Asset allocation using flexible dynamic correlation models with regime switching

The asset allocation decision is often considered as a trade-off between maximizing the expected return of a portfolio and minimizing the portfolio risk. The riskiness is evaluated in terms of variance of the portfolio return, so that it is fundamental to consider correctly the variance of its components and their correlations. The evidence for the heteroskedastic behaviour of the returns and the time-varying relationships among the portfolio components have recently shifted attention to the multivariate GARCH models with time varying correlation. In this work we insert a particular Markov Switching dynamics in some Dynamic Correlation models to consider the abrupt changes in correlations affecting the assets in different ways. This class of models is very general and provides several specifications, constraining some coefficients. The models are applied to solve a sectorial asset allocation problem and are compared with alternative models.     

Models to date the business cycle: The Italian case

The problem of dating the business cycle has recently received many contributions, with a lot of proposed statistical methodologies, parametric and non-parametric. In general, these methods are not used in official dating, which is carried out by experts, who use their subjective evaluations of the state of economy. In this work we try to apply some statistical procedures to obtain an automatic dating of the Italian business cycle in the last 30 years, checking differences among various methodologies and with the ISAE chronology. The purpose of this exercise is to verify if purely statistical methods can reproduce the turning points detection proposed by economists, so that they could be fruitfully used in official dating. To this end parametric as well as non-parametric methods are employed. The analysis is carried out both aggregating results from single time series and directly in a multivariate framework. The different methods are also evaluated with respect to their ability to timely track (ex post) turning points.     

Realized volatility forecasting: Robustness to measurement errors

In this paper, we suggest how to handle the issue of the heteroskedasticity of measurement errors when specifying dynamic models for the conditional expectation of realized variance. We show that either adding a GARCH correction within an asymmetric extension of the $\mathsf{HAR}$  class ($\mathsf{AHAR}$-$\mathsf{GARCH}$), or working within the class of asymmetric multiplicative error models ($\mathsf{AMEM}$) greatly reduces the need for quarticity/quadratic terms to capture attenuation bias. This feature in $\mathsf{AMEM}$ can be strengthened by considering regime specific dynamics. Model Confidence Sets confirm this robustness both in- and out-of-sample for a panel of 28 big caps and the S&P500 index.     

A realistic model for official interest rate movements and their consequences

This article extends the Vector Autoregression (VAR) methodology to examine the consequences of monetary policy decisions by considering two types of nonlinearities in the determination of official interest rates: (1) the asymmetry related to the different nature of the discrete and infrequent positive and negative interest rate movements determined by central bankers and (2) the convexity in the transmission of policy shocks induced by the nonnegativity constraint in interest rates. For the UK, we find some evidence of both types of asymmetries. In the US, responses to unexpected interest rate shocks are far more symmetric. Results highlight the importance of considering all types of asymmetries when studying monetary transmission.