In this issue we have Efficient Prediction of Excess Returns ; Building composite leading indexes in a dynamic factor model framework: a new proposal ; Jackknifing stock return predictions ; Modelling electricity prices: from the state of the art to a draft of a new proposal.
Jonathan H. Wright
|It is well known that augmenting a standard linear regression model with variables that are correlated with the error term but uncorrelated with the original regressors will increase asymptotic efficiency of the original coefficients. We argue that in the context of predicting excess returns, valid augmenting variables exist and are likely to yield substantial gains in estimation efficiency and, hence, predictive accuracy. The proposed augmenting variables are ex post measures of an unforecastable component of excess returns: ex post errors from macroeconomic survey forecasts and the surprise components of asset price movements around macroeconomic news announcements. These "surprises" cannot be used directly in forecasting–they are not observed at the time that the forecast is made–but can nonetheless improve forecasting accuracy by reducing parameter estimation uncertainty. We derive formal results about the be nefits and limits of this approach and apply it to standard examples of forecasting excess bond and equity returns. We find substantial improvements in out-of-sample forecast accuracy for standard excess bond return regressions; gains for forecasting excess stock returns are much smaller.|
|JEL:||C22 C53 G14|
|By:||Massimiliano Serati (Cattaneo University (LIUC))
Gianni Amisano (Brescia University)
|One of the most problematic aspects in the work of policy makers and practitioners is having efficient forecasting tools combining two seemingly incompatible features: ease of use and completeness of the information set underlying the forecasts. Econometric literature provides different answers to these needs: Dynamic Factor Models (DFMs) optimally exploit the information coming from large datasets; composite leading indexes represent an immediate and flexible tool to anticipate the future evolution of a phenomenon. Curiously, the recent DFM literature has either ignored the construction of leading indexes or has made unsatisfactory choices as regards the criteria for aggregating the index components and the identification of factors that feed the index. This paper fills the gap and proposes a multi-step procedure for building composite leading indexes within a DFM framework. Once selected the target economic varia ble and estimated a DFM based on a large target-oriented dataset, we identify the common factor shocks through sign restrictions on the impact multipliers and simulate the structural form of the model. The Forecast Error Variance Decompositions obtained over a k steps-ahead simulation horizon define k sets of weights for aggregating factors (in a different way depending on the leading horizon) in order to get composite leading indexes. This procedure is used for a very preliminar empirical exercise aimed at forecasting crude nominal oil prices. The results seem to be encouraging and support the validity of the proposal: we generate a wide range of horizon-specific leading indexes with appreciable forecasting performances.|
|We show that the general bias reducing technique of jackknifing can be successfully applied to stock return predictability regressions. Compared to standard OLS estimation, the jackknifing procedure delivers virtually unbiased estimates with mean squared errors that generally dominate those of the OLS estimates. The jackknifing method is very general, as well as simple to implement, and can be applied to models with multiple predictors and overlapping observations. Unlike most previous work on inference in predictive regressions, no specific assumptions regarding the data generating process for the predictors are required. A set of Monte Carlo experiments show that the method works well in finite samples and the empirical section finds that out-of-sample forecasts based on the jackknifed estimates tend to outperform those based on the plain OLS estimates. The improved forecast ability also translates into economica lly relevant welfare gains for an investor who uses the predictive regression, with jackknifed estimates, to time the market.|
|By:||Massimiliano Serati (Cattaneo University (LIUC))
Matteo Manera (Cattaneo University (LIUC))
|In the last decades a liberalization of the electric market has started; prices are now determined on the basis of contracts on regular markets and their behaviour is mainly driven by usual supply and demand forces. A large body of literature has been developed in order to analyze and forecast their evolution: it includes works with different aims and methodologies depending on the temporal horizon being studied. In this survey we depict the actual state of the art focusing only on the recent papers oriented to the determination of trends in electricity spot prices and to the forecast of these prices in the short run. Structural methods of analysis, which result appropriate for the determination of forward and future values are left behind. Studies have been divided into three broad classes: Autoregressive models, Regime switching models, Volatility models. Six fundamental points arise: the peculiarities of electri city market, the complex statistical properties of prices, the lack of economic foundations of statistical models used for price analysis, the primacy of uniequational approaches, the crucial role played by demand and supply in prices determination, the lack of clearcut evidence in favour of a specific framework of analysis. To take into account the previous stylized issues, we propose the adoption of a methodological framework not yet used to model and forecast electricity prices: a time varying parameters Dynamic Factor Model (DFM). Such an eclectic approach, introduced in the late ‘70s for macroeconomic analysis, enables the identification of the unobservable dynamics of demand and supply driving electricity prices, the coexistence of short term and long term determinants, the creation of forecasts on future trends. Moreover, we have the possibility of simulating the impact that mismatches between demand and supply have over the price variable. This way it is possible t o evaluate whether congestions in the network (eventually leading bla ck out phenomena) trigger price reactions that can be considered as warning mechanisms.|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.