Modelling the demand for M3 in the Euro area

Modelling monetary transmission is central to understanding the role of monetary policy in the Euro area, and money demand is commonly seen as a link in that transmission mechanism. Since the beginning of the 1990s, many studies have suggested that the demand for Euro area broad money is stable over the long run because the estimation of an area-wide demand for money function provides an appropriate solution to a number of potential causes of misspecification of the single-country relations (such as spillover effects and currency substitution), and enjoys the positive consequences of a statistical averaging effect. On the other side, it must be stressed that previous benefits can be achieved at the risk of introducing parameter heterogeneity into the area-wide relationship. In order to shed some light on the issue, this study is first devoted to an analysis of the main econometric features of the money M3 demand at Euro area and single country levels, then it compares the two sets of results in a common framework that, differently from all previous studies, explicitly takes account of the potential nonstationarity of the variables of interest in both estimation and testing phases. The comparison shows that the area-wide money demand is more smooth and less subject to shocks than the single-country ones. Finally, a number of poolability tests run over subgroups highlight that low precision associated with the estimates of the parameters of the national models makes it impossible to exclude that their long-run specifications do in fact coincide.     

Estimation of objective and risk-neutral distributions based on moments of integrated volatility

In this paper, we present an estimation procedure which uses both option prices and high-frequency spot price feeds to estimate jointly the objective and risk-neutral parameters of stochastic volatility models. The procedure is based on a method of moments that uses analytical expressions for the moments of the integrated volatility and series expansions of option prices and implied volatilities. This results in an easily implementable and rapid estimation technique. An extensive Monte Carlo study compares various procedures and shows the efficiency of our approach. Empirical applications to the Deutsche mark–US dollar exchange rate futures and the S&P 500 index provide evidence that the method delivers results that are in line with the ones obtained in previous studies where much more involved estimation procedures were used.     

Algorithmic Pricing What Implications for Competition Policy?

Review of Industrial Organization - Pricing decisions are increasingly in the “hands” of artificial algorithms. Scholars and competition authorities have voiced concerns that those...     

DIFFUSION COEFFICIENT ESTIMATION AND ASSET PRICING WHEN RISK PREMIA AND SENSITIVITIES ARE TIME VARYING: A COMMENT

The purpose of this note is to analyze the diffusion coefficient estimator suggested by Chesney, Elliott, Madan, and Yang (1993). I start by correcting their formula (4.1), and by showing that their procedure is a member of a class of estimators sharing the same Milstein approximation. I then show how to select the minimum variance estimator (for constant μσ) within a two-parameter subclass of procedures which do not depend on the current realization of the process. I also show that if μ is small the best procedure only allows moderate reduction in variance with respect to the classical quadratic variation estimator (which is a member of the same class). the note concludes by highlighting the fact that the empirical use of the filtered volatilities poses an error in variables problem which can be addressed using instrumental variables methods.     

Mean–variance econometric analysis of household portfolios

We investigate households' portfolio choice using a microeconometric approach derived from mean–variance optimization. We assume that households have heterogeneous expectations on the distribution of excess returns and that they cannot take short positions in risky assets. Assuming two such assets, we derive an explicit solution of the model characterized by four possible portfolio regimes, which are analyzed using two structural probit and tobit specifications with three latent state variables. Both specifications are estimated by weighted maximum likelihood on a cross‐section of US households drawn from the 2004 SCF. The tobit specification is simulated in order to evaluate the regressors' effects on regime probabilities and asset demands. We also assess to what extent the predicted state variables are consistent with the self‐reported expected returns and risk aversion elicited from the SCF questionnaire. Copyright © 2009 John Wiley & Sons, Ltd.