Combination Forecasts of Bond and Stock Returns: An Asset Allocation Perspective

We investigate the out-of-sample forecasting ability of the HML, SMB, momentum, short-term and long-term reversal factors along with their size and value decompositions on U.S. bond and stock returns for a variety of horizons ranging from the short run (1 month) to the long run (2 years). Our findings suggest that these factors contain significantly more information for future bond and stock market returns than the typically employed financial variables. Combination of forecasts of the empirical factors turns out to be particularly successful, especially from an an asset allocation perspective. Similar findings pertain to the European and Japanese markets

The IGARCH e®ect: Consequences on volatility forecasting and option trading

This paper studies the integrated Garch (IGARCH) e®ect, a phenomenon often encountered when estimating conditional auto-regressive models on ¯nancial time series. The analysis of twelve indexes of major ¯nancial markets provides empirical evidence of its well-spread presence especially in periods of market turbulence. We examine its impact on volatility forecasting and on trading and hedging options. We show that a strong IGARCH e®ect may have relevant consequences on trading and on risk management.stock returns, volatility forecasting, GARCH(1,1), IGARCH effect, option hedging

Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?

We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets while simpler models tend to perform better in sizeable data sets. To combine best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time

Does the Investment Opportunities Bias Affect the Investment-Cash Flow Sensitivities of Unlisted SMEs?

Using a panel of 5,999 small and medium-sized Belgian enterprises (SMEs) over the period 2002-2008, we identify three measures of investment opportunities suitable for unlisted firms. We then estimate firm-varying investment-cash flow sensitivities (ICFS) from reduced-form investment equations that include these measures, and compare them with those derived from a model that does not control for investment opportunities. We find that all our models yield similar ICFS estimates, which are significantly related to a wide set of proxies for financing constraints. These findings suggest that the ICFS of SMEs do not simply reflect investment opportunities. The investment opportunities bias may therefore have been overstated in previous literature.Financing constraints, Firm-varying investment-cash flow sensitivities, Investment opportunities, Gross added value.

A blocking and regularization approach to high dimensional realized covariance estimation

We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the ’RnB’ estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results

Credit ratings and bank monitoring ability

In this paper, the authors use credit rating data from two Swedish banks to elicit evidence on banks' loan monitoring ability. They test the banks' ability to forecast credit bureau ratings, and vice versa, and show that bank ratings are able to predict future credit bureau ratings. This is evidence that bank credit ratings, consistent with theory, contain valuable private information. However, the authors also find that public ratings have an ability to predict future bank ratings, implying that internal bank ratings do not fully or efficiently incorporate all publicly available information. This suggests that risk analyses by banks or regulators should be based on both internal bank ratings and public ratings. They also document that the credit bureau ratings add information to the bank ratings in predicting bankruptcy and loan default. The methods the authors use represent a new basket of straightforward techniques that enables both financial institutions and regulators to assess the performance of credit ratings systems.Credit ratings ; Risk assessment

FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS

In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes. Models are tested on a time series of California power market system prices and loads from the period proceeding and including the market crash.Electricity, price forecasting, ARMA model, seasonal component

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance

Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features