Suchergebnisse

Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by inflation and interest rate expectations as well as a flight-to-safety during times of stress in financial markets. Based on the new DCC-MIDAS model, we construct stock-bond hedge portfolios and show that these portfolios outperform various benchmark portfolios in terms of portfolio risk. While optimal daily weights minimize portfolio risk, we find that portfolio turnover and trading costs can be substantially reduced when switching to optimal monthly weights.

We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothesis that the variable has no explanatory power. Hence, under the null hypothesis the long-term component is constant and the GARCH-MIDAS reduces to the simple GARCH model. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte-Carlo simulation. The usefulness of our procedure is illustrated by an empirical application to S&P 500 return data.

In this paper we develop an asymptotic theory for the parametric GARCH-in-Mean model. The asymptotics is based on a study of the volatility as a process of the model parameters. The proof makes use of stochastic recurrence equations for this random function and uses exponential inequalities to localize the problem. Our results show why the asymptotics for this specification is quite complex although it is a rather standard parametric model. Nevertheless, our theory does not yet treat all standard specifications of the mean function.

We propose a new measure of the expected variance risk premium that is based on a forecast of the conditional variance from a GARCH-MIDAS model. We find that the new measure has strong predictive ability for future U.S. aggregate stock market returns and rationalize this result by showing that the new measure effectively isolates fundamental uncertainty as the factor that drives the variance risk premium.

We examine how the interaction between monetary policy and macroeconomic conditions affects inflation uncertainty in the long-term. The unobservable inflation uncertainty is quantified by means of the slowly evolving long-term variance component of inflation in the framework of the Spline-GARCH model (Engle and Rangel, 2008). For a cross-section of 13 developed economies, we find that long-term inflation uncertainty is high if central bank governors are perceived as less inflation-averse and if the conduct of monetary policy is ad-hoc rather than rule-based.

This paper analyzes volatility spillovers in multivariate GARCH-type models. We show that the cross-effects between the conditional variances determine the persistence of the transmitted volatility innovations. In particular, the effect of a foreign volatility innovation on a conditional variance is even more persistent than the effect of an own innovation unless it is offset by an accompanying negative variance spillover of sufficient size. Moreover, ignoring a negative variance spillover causes a downward bias in the estimate of the initial impact of the foreign volatility innovation. Applying the concept to portfolios of small and large firms, we find that shocks to small firm returns affect the large firm conditional variance once we allow for (negative) spillovers between the conditional variances themselves.

This paper employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in-mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework we examine the twelve potential intertemporal relationships between inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both directly and indirectly via the nominal uncertainty. Output growth boosts inflation but mainly indirectly through a reduction in real uncertainty. Our findings highlight that macroeconomic performance affects nominal and real uncertainty in many ways and that the bidirectional relation between inflation and growth works to a large extend indirectly via the uncertainty channel.

In a simple New Keynesian model, we derive a closed form solution for the inflation persistence parameter as a function of the policy weights in the central bank's Taylor rule. By estimating the time-varying weights that the FED attaches to inflation and the output gap, we show that the empirically observed changes in U.S. inflation persistence during the period 1975 to 2010 can be well explained by changes in the conduct of monetary policy. Our findings are in line with Benati's (2008) view that inflation persistence should not be considered a structural parameter in the sense of Lucas.