The recent introduction in Italian mutual fund market of Morningstar performance rating performed by private institutions gives rise to the question of which is the relation between this relative benchmark measure and the other traditional performance measures. This paper provides a comprehensive analysis of the relative benchmark performance measure (Morningstar rating) applied to Italian equity funds. We find that this performance measure is highly correlated with the classical performance measures (Sharpe ratio, Sortino ratio and Treynor ratio) and lowly correlated with the customized benchmark measure (Information ratio). Furthermore, performing a persistence analysis, using non parametric methods called Cross-Product Ratio and Chi-Squared test, we observe that only Morningstar rating measure generates a strong degree of persistence. Our results deviate from most European studies that argue Italian mutual funds display weak persistence
This paper provides the theoretical and operational framework for estimating past values of relevant time series starting from a (limited) information set. We consider a general approach that includes as special cases time series aggregation and temporal and/or spatial disaggregation problems. Furthermore, we explore the relevant problems and the possible solutions associated with a retropolation exercise, evidencing that linear models could be the preferred representation for the production of the needed data. The methodology is designed with a focus on economic time series but it could be considered even for other statistical areas. An empirical example is presented: we analyze the back-calculation of Eu15 Industrial Production Index comparing our approach with the Eurostat official one.
This chapter presents an introduction to the current literature on stochastic volatility models. For these models the volatility depends on some unobserved components or a latent structure.Given the time-varying volatility exhibited by most financial data, in the last two decades there has been a growing interest in time series models of changing variance and the literature on stochastic volatility models has expanded greatly. Clearly, this chapter cannot be exhaustive, however we discuss some of the most important ideas, focusing on the simplest forms of the techniques and models used in the literature.The chapter is organised as follows. Section 1 considers some motivations for stochastic volatility models: empirical stylised facts, pricing of contingent assets and risk evaluation. While section 2 presents models of changing volatility, section 3 focuses on stochastic volatility models and distinguishes between models with continuous and discrete volatility, the latter depending on a hidden Markov chain. Section 4 is devoted to the estimation problem which is still an open question, then a wide range of possibility is given. Sections 5 and 6 introduce some extensions and multivariate models. Finally, in section 7 an estimation program is presented and some possible applications to option pricing and risk evaluation are discussed.Readers interested in the practical utilisation of stochastic volatility models and in the applications can skip section 4.3 without hindering comprehension.
The intent of this paper is the construction of an econometric model able to produce reliable and reasonable forecasts for the US dollar/Euro real exchange rate. In order to achieve this aim, an area-wide model is analysed. The aggregation is motivated by the fact that the Euro-zone is under a single monetary policy. Furthermore, a more parsimonious parametric model enables one to consider an important source of non-stationarity given by the presence of structural breaks using the multivariate cointegration analysis. Against the Meese-Rogoff critique, the out-of-sample one-step-ahead forecasts using actual values of the exogenous produced by the estimated VECM are reasonably satisfactory.Real Exchange Rates, Cointegration, Structural Breaks, Area-WIDE Model, Forecasting,
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