This paper deals with the problem of estimation and prediction in a compound Poisson ECOGARCH(1, 1) model. For this we construct a quasi maximum likelihood estimator under the assumption that all jumps of the log-price process are observable. Since these jumps occur at unequally spaced time points, it is clear that the estimator has to be computed for irregularly spaced data. Assuming normally ...

Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-deter...

This paper analyzes the ability of both economic variables and moving-average rules to forecast the monthly U.S. equity premium using out-of-sample tests for 1960–2008. Both approaches provide statistically and economically significant out-of-sample forecasting gains, which are concentrated in U.S. business-cycle recessions. Nevertheless, economic variables and moving-average rules capture diff...

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...

We propose a simple but effective estimation procedure to extract the level and the volatility dynamics of a latent macroeconomic factor from a panel of observable indicators. Our approach is based on a multivariate conditionally heteroskedastic exact factor model that can take into account the heteroskedasticity feature shown by most macroeconomic variables and relies on an iterated Kalman fil...

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 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...