nep-for New Economics Papers
on Forecasting
Issue of 2015‒12‒28
thirteen papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Key US Macroeconomic Variables with a Factor-Augmented Qual VAR By Rangan Gupta; Eric Olson; Mark E. Wohar
  2. Predictive Models for Disaggregate Stock Market Volatility By Chong, Terence Tai Leung; Lin, Shiyu
  3. A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models By Ana Beatriz Galvão; Liudas Giraitis; George Kapetanios; Katerina Petrova
  4. Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms By Yongchen Zhao
  5. Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models By Stelios D. Bekiros; Alessia Paccagnini
  6. Accounting for Oil and Gas Exploration Activities: A Triumph of Economics over Politics By Misund, Bård
  7. A fully non-parametric heteroskedastic model By Matthieu Garcin; Clément Goulet
  8. Risk appetite and exchange rates By Adrian, Tobias; Etula, Erkko; Shin, Hyun Song
  9. Industry based equity premium forecasts By Nuno Silva
  10. Predicting South African Equity Premium using Domestic and Global Economic Policy Uncertainty Indices: Evidence from a Bayesian Graphical Model By Mehmet Balcilar; Rangan Gupta; Mampho P. Modise; John W. Muteba Mwamba
  11. A Time Varying DSGE Model with Financial Frictions By Ana Beatriz Galvão; Liudas Giraitis; George Kapetanios; Katerina Petrova
  12. The Role of Domestic and Global Economic Policy Uncertainties in Predicting Stock Returns and their Volatility for Hong Kong, Malaysia and South Korea: Evidence from a Nonparametric Causality-in-Quantiles Approach By Mehmet Balcilar; Rangan Gupta; Won Joong Kim; Clement Kyei
  13. The Impact of Economic Policy Uncertainty on US Real Housing Returns and their Volatility: A Nonparametric Approach By Christophe André; Lumengo Bonga-Bonga; Rangan Gupta

  1. By: Rangan Gupta (Department of Economics, University of Pretoria); Eric Olson (College of Business and Economics, West Virginia University, Morgantown, WV 26506, USA.); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA, and School of Business and Economics, Loughborough University, Leicestershire, LE11 3TU, UK.)
    Abstract: In this paper, we first extract 8 factors from a monthly data set of 130 macroeconomic and financial variables. Then these extracted factors are used to construct a Factor-Augmented Qualitative VAR (FA-Qual VAR) model to forecast industrial production growth, inflation, the Federal funds rate and the term spread based on a pseudo real-time recursive forecasting exercise over an out-of-sample period of 1980:1-2014:12, using an in-sample period of 1960:1-1979:12. Short-, medium- and long-run horizons of one-, six, twelve- and twenty-four-month(s)-ahead are considered. The forecasts from the FA-Qual VAR is compared with that of a standard VAR model (comprising of output, prices, interest rate and the term spread), and that of a Qualitative VAR (Qual VAR) model (which includes the variables in the VAR and the latent business cycle index generated based on the information from the industrial production growth, inflation, the Federal Funds rate and the term spread). In general, we observe that the FA-QualVAR tends to perform significantly better than the VAR and Qual VAR for the one-month-ahead and six-months-ahead forecast horizons for the key US variables under consideration. In other words, adding information from a large data set (through the use of factors) tend to produce forecasting gains at short- to medium-run horizons.
    Keywords: Vector Autoregressions, Business Cycle Turning Points, Factors, Forecasting
    JEL: C32 C53 E37 E47
    Date: 2015–11
  2. By: Chong, Terence Tai Leung; Lin, Shiyu
    Abstract: This paper incorporates the macroeconomic determinants into the forecasting model of industry-level stock return volatility in order to detect whether different macroeconomic factors can forecast the volatility of various industries. To explain different fluctuation characteristics among industries, we identified a set of macroeconomic determinants to examine their effects. The Clark and West (2007) test is employed to verify whether the new forecasting models, which vary among industries based on the in-sample results, can have better predictions than the two benchmark models. Our results show that default return and default yield have significant impacts on stock return volatility.
    Keywords: Industry level stock return volatility; Out-of-sample forecast; Granger Causality.
    JEL: C12 G12
    Date: 2015–11–08
  3. By: Ana Beatriz Galvão (University of Warwick); Liudas Giraitis (Queen Mary University of London); George Kapetanios (Queen Mary University of London); Katerina Petrova (Queen Mary University of London)
    Abstract: DSGE models have recently received considerable attention in macroeconomic analysis and forecasting. They are usually estimated using Bayesian methods, which require the computation of the likelihood function under the assumption that the parameters of the model remain fixed throughout the sample. This paper presents a Local Bayesian Likelihood method suitable for estimation of DSGE models that can accommodate time variation in all parameters of the model. There are two advantages in allowing the parameters to vary over time. The first is that it enables us to assess the possibilities of regime changes, caused by shifts in the policy preferences or the volatility of shocks, as well as the possibility of misspecification in the design of DSGE models. The second advantage is that we can compute predictive densities based on the most recent parameters' values that could provide us with more accurate forecasts. The novel Bayesian Local Likelihood method applied to the Smets and Wouters (2007) model provides evidence of time variation in the policy parameters of the model as well as the volatility of the shocks. We also show that allowing for time variation improves considerably density forecasts in comparison to the fixed parameter model and we interpret this result as evidence for the presence of stochastic volatility in the structural shocks.
