This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. This paper proposes a new way of computing a coincident indic...

We find that covariance matrix forecasts for an international interest rate portfolio generated by a model that incorporates interest-rate level volatility effects perform best with respect to statistical loss functions. However, within a value-at-risk (VaR) framework, the relative performance of the covariance matrix forecasts depends greatly on the VaR distributional assumption. Simple foreca...

We develop a specification test of predictive densities based on that the generalized residuals of correctly specified predictive density models are i.i.d. uniform. The proposed sequential test examines the hypotheses of serial independence and uniformity in two stages, wherein the first stage test of serial independence is robust to violation of uniformity. The approach of data driven smooth t...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VAR...

This paper addresses the following issue: given a set of daily observations on an asset (historical opening, closing, high and low prices), how should one go about estimating the asset’s volatility? We use high-frequency data on very liquid assets to construct daily realized volatility series, which enables us to treat volatility as observed rather than latent. We then compare the empirical per...

Many authors have documented that it is challenging to explain exchange rate fluctuations with macroeconomic fundamentals: a random walk forecasts future exchange rates better than existing macroeconomic models. This paper applies newly developed tests for nested model that are robust to the presence of parameter instability. The empirical evidence shows that for some countries we can reject th...

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...

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 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...