We investigate the long-run stock-bond correlation using a novel model that combines the dynamic conditional correlation model with the mixed-data sampling approach. The long-run correlation is affected by both macro-finance variables (historical and forecasts) and the lagged realized...

In this paper we show that the long-run stock and bond volatility and the long-run stock-bond correlation depend on macroeconomic uncertainty. We use the mixed data sampling (MIDAS) econometric approach. The findings are in accordance with the flight-to-quality phenomenon when macroeconomic...

We propose a new simple model incorporating the implication of the quantity theory of money that money growth and inflation should move one for one in the long run, and, hence, inflation should be predictable by money growth. The model fits postwar U.S. data well, and beats common univariate...

Economy-wide effects of shocks to the US federal funds rate are estimated in a state space model with 120 US macroeconomic and financial time series driven by the dynamics of the federal funds rate and a few dynamic factors. This state space system is denoted a factor-augmented VAR (FAVAR) by...

We present and evaluate a numerical optimization method (together with an algorithm for choosing the starting values) pertinent to the constrained optimization problem arising in the estimation of the GARCH models with inequality constraints, in particular the Simplified Component GARCH Model...

We study the short-term price behavior of Phase 2 EU emission allowances. We model returns and volatility dynamics, and we demonstrate that a standard ARMAX-GARCH framework is inadequate for this modeling and that the gaussianity assumption is rejected due to a number of outliers. To improve the...

This paper discusses identification problems in the fractionally cointegrated system of Johansen (2008) and Johansen and Nielsen (2012). It is shown that several equivalent re-parameterizations of the model associated with different fractional integration and cointegration parameters may exist...

This paper studies vector autoregressive models with parsimoniously time-varying parameters. The parameters are assumed to follow parsimonious random walks, where parsimony stems from the assumption that increments to the parameters have a non-zero probability of being exactly equal to zero.We...

We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...

The purpose of the present paper is to analyse a simple bubble model suggested by Blanchard and Watson. The model is defined by y(t) =s(t)?y(t-1)+e(t), t=1,…,n, where s(t) is an i.i.d. binary variable with p=P(s(t)=1), independent of e(t) i.i.d. with mean zero and finite variance. We take ?1...