Financial Variables as Predictors of Real Output Growth

We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We discover that adding low frequency stock returns (up to annual returns, depending on forecast horizon) to a quarterly AR(1) model improves forecasts of output growth.Forecasting, Mixed Frequencies, Functional linear regression

Mixing Frequencies : Stock Returns as a Predictor of Real Output Growth

We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We find that our mixed frequency models perform well in forecasting real output growth.Forecasting, Mixed Data Sampling, Functional linear regression, Test for Superior Predictive Ability

Dynamic Regressions with Variables Observed at Different Frequencies

We consider the problem of formulating and estimating dynamic regression models with variables observed at different frequencies. The strategy adopted is to define the dynamics of the model in terms of the highest available frequency, and to apply certain lag polynomials to transform the dynamics so that the model is expressed solely in terms of observed variables. A general solution is provided for models with monthly and quarterly observations. We also show how the methods can be extended to models with quarterly and annual observations, and models combining monthly and annual observations.

Global and Regional Sources of Risk in Equity Markets: Evidence from Factor Models with Time-Varying Conditional Skewness

This study examines the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. Besides conditional heteroskedasticity, the models allow shocks to have time-varying conditional skewness. The global factor appears less important for market volatility in models that permit time-varying conditional skewness. The influence of regional and global factors on risk is small in most of the markets, except in the late 1990s during which the regional factor accounted for a substantial portion of negative skewness in the markets' returns distribution.Asymmetries, Skewness, Volatility, Spillover, Stock returns

Global and Regional Sources of Risk in Equity Markets: Evidence from Factor Models with Time-Varying Conditional Skewness

We examine the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. The models allow for conditional heteroskedasticity and time-varying conditional skewness, and permit mean, variance and skewness spillovers to be measured. We find that the pattern of spillovers changed in the late 1990s. When spillovers are allowed to vary with the type of news arriving in a market, we find that local news reduces mean spillovers but increases variance spillovers. News about regional countries increases skewness spilloversAsymmetries, Skewness, Volatility, Spillover, Stock returns, News.

Non-Fundamental Expectations and Economic Fluctuations: Evidence from Professional Forecasts

It is theoretically possible that non-fundamental idiosyncratic shocks to agents’ rational expectations are a source of economic fluctuations. Studies using data on consumer and investor sentiment suggest that this is indeed a significant source of fluctuations. We present the results of a study that uses forecasts from professional forecasters to extract non-fundamental shocks to expectations. In contrast to previous studies, we show that non-fundamental expectations are not a significant source of output fluctuations.Non-fundamental expectations; Sunspots; Economic fluctuations; Survey of Professional Forecasters; Vector autoregressions

Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange

We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast "calibration" can be used to improve deficient density forecasts. Finally, motivated by recent advances in financial risk management, we provide a detailed application to multivariate high-frequency exchange rate density forecasts. Copyright © 1998 F.X. Diebold, J. Hahn, and A.S. Tay. This paper is also available at

Evaluating Density Forecasts

We propose methods for evaluating density forecasts. We focus primarily on methods that are applicable regardless of the particular user's loss function. We illustrate the methods with a detailed simulation example, and then we present an application to density forecasting of daily stock market returns. We discuss extensions for improving suboptimal density forecasts, multi-step-ahead density forecast evaluation, multivariate density forecast evaluation, monitoring for structural change and its relationship to density forecasting, and density forecast evaluation with known loss function.

Evaluating density forecasts

The authors propose methods for evaluating and improving density forecasts. They focus primarily on methods that are applicable regardless of the particular user's loss function, though they take explicit account of the relationships between density forecasts, action choices, and the corresponding expected loss throughout. They illustrate the methods with a detailed series of examples, and they discuss extensions to improving and combining suboptimal density forecasts, multistep-ahead density forecast evaluation, multivariate density forecast evaluation, monitoring for structural change and its relationship to density forecasting, and density forecast evaluation with known loss function.Forecasting