Forecast combining with neural networks

This paper investigates the use of Artificial Neural Networks (ANNs) to combine time series forecasts of stock market volatility from the USA, Canada, Japan and the UK. We demonstrate that combining with nonlinear ANNs generally produces forecasts which, on the basis of out-of-sample forecast encompassing tests and mean squared error comparisons, routinely dominate forecasts from traditional linear combining procedures. Superiority of the ANN arises because of its flexibility to account for potentially complex nonlinear relationships not easily captured by traditional linear models.     

How efficient is the European football betting market? Evidence from arbitrage and trading strategies

This paper assesses the international efficiency of the European football betting market by examining the forecastability of match outcomes on the basis of the information contained in different sets of online and fixed odds quoted by six major bookmakers. The paper also investigates the profitability of strategies based on: combined betting, simple heuristic rules, regression models and prediction encompassing. The empirical results show that combined betting across different bookmakers can lead to limited but highly profitable arbitrage opportunities. Simple trading rules and betting strategies based on forecast encompassing are found capable of also producing significant positive returns. Despite the deregulation, globalization and increased competition in the betting industry over recent years, the predictabilities and profits reported in this paper are not fully consistent with weak-form market efficiency. Copyright © 2008 John Wiley & Sons, Ltd.     

Does Information Help Intra‐Day Volatility Forecasts?

While much research related to forecasting return volatility does so in a univariate setting, this paper includes proxies for information flows to forecast intra‐day volatility for the IBEX 35 futures market. The belief is that volume or the number of transactions conveys important information about the market that may be useful in forecasting. Our results suggest that augmenting a variety of GARCH‐type models with these proxies lead to improved forecasts across a range of intra‐day frequencies. Furthermore, our results present an interesting picture whereby the PARCH model generally performs well at the highest frequencies and shorter forecasting horizons, whereas the component model performs well at lower frequencies and longer forecast horizons. Both models attempt to capture long memory; the PARCH model allows for exponential decay in the autocorrelation function, while the component model captures trend volatility, which dominates over a longer horizon. These characteristics are likely to explain the success of each model. Copyright © 2013 John Wiley & Sons, Ltd.     

Bayesian forecasts following a major level change in exponential smoothing

Exponential smoothing techniques enjoy a wide range of applications to problems in signal detection, inventory and production control, financial planning, and many other areas of business and engineering. One of the most useful models used to explain the theoretical structure of the process is a changing levels model, in which the underlying level of the stochastic process is assumed to undergo random changes in each time period. The observation is modelled as a noisy disturbance of this level. Occasionally a major intervention or level change occurs that is much larger than the typical period-to-period fluctuation in the random level. Using a Bayesian approach it is the purpose of this paper to show how the distribution of the major level change can be detected, estimated and then incorporated in forecasts. Updating equations are obtained for the posterior mean and variance of the major level change as well as the new level.     

Level shifts,temporary changes and forecasting

The purpose of this paper is to analyze the effect of not treating Level Shift and Temporary Change outliers on the point forecasts and prediction intervals from ARIMA models. One of the principal conclusions is that the outliers of the type discussed here considerably increase the inaccuracy of point forecasts, although the latter depends not only on the time of occurrence of the outliers from the forecast origin but also on the type of ARIMA processes under consideration. However, regardless of the time of occurrence and of the type of ARIMA processes considered, Level Shifts and Temporary Changes significantly affect the width of the prediction intervals.     

Is forecasting with large models informative? Assessing the role of judgement in macroeconomic forecasts

We evaluate residual projection strategies in the context of a large‐scale macro model of the euro area and smaller benchmark time‐series models. The exercises attempt to measure the accuracy of model‐based forecasts simulated both out‐of‐sample and in‐sample. Both exercises incorporate alternative residual‐projection methods, to assess the importance of unaccounted‐for breaks in forecast accuracy and off‐model judgement. Conclusions reached are that simple mechanical residual adjustments have a significant impact on forecasting accuracy irrespective of the model in use, likely due to the presence of breaks in trends in the data. The testing procedure and conclusions are applicable to a wide class of models and of general interest. Copyright © 2010 John Wiley & Sons, Ltd.