A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics

The analysis of the intraday dynamics of correlations among high-frequency returns is challenging due to the presence of asynchronous trading and market microstructure noise. Both effects may lead to significant data reduction and may severely underestimate correlations if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering correlations, (ii) market microstructure noise is taken into account, (iii) estimation is performed through standard maximum likelihood methods. Our empirical analysis, performed on 1-second NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons.Comment: 30 pages, 10 figures, 7 table

Combining long memory and level shifts in modeling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean- and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in high-frequency measures of volatility whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes, and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons

Combining long memory and level shifts in modeling and forecasting the volatility of asset returns

We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons

Real-Time Time-Varying Equilibrium Interest Rates: Evidence on the Czech Republic

This paper examines (real-time) equilibrium interest rates in the Czech Republic in 2001:1- 2005:12 estimating various specifications of simple Taylor-type monetary policy rules. First, we estimate it using GMM. Second, we apply structural time-varying coefficient model with endogenous regressors to evaluate fluctuations of equilibrium interest rate over time. The results suggest that there is substantial interest rate smoothing and central bank primarily responds to inflation (forecast) developments. The estimated parameters seem to sustain the equilibrium determinacy. We find that the equilibrium interest rates gradually decreased over sample period to the levels comparable to those of in the euro area reflecting capital accumulation, smaller risk premium and successful disinflation in the Czech economy.http://deepblue.lib.umich.edu/bitstream/2027.42/57228/1/wp848 .pd

A numerically efficient implementation of the expectation maximization algorithm for state space models

Peer reviewedPostprin

Estimating Potential GDP for the Romanian Economy. An Eclectic Approach

The paper provides potential output and output gap estimates for the Romanian economy in the period 1998-2008. Our approach consists in combining the production function structural method with several statistical de-trending methods. The contribution of our analysis to the scarce literature dealing with the estimation of the cyclical position of the Romanian economy is twofold. First, we identify the contribution of the production factors to the potential output growth. Second, we aggregate the results obtained through filtering techniques in a consensus estimate, ascribing to each method a weight inversely related to its revision stability. The results suggest for the period 2001-2008 an average annual growth rate of the potential output equal to 5.8%, but on a descending slope, due to the adverse developments in the macroeconomic context.potential GDP, output gap, NAIRU, business cycle