We systematically examine the comparative predictive performance of a number of alternativelinear and non-linear models for stock and bond returns in the G7 countries. Besides Markovswitching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regimeswitching...

I apply standard time series models to US housing prices. Forecasts made in 2005 or earlier would have produced stress scenarios that are worse than the subsequent actual change in housing prices. The probability of these scenarios is in the range that banks claim to consider in their risk...

This paper uses dimension asymptotics to study why overfit linear regression models shouldbe compared out-of-sample; we let the number of predictors used by the larger model increasewith the number of observations so that their ratio remains uniformly positive. Under this limittheory, the naive...

In this paper we develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a...

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extendingit in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

By using a unique data set of single-family house transactions, we examine theaccuracy of the cost and sales comparison approach over different forecast horizons. We find that sales comparison values provide better long-term forecaststhan cost values if the economic loss function is symmetric. A...

In this paper we adopt a principal components analysis (PCA) to reduce the dimensionality of the term structure and employ autoregressive models (AR) to forecast principal components which, in turn, are used to forecast swap rates. Arguing in favor of structural variation, we propose data...

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

It is investigated whether Euro-area variables can be forecast better based on synthetic time series for the pre-Euro period or by using just data from Germany for the pre-Euro period. Our forecast comparison is based on quarterly data for the period 1970Q1 - 2003Q4 for ten macroeconomic...