scholarly journals Two Out-of-Sample Forecasting Models of the Equity Premium

2020 ◽  
Author(s):  
Thiago de Oliveira Souza
Keyword(s):  
Equity Premium ◽  
Forecasting Models ◽  
Out Of Sample

scholarly journals Two Out-of-Sample Forecasting Models of the Equity Premium

2020 ◽  
Author(s):  
Thiago de Oliveira Souza
Keyword(s):  
Equity Premium ◽  
Forecasting Models ◽  
Out Of Sample

scholarly journals Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4173
Author(s):  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Crude Oil ◽  
Predictive Value ◽  
Realized Variance ◽  
International Spillovers ◽  
Forecasting Models ◽  
West Texas ◽  
Out Of Sample ◽  
One Year ◽  
Lasso Estimator

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.

scholarly journals Mathematical Development and Evaluation of Forecasting Models for Accuracy of Inflation in Developing Countries: A Case of Vietnam

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Nhu-Ty Nguyen ◽  
Thanh-Tuyen Tran
Keyword(s):  
Raw Materials ◽  
Arima Model ◽  
Consumer Price Index ◽  
Forecast Accuracy ◽  
Complex Problem ◽  
Forecast Performance ◽  
Forecasting Models ◽  
Out Of Sample ◽  
Forecasting Inflation ◽  
Using Data

Inflation is a key element of a national economy, and it is also a prominent and important issue influencing the whole economy in terms of marketing. This is a complex problem requiring a large investment of time and wisdom to attain positive results. Thus, appropriate tools for forecasting inflation variables are crucial significant for policy making. In this study, both clarified value calculation and use of a genetic algorithm to find the optimal parameters are adopted simultaneously to construct improved models: ARIMA, GM(1,1), Verhulst, DGM(1,1), and DGM(2,1) by using data of Vietnamese inflation output from January 2005 to November 2013. The MAPE, MSE, RMSE, and MAD are four criteria with which the various forecasting models results are compared. Moreover, to see whether differences exist, Friedman and Wilcoxon tests are applied. Both in-sample and out-of-sample forecast performance results show that the ARIMA model has highly accurate forecasting in Raw Materials Price (RMP) and Gold Price (GP), whereas, the calculated results of GM(1,1) and DGM(1,1) are suitable to forecast Consumer Price Index (CPI). Therefore, the ARIMA, GM(1,1), and DGM(1,1) can handle the forecast accuracy of the issue, and they are suitable in modeling and forecasting of inflation in the case of Vietnam.

scholarly journals Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?

2005 ◽  
Author(s):  
John Y. Campbell ◽  
Samuel Brodsky Thompson
Keyword(s):  
Equity Premium ◽  
Out Of Sample

Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy

2009 ◽  
Author(s):  
David Rapach ◽  
Jack Strauss ◽  
Guofu Zhou
Keyword(s):  
Equity Premium ◽  
Real Economy ◽  
The Real ◽  
Out Of Sample

Means to an end: Pro-government militias as a predictive indicator of strategic mass killing

2015 ◽  
Vol 34 (5) ◽  
pp. 461-484 ◽  
Author(s):  
Ore Koren
Keyword(s):  
Political Violence ◽  
Large Body ◽  
Structural Factors ◽  
Mass Killing ◽  
Forecasting Models ◽  
Sample Data ◽  
Out Of Sample ◽  
Structural Indicators ◽  
Predictive Strength ◽  
Predictive Indicator

Forecasting models of state-led mass killing are limited in their use of structural indicators, despite a large body of research that emphasizes the importance of agency and security repertoires in conditioning political violence. I seek to overcome these limitations by developing a theoretical and statistical framework that highlights the advantages of using pro-government militias (PGMs) as a predictive indicator in forecasting models of state-led mass killing. I argue that PGMs can lower the potential costs associated with mass killing for a regime faced with an internal threat, and might hence “tip the balance” in its favor. In estimating a series of statistical models and their receiver–operator characteristic curves to evaluate this hypothesis globally for the years 1981–2007, focusing on 270 internal threat episodes, I find robust support for my expectations: including PGM indicators in state-led mass killing models significantly improves their predictive strength. Moreover, these results hold even when coefficient estimates produced by in-sample data are used to predict state-led mass killing in cross-validation and out-of-sample data for the years 2008–2013. This study hence provides an introductory demonstration of the potential advantages of including security repertoires, in addition to structural factors, in forecasting models.

scholarly journals Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach

2019 ◽  
Vol 11 (12) ◽  
pp. 50
Author(s):  
Anwen Yin
Keyword(s):  
Structural Breaks ◽  
Equity Premium ◽  
Model Averaging ◽  
Forecast Combination ◽  
Two Stage ◽  
Explicit Theory ◽  
Out Of Sample ◽  
Combination Approach ◽  
Averaged Models ◽  
Averaged Model

This paper introduces a two-stage out-of-sample predictive model averaging approach to forecasting the U.S. market equity premium. In the first stage, we combine the break and stable specifications for each candidate model utilizing schemes such as Mallows weights to account for the presence of structural breaks. Next, we combine all previously averaged models by equal weights to address the issue of model uncertainty. Our empirical results show that the double-averaged model can deliver superior statistical and economic gains relative to not only the historical average but also the simple forecast combination when forecasting the equity premium. Moreover, our approach provides an explicit theory-based linkage between forecast combination and structural breaks which distinguishes this study from other closely related works.

Out-of-sample equity premium prediction: A scenario analysis approach

2018 ◽  
Vol 37 (5) ◽  
pp. 604-626
Author(s):  
Xiaoxiao Tang ◽  
Feifang Hu ◽  
Peiming Wang
Keyword(s):  
Scenario Analysis ◽  
Equity Premium ◽  
Analysis Approach ◽  
Out Of Sample

Evaluating the performance of Gulf of Alaska walleye pollock (Theragra chalcogramma) recruitment forecasting models using a Monte Carlo resampling strategy

2009 ◽  
Vol 66 (3) ◽  
pp. 367-381 ◽  
Author(s):  
Yong-Woo Lee ◽  
Bernard A. Megrey ◽  
S. Allen Macklin
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Gulf Of Alaska ◽  
Data Set ◽  
Forecasting Models ◽  
Walleye Pollock Theragra Chalcogramma ◽  
Sample Data ◽  
Out Of Sample ◽  
Data Points ◽  
Recruitment Model

Multiple linear regressions (MLRs), generalized additive models (GAMs), and artificial neural networks (ANNs) were compared as methods to forecast recruitment of Gulf of Alaska walleye pollock ( Theragra chalcogramma ). Each model, based on a conceptual model, was applied to a 41-year time series of recruitment, spawner biomass, and environmental covariates. A subset of the available time series, an in-sample data set consisting of 35 of the 41 data points, was used to fit an environment-dependent recruitment model. Influential covariates were identified through statistical variable selection methods to build the best explanatory recruitment model. An out-of-sample set of six data points was retained for model validation. We tested each model’s ability to forecast recruitment by applying them to an out-of-sample data set. For a more robust evaluation of forecast accuracy, models were tested with Monte Carlo resampling trials. The ANNs outperformed the other techniques during the model fitting process. For forecasting, the ANNs were not statistically different from MLRs or GAMs. The results indicated that more complex models tend to be more susceptible to an overparameterization problem. The procedures described in this study show promise for building and testing recruitment forecasting models for other fish species.

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