    Keywords: DSGE models, Local likelihood, Bayesian methods, Time varying parameters
    JEL: C11 C53 E27 E52
    Date: 2015–12
  4. By: Yongchen Zhao (Department of Economics, Towson University)
    Abstract: Based on a set of carefully designed Monte Carlo exercises, this paper documents the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are cross-sectionally heterogeneous and dynamically evolving over time. Results from these exercises provide guidelines regarding the selection of forecast combination method based on the nature, frequency, and magnitude of instabilities in forecasts as well as the target variable. Following these guidelines, a simple forecast combination procedure is proposed and demonstrated through a real-time forecast combination exercise using the U.S. Survey of Professional Forecasters, where combined forecasts are shown to have superior performance that is not only statistically significant but also of practical importance.
    Keywords: Forecast combination, Exponential re-weighting, Shrinkage, Estimation error, Performance stability, Real-Time Data.
    JEL: C53 C22 C15
    Date: 2015–12
  5. By: Stelios D. Bekiros; Alessia Paccagnini
    Abstract: Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE–VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4–2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1–2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.
    Keywords: Bayesian estimation; Forecasting; Metropolis–Hastings; Markov Chain Monte Carlo; Marginal data density; Factor augmented DSGE
    Date: 2014–03
  6. By: Misund, Bård (UiS)
    Abstract: For more than 40 years oil and gas companies have been able to choose between two competing methods for accounting for exploration activities. The literature suggests that accounting method discretion can potentially signal managements' private information with the benefit of improving the relevance of accruals for forecasting future cash flows. However, if accounting method flexibility is used for financial window-dressing, accruals can lose their value-relevance and investors will resort to cash flows measures instead. In this study we compare the value-relevance of earnings versus cash flow for oil and gas companies from 1992 to 2013. Our results suggest that earnings are not significant, independent of accounting method choice, consistent with the view that accruals have limited value in the oil and gas industry. Rather, it seems that cash flow measures of both current and future profitability are significantly associated with oil company returns. These findings suggest that the financial markets lack confidence in oil company earnings, irrespective of accounting method choice.
    Keywords: Full cost versus successful efforts; oil and gas company valuation; petroleum accounting; value-relevance.
    JEL: G12 M40 Q33
    Date: 2015–12–18
  7. By: Matthieu Garcin (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Natixis Asset Management - SAMS, LABEX Refi - ESCP Europe); Clément Goulet (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, LABEX Refi - ESCP Europe)
    Abstract: In this paper we propose a new model for estimating returns and volatility. Our approach is based both on the wavelet denoising technique and on the variational theory. We assess that the volatility can be expressed as a non-parametric functional form of past returns. Therefore, we are able to forecast both returns and volatility and to build confidence intervals for predicted returns. Our technique outperforms classical time series theory. Our model does not require the stationarity of the observed log-returns, it preserves the volatility stylised facts and it is based on a fully non-parametric form. This non-parametric form is obtained thanks to the multiplicative noise theory. To our knowledge, this is the first time that such a method is used for financial modeling. We propose an application to intraday and daily financial data.
    Keywords: Volatility modeling,non variational calculus,wavelet theory,trading strategy
    Date: 2015–09
  8. By: Adrian, Tobias (Federal Reserve Bank of New York); Etula, Erkko (Harvard University); Shin, Hyun Song (Bank for International Settlements)
    Abstract: We present evidence that the growth of U.S.-dollar-denominated banking sector liabilities forecasts appreciations of the U.S. dollar, both in-sample and out-of-sample, against a large set of foreign currencies. We provide a theoretical foundation for a funding liquidity channel in a global banking model where exchange rates fluctuate as a function of banks’ balance sheet capacity. We estimate prices of risk using a cross-sectional asset pricing approach and show that the U.S. dollar funding liquidity forecasts exchange rates because of its association with time-varying risk premia. Our empirical evidence shows that this channel is separate from the more familiar “carry trade” channel. Although the financial crisis of 2007-09 induced a structural shift in our forecasting variables, when we control for this shift, the forecasting relationship is preserved.
    Keywords: asset pricing; financial intermediaries; exchange rates
    JEL: F30 F31 G12 G24
    Date: 2015–12–01
  9. By: Nuno Silva (University of Coimbra/GEMF)
    Abstract: In this paper we used industry indexes to predict the equity premium in the US. We considered several types of predictive models: i) constant coefficients and constant volatility, ii) drifting coefficients and constant volatility, iii) constant coefficients and stochastic volatility and iv) drifting coefficients and stochastic volatility. The models were estimated through the particle learning algorithm, which is suitable for dealing with the problem that an investor faces in practice, given that it allows the investor to revise the parameters as new information arrives. All the models exhibit similar statistical predictive ability, but stochastic volatility models generate slightly higher utility gains.
