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on Forecasting |
| By: | Benjamin Beckers; Samya Beidas-Strom |
| Abstract: | We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons. |
| Keywords: | Oil;Forecasting;VARs, forecasts, prices, random walk, demand, Time-Series Models, Forecasting and Other Model Applications, Energy and the Macroeconomy, All Countries, |
| Date: | 2015–11–25 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:15/251&r=for |
| By: | Dilip Kumar (Indian Institute of Management Kashipur) |
| Abstract: | We provide a framework based on the unbiased extreme value volatility estimator (Namely, the AddRS estimator) to compute and predict the long position and a short position VaR, henceforth referred to as the ARFIMA-AddRS-SKST model. We evaluate its VaR forecasting performance using the unconditional coverage test and the conditional coverage test for long and short positions on four global indices (S&P 500, CAC 40, IBOVESPA and S&P CNX Nifty) and compare the results with that of a bunch of alternative models. Our findings indicate that the ARFIMA-AddRS-SKST model outperforms the alternative models in predicting the long and short position VaR. Finally, we examine the economic significance of the proposed framework in estimating and predicting VaR using Lopez loss function approach so as to identify the best model that provides the least monetary loss. Our findings indicate that the VaR forecasts based on the ARFIMA-AddRS-SKST model provides the least total loss for various x% long and short positions VaR and this supports the superior properties of the proposed framework in forecasting VaR more accurately. |
| Keywords: | Extreme value volatility estimator; Value-at-risk; Skewed Student t distribution; Risk management. |
| JEL: | C22 C53 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iefpro:3205528&r=for |
| By: | Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Greece); Periklis Gogas (Department of Economics, Democritus University of Thrace, Greece); Theophilos Papadimitriou (Department of Economics, Democritus University of Thrace, Greece); Rangan Gupta (Department of Economics, University of Pretoria) |
| Abstract: | Forecasting the evolution path of macroeconomic variables has always been of keen interest to policy authorities. A common tool in the relevant forecasting literature is the term spread of Treasury bond interest rates. In this paper we decompose the term spread of treasury bonds into an expectations and a term premium component and we evaluate the informational content of each component in forecasting the real GDP growth rate and inflation (as measured by the GDP deflator) in various forecasting horizons. In doing so, we evaluate alternative decomposition procedures, introduce the nonlinear machine learning Support Vector Regression (SVR) methodology in rolling regressions and examine both point and conditional probability distribution forecasts. We also consider a number of control variables that are typically used in this context. According to our empirical findings neither the term spread nor its decomposition possess the ability to forecast output growth or inflation. |
| Keywords: | Inflation, GDP, Forecasting, Support Vector Machines, Term Premium |
| JEL: | C22 C53 E47 |
| Date: | 2016–02 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201613&r=for |
| By: | BRAIONE, M. (Université catholique de Louvain, CORE, Belgium) |
| Abstract: | We propose a scalar variation of the multivariate HEAVY model of Noureldin et al. which allows for a time-varying long run component in the specification of the daily conditional covariance matrix. Differently from the original model featuring a BEKK-type parameterization, ours extends it to allow for a separate modeling of the conditional volatilities and the conditional correlation matrix, in a DCC fashion. Estimation is performed in one step by QML and multi-step ahead forecasting is feasible applying the direct approach to the HEAVY-P equation. In an empirical application aiming at modeling and forecasting the conditional covariance matrix of a stock (BAC) and an index (S&P 500), we find that the new model statistically outperforms the original HEAVY model both in-sample and out-of-sample. |
| Keywords: | HEAVY model, Long term models, Mixed Data Sampling, Direct forecasting |
| Date: | 2016–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvco:2016002&r=for |
| By: | García, Jaume; Pérez, Levi; Rodríguez, Plácido |
| Abstract: | An empirical analysis of Spanish football betting odds is carried out here to test whether football matches final result estimates by experts (bookmakers) differ (better/worse) from those by the ‘crowd’ (football pools bettors). Examination of implied probabilities for each of the possible outcomes evidences the existence of favourite long-shot bias in the betting market for Spanish football. A further study of the accuracy of probability forecasts concludes that experts seem to be better in forecasting football results than the ‘crowd’. |
| Keywords: | betting odds, forecasting, wisdom-of-crowds hypothesis, favourite long-shot bias |
| JEL: | C53 L83 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:69687&r=for |
| By: | Nikola Milosevic |
| Abstract: | Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity's future price over the long time. Our method is able to correctly predict whether some company's value will be 10% higher or not over the period of one year in 76.5% of cases. |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1603.00751&r=for |
| By: | Serhat Solmaz; Marzie Taheri Sanjani |
| Abstract: | External headwinds, together with domestic vulnerabilities, have loomed over the prospects of emerging markets in recent years. We propose an empirical toolbox to quantify the impact of external macro-financial shocks on domestic economies in parsimonious way. Our model is a Bayesian VAR consisting of two blocks representing home and foreign factors, which is particularly useful for small open economies. By exploiting the mixed-frequency nature of the model, we show how the toolbox can be used for “nowcasting†the output growth. The conditional forecast results illustrate that regular updates of external information, as well as domestic leading indicators, would significantly enhance the accuracy of forecasts. Moreover, the analysis of variance decompositions shows that external shocks are important drivers of the domestic business cycle. |
| Date: | 2015–12–23 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:15/269&r=for |