ERN: Forecasting Techniques (Topic)最新文献

{"title":"On the Short-Term Predictability of Stock Returns: A Quantile Boosting Approach","authors":"Rıza Demirer, Christian Pierdzioch, Huacheng Zhang","doi":"10.2139/ssrn.2852477","DOIUrl":"https://doi.org/10.2139/ssrn.2852477","url":null,"abstract":"We study the predictability of stock returns using an iterative model-building approach known as quantile boosting. Examining alternative return quantiles that represent normal, bull and bear markets via recursive quantile regressions, we trace the predictive value of extensively studied predictors including the recently suggested short interest and sentiment variables. We find that short-term returns are predictable to some extent for extreme lower quantiles of the conditional distribution of returns. Interestingly, however, short-interest and sentiment variables do not add significant predictive power, challenging the recent findings on the predictive ability of short sellers for future cash flows and associated market returns.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123799832","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}

{"title":"Generalized Autoregressive Score Models in R: The GAS Package","authors":"David Ardia, Kris Boudt, Leopoldo Catania","doi":"10.18637/jss.v088.i06","DOIUrl":"https://doi.org/10.18637/jss.v088.i06","url":null,"abstract":"This paper presents the R package GAS for the analysis of time series under the generalized autoregressive score (GAS) framework of Creal, Koopman, and Lucas (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of non-linear models. The GAS package provides functions to simulate univariate and multivariate GAS processes, to estimate the GAS parameters and to make time series forecasts. We illustrate the use of the GAS package with a detailed case study on estimating the time-varying conditional densities of financial asset returns.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131776568","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}

{"title":"Idiosyncratic Risk, Costly Arbitrage, and the Cross-Section of Stock Returns","authors":"Jie Cao, Bing Han","doi":"10.2139/ssrn.1291626","DOIUrl":"https://doi.org/10.2139/ssrn.1291626","url":null,"abstract":"We test a new cross-sectional relation between expected stock return and idiosyncratic risk implied by the theory of costly arbitrage. If arbitrageurs find it more difficult to correct the mispricing of stocks with high idiosyncratic risk, there should be a positive (negative) relation between expected return and idiosyncratic risk for undervalued (overvalued) stocks. We combine several well-known anomalies to measure stock mispricing and proxy stock idiosyncratic risk using an exponential GARCH model for stock returns. We confirm that average stock returns monotonically increase (decrease) with idiosyncratic risk for undervalued (overvalued) stocks. Overall, our results support the importance of idiosyncratic risk as an arbitrage cost.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115765660","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}

{"title":"Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets","authors":"J. French","doi":"10.2139/ssrn.2629003","DOIUrl":"https://doi.org/10.2139/ssrn.2629003","url":null,"abstract":"The study adds an empirical outlook on the predicting power of using data from the future to predict future returns. The crux of the traditional Capital Asset Pricing Model (CAPM) methodology is using historical data in the calculation of the beta coefficient. This study instead uses a battery of Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the beta formula. The covariance of the portfolio and market returns are assumed to remain constant in the time-varying beta calculations. The data spans from 3 January 2005 to 29 December 2014. One ten-year, two five-year, and three three-year sample periods were used, for robustness, with ten different portfolios. Out of sample forecasts, mean absolute error (MAE) and mean squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural Network, and the standard market ex-post model. Find that the time-varying MGARCH and SGARCH beta performed better with out-of-sample testing than the other ex-ante models. Although the simplest approach, constant ex-post beta, performed as well or better within this empirical study.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"19 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124298439","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}

{"title":"Mixed-Frequency Multivariate GARCH","authors":"G. Dhaene, Wu Jianbin","doi":"10.2139/ssrn.2799569","DOIUrl":"https://doi.org/10.2139/ssrn.2799569","url":null,"abstract":"We introduce and evaluate mixed-frequency multivariate GARCH models for forecasting low-frequency (weekly or monthly) multivariate volatility based on high-frequency intra-day returns (at five-minute intervals) and on the overnight returns. The low-frequency conditional volatility matrix is modelled as a weighted sum of an intra-day and an overnight component, driven by the intra-day and the overnight returns, respectively. The components are specified as multivariate GARCH (1,1) models of the BEKK type, adapted to the mixed-frequency data setting. For the intra-day component, the squared high-frequency returns enter the GARCH model through a parametrically specified mixed-data sampling (MIDAS) weight function or through the sum of the intra-day realized volatilities. For the overnight component, the squared overnight returns enter the model with equal weights. Alternatively, the low-frequency conditional volatility matrix may be modelled as a single-component BEKK-GARCH model where the overnight returns and the high-frequency returns enter through the weekly realized volatility (defined as the unweighted sum of squares of overnight and high-frequency returns), or where the overnight returns are simply ignored. All model variants may further be extended by allowing for a non-parametrically estimated slowly-varying long-run volatility matrix. The proposed models are evaluated using five-minute and overnight return data on four DJIA stocks (AXP, GE, HD, and IBM) from January 1988 to November 2014. The focus is on forecasting weekly volatilities (defined as the low frequency). The mixed-frequency GARCH models are found to systematically dominate the low-frequency GARCH model in terms of in-sample fit and out-of-sample forecasting accuracy. They also exhibit much lower low-frequency volatility persistence than the low-frequency GARCH model. Among the mixed-frequency models, the low-frequency persistence estimates decrease as the data frequency increases from daily to five-minute frequency, and as overnight returns are included. That is, ignoring the available high-frequency information leads to spuriously high volatility persistence. Among the other findings are that the single-component model variants perform worse than the two-component variants; that the overnight volatility component exhibits more persistence than the intra-day component; and that MIDAS weighting performs better than not weighting at all (i.e., than realized volatility).","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122613927","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}

