This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategi...

We use the information in intraday data to forecast the volatility of crude oil at a horizon of 1 to 66 days using a variety of models relying on the decomposition of realized variance in its positive or negative (semivariances) part and its continuous or discontinuous part (jumps). We show the importance of these decompositions in predictive regressions using a number of specifications. Nevert...

A practice that has become widespread and widely endorsed is that of evaluating forecasts of financial variability obtained from discrete time models by comparing them with high-frequency ex post estimates (e.g. realised volatility) based on continuous time theory. In explanatory financial variability modelling this raises several methodological and practical issues, which suggests an alternati...

This paper examines the predictive content of several macroeconomic variables for the decisions of the Federal Open Market Committee (FOMC) to change its target for the Federal funds rate. To take the discrete nature of the target changes and their serial dependence into account we develop forecasting procedures that are based on dynamic extensions of a multinomial logit model. We …nd that the ...

This paper shows how a mean variance criterion can be applied to a multi period setting in order to obtain efficient portfolios in an asset and liability context. The optimization model allows for rebalancing activities, transaction costs, stochastic volatilities for both assets and liabilities. Furthermore, a general framework for the projection of pension fund liabilities as well as for the g...

In this paper, we present an alternative strategy for estimation of DSGE models when data is available at di¤erent time intervals. Our method is based on a data augmentation technique within Bayesian estimation of structural models and allows us to jointly use data at di¤erent frequencies. The bene…ts achieved via this methodology will be twofold, resolution of time aggregation bias and identi…...

We take a model selection approach to the question of whether a class of adaptive prediction models ("artificial neural networks") are useful for predicting future values of 9 macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolli...

This paper focusses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focusses on average expectations rather than individual expectations is advanced. Other mod...

As extensions to the Black-Scholes model with constant volatility, option pricing models with time-varying volatility have been suggested within the framework of generalized autoregressive conditional heteroskedasticity (GARCH). However, application of the GARCH option pricing model has been hampered by the lack of simulation techniques able to incorporate early exercise features. In the presen...