    Keywords: equity premium prediction, industries, particle filter, combination of forecasts.
    JEL: C11 G11 G14 G17
    Date: 2015–12
  10. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Turkey and Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria); Mampho P. Modise (Department of Economics, University of Pretoria); John W. Muteba Mwamba (Department of Economics and Econometrics, University of Johannesburg)
    Abstract: This paper analyses whether we can predict South African excess stock returns based on a measure of economic policy uncertainty (EPU) of South Africa and twenty other developed and emerging markets. In this regard, we use a Bayesian graphical model estimated over the sample period of 1998:01-2012:12. The model is also estimated in a rolling-window fashion over the monthly sample period of 2003:01-2012:03, using an initial sample period of 1998:01-2002:12. The Bayesian shrinkage approach allows us to simultaneously model the 21 EPUs, over and above 22 other standard financial and macroeconomic predictors. In addition, the Bayesian graphical model also provides both instantaneous and lagged relationships between the predictors and the equity premium. Our full sample results show that, in terms of instantaneous relationship, none of the EPUs play any role, and for the lagged relationship, only the EPU of Hong Kong and the Netherlands can be considered as important with posterior inclusion probabilities in excess of 0.50. Rolling estimates show that instantaneous relationships are quite constant and do not indicate any significant links from EPUs to the equity premium. On the other hand, rolling estimates are highly time-varying, and there is significant lagged impact from most of the EPUs in various sub-periods.
    Keywords: Economic Policy Uncertainty, Stock Prices, Prediction, Bayesian Graphical Models, Vector Autoregression, South Africa
    JEL: C32 C53 E60 G12 G17
    Date: 2015–12
  11. By: Ana Beatriz Galvão (University of Warwick); Liudas Giraitis (Queen Mary University of London); George Kapetanios (Queen Mary University of London); Katerina Petrova (Queen Mary University of London)
    Abstract: We build a time varying DSGE model with financial frictions in order to evaluate changes in the responses of the macroeconomy to financial friction shocks. Using US data, we find that the transmission of the financial friction shock to economic variables, such as output growth, has not changed in the last 30 years. The volatility of the financial friction shock, however, has changed, so that output responses to a one-standard deviation shock increase twofold in the 2007-2011 period in comparison with the 1985-2006 period. The time varying DSGE model with financial frictions improves the accuracy of forecasts of output growth and inflation during the tranquil period of 2000-2006, while delivering similar performance to the fixed coefficient DSGE model for the 2007-2012 period.
    Keywords: DSGE models, Financial frictions, Local likelihood, Bayesian methods, Time varying parameters
    JEL: C11 C53 E27 E52
    Date: 2015–12
  12. By: Mehmet Balcilar (Eastern Mediterranean University, Turkey and University of Pretoria, South Africa); Rangan Gupta (Department of Economics, University of Pretoria); Won Joong Kim (Department of Economics, Konkuk University, Seoul, Republic of Korea.); Clement Kyei (Department of Economics, University of Pretoria)
    Abstract: This paper analyses whether we can predict stock return and its volatility of Hong Kong, Malaysia and South Korea based on measures of domestic and global (China, the European Area, Japan, and the US) economic policy uncertainties (EPU). While, linear Granger causality tests fail to find evidence of predictability, barring the case of South Korean EPU predicting its own stock returns, when we use a nonparametric causality-in-quantiles test, strong evidence of causality is detected from the EPUs for stock return volatility of Malaysia, and both returns and volatility at certain parts of the conditional distributions for South Korea. There is no evidence of predictability from domestic and global EPUs for return and volatility of the Hong Kong stock market. Given the statistical evidence of nonlinearity in our data set, we consider the results from the nonparametric test as more robust relative to the standard linear causality test.
    Keywords: Economic Policy Uncertainty, Stock Returns, Volatility, Linear Causality, Nonparametric Quantile Causality, Emerging Markets
    JEL: C32 C53 E60 G12 G17
    Date: 2015–11
  13. By: Christophe André (Economics Department, Organisation for Economic Co-operation and Development (OECD), 75775 Paris, Cedex 16, France); Lumengo Bonga-Bonga (Faculty of Economic and Financial Sciences, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: This paper analyzes whether a news-based measure of economic policy uncertainty (EPU) helps predict movements in real housing returns. We find evidence of structural breaks and nonlinearity in the relationship between real housing returns and EPU. Hence, we employ a k-th order non-parametric Granger causality test, which is robust to such features. We find strong evidence that economic policy uncertainty affects both real housing returns and their volatility. This suggests that investors in property or related securities can gain information from EPU, not only for predicting future returns, but also in assessing related risks.
    Keywords: Economic policy uncertainty, real housing returns, volatility, non-parametric causality
    JEL: C32 C58 G10 G17
    Date: 2015–11

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