam, Cahit Adaoglu, Korhan K. Gokmenoglu","doi":"10.24818/18423264/52.1.18.18","DOIUrl":"https://doi.org/10.24818/18423264/52.1.18.18","url":null,"abstract":"In this study, we develop a synergistic forecasting model using the information fusion approach. By using high frequency (one-minute) foreign exchange (FX) data, the model fuses two standalone models, namely the technical analysis structural model and the intra-market model. Subsequently, the outputs are fed into a unique modified extended Kalman filter whose functional parameters are estimated dynamically by using an artificial neural network. The synergistic model is tested on four currency pairs that dominate the FX market. In terms of forecasting performance, both root mean squared error and correct directional change performance results show that the synergistic model is statistically outperform and superior to each of the both standalone models as well as to the benchmark random walk model in the literature.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88514079","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: 1
Predictive Power of Markovian Models: Evidence from U.S. Recession Forecasting 马尔可夫模型的预测能力:来自美国经济衰退预测的证据
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-03-14 DOI: 10.2139/ssrn.3152699
R. Tian, Gang Shen
{"title":"Predictive Power of Markovian Models: Evidence from U.S. Recession Forecasting","authors":"R. Tian, Gang Shen","doi":"10.2139/ssrn.3152699","DOIUrl":"https://doi.org/10.2139/ssrn.3152699","url":null,"abstract":"This paper brings new evidence of predicting the U.S. recessions through Markovian models. The Markovian models, including the Hidden Markov and Markov models, incorporate the temporal autocorrelation of binary recession indicators in a more traditional and natural way. Considering interest rates and spreads, stock prices, monetary aggregates, and output as the candidate predictors, we examine the out-of-sample performance of the Markovian models in predicting the recessions one to twelve months ahead. The Markovian models are superior to the probit models in detecting a recession and capturing the recession duration. We find the \"one-month lag phenomenon\" that the best Markovian model supported by statistical model selection procedures can always recognize the onset of a recession one month after it starts. In addition, the yield spread continues to serve as the most e\u000ecient predictor variable in explaining business cycles.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83908803","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
Estimating the Term Structure with Linear Regressions: Getting to the Roots of the Problem 用线性回归估计期限结构:找到问题的根源
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2018-03-08 DOI: 10.2139/ssrn.3028075
A. Golinski, P. Spencer
{"title":"Estimating the Term Structure with Linear Regressions: Getting to the Roots of the Problem","authors":"A. Golinski, P. Spencer","doi":"10.2139/ssrn.3028075","DOIUrl":"https://doi.org/10.2139/ssrn.3028075","url":null,"abstract":"\u0000 Linear estimators of the affine term structure model are inconsistent since they cannot reproduce the factors used in estimation. This is a serious handicap empirically, giving a worse fit than the conventional ML estimator that ensures consistency. We show that a simple self-consistent estimator can be constructed using the eigenvalue decomposition of a regression estimator. The remaining parameters of the model follow analytically. Estimates from this model are virtually indistinguishable from that of the ML estimator. We apply the method to estimate various models of U.S. Treasury yields. These exercises greatly extend the range of models that can be estimated.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90940627","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: 5
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