Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange...

This paper develops a novel and e¤ective bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. The bootstrap, which combines elements of …xed regressor and wild bootstrap methods, is simple to use. We …rst derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to foreca...

One of the most significant factors influencing the liquidity of the financial market is the amount of currency in circulation. Although the central bank is responsible for the distribution of the currency it cannot assess the demand for the currency, as that demand is influenced by the non-banking sector. Therefore, the amount of currency in circulation has to be forecasted. This paper introdu...

This paper proposes a computationally efficient algorithm for quantifying the impact of interest-rate risk and longevity risk on the distribution of annuity values in the distant future. The algorithm simulates the state variables out to the end of the horizon period and then uses a Taylor series approximation to compute approximate annuity values at the end of that period, thereby avoiding a c...

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…...