{"title":"Rational Bubble Testing: An In-Depth Study on CNX Nifty","authors":"Bikramaditya Ghosh","doi":"10.5958/2249-7323.2016.00028.6","DOIUrl":"https://doi.org/10.5958/2249-7323.2016.00028.6","url":null,"abstract":"Stock Market bubble, that drives either a few selected stocks, or sometimes even an entire Index far away from the real world of valuation does find its rot in herd behaviour and cognitive bias, as behavioral finance states, however quantification becomes important as public money gets stuck in this financial hurricane more often than not. This study is conducted empirically on CNX Nifty on the onset of 2008 global crisis and concludes recently to detect the indirect evidence of an asset price bubble. Identification of unnatural deviation and asset price movement in a radical manner over a relatively short period of time, without giving adequate time to disperse the information in a symmetric manner becomes quite crucial in investment finance. This study extends Caspi, Itamar's study done in 2013 on behalf of Bank of Israel, on the US markets (namely S&P 500) using three advanced forms of Augmented Dickey Fuller Test (such as RADF, SADF and GSADF simultaneously) and investigates its validity in Indian bellwether bourse CNX Nifty. This study will also throw an interesting aspect of de-coupling myth in Indian capital market along with the spotting of herding behaviour during the global crisis, post the financial cyclone and during the recovery phase extended till recently.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121686343","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}

{"title":"Some Theoretical Results on Forecast Combinations","authors":"F. Chan, Laurent L. Pauwels","doi":"10.2139/ssrn.2638232","DOIUrl":"https://doi.org/10.2139/ssrn.2638232","url":null,"abstract":"This paper proposes a framework for the analysis of the theoretical properties of forecast combination, with the forecast performance being measured in terms of mean squared forecast errors (MSFE). Such a framework is useful for deriving all existing results with ease. In addition, it also provides insights into two forecast combination puzzles. Specifically, it investigates why a simple average of forecasts often outperforms forecasts from single models in terms of MSFEs, and why a more complicated weighting scheme does not always perform better than a simple average. In addition, this paper presents two new findings that are particularly relevant in practice. First, the MSFE of a forecast combination decreases as the number of models increases. Second, the conventional approach to the selection of optimal models, based on a simple comparison of MSFEs without further statistical testing, leads to a biased selection.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129592932","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}

{"title":"An Adaptive Approach to Forecasting Three Key Macroeconomic Variables for Transitional China","authors":"Linlin Niu, Xiu Xu, Ying Chen","doi":"10.2139/ssrn.2593768","DOIUrl":"https://doi.org/10.2139/ssrn.2593768","url":null,"abstract":"We propose the use of a local autoregressive (LAR) model for adaptive estimation and forecasting of three of China’s key macroeconomic variables: GDP growth, inflation and the 7-day interbank lending rate. The approach takes into account possible structural changes in the data-generating process to select a local homogeneous interval for model estimation, and is particularly well-suited to a transition economy experiencing ongoing shifts in policy and structural adjustment. Our results indicate that the proposed method outperforms alternative models and forecast methods, especially for forecast horizons of 3 to 12 months. Our 1-quarter ahead adaptive forecasts even match the performance of the well-known CMRC Langrun survey forecast. The selected homogeneous intervals indicate gradual changes in growth of industrial production driven by constant evolution of the real economy in China, as well as abrupt changes in interestrate and inflation dynamics that capture monetary policy shifts.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116457940","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}

{"title":"The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference","authors":"N. Basturk, S. Grassi, Lennart F. Hoogerheide, A. Opschoor, H. V. Dijk","doi":"10.2139/ssrn.2587011","DOIUrl":"https://doi.org/10.2139/ssrn.2587011","url":null,"abstract":"This paper presents the R-package MitISEM (mixture of t by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture of Student-t densities as approximating density. In the first stage a mixture of Student-t densities is fitted to the target using an expectation maximization (EM) algorithm where each step of the optimization procedure is weighted using importance sampling. In the second stage this mixture density is a candidate density for efficient and robust application of importance sampling or the Metropolis-Hastings (MH) method to estimate properties of the target distribution. The package enables Bayesian inference and prediction on model parameters and probabilities, in particular, for models where densities have multi-modal or other non-elliptical shapes like curved ridges. These shapes occur in research topics in several scientific fields. For instance, analysis of DNA data in bio-informatics, obtaining loans in the banking sector by heterogeneous groups in financial economics and analysis of education's effect on earned income in labor economics. The package MitISEM provides also an extended algorithm, 'sequential MitISEM', which substantially decreases computation time when the target density has to be approximated for increasing data samples.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124155251","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}

{"title":"The Predictive Power of Portfolio Characteristics","authors":"Barry M. Gillman, E. Khusainova, J. Mier","doi":"10.2139/ssrn.2539670","DOIUrl":"https://doi.org/10.2139/ssrn.2539670","url":null,"abstract":"In order to predict future relative results within a universe of equity portfolios, the authors hypothesize that it is possible to use selected portfolio characteristics as opposed to relying on past performance. This research uses Active Share and Concentration Coefficient data for universes of US, international, and global equity mutual fund portfolios to develop a method of predicting the future relative ranking of the portfolios’ information ratios. The predictive power of this approach appears statistically significant for the five-year period of 2009-2013, but not during the financial crisis years of 2007-2008. The authors believe these results are indicative of the usefulness of this approach, but not conclusive due to the limited time frame (seven years of data) and universe (174 funds). The authors invite collaboration for further research.","PeriodicalId":170198,"journal":{"name":"ERN: Forecasting Techniques (Topic)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134051546","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}