Michele Costa

Analysis and Measurement of Poverty. Univariate and Multivariate Approaches and Their Policy Implications. A Case Study: Italy

This research presents and discusses the relative merits and limitations of the univariate and multivariate analyses and measurement of poverty to represent the state of poverty, the poverty ratio time path, and to assess the power of these approaches to identify the main causes of poverty and to inspire the proposal of sound socioeconomic policies. The univariate measurement of poverty analyses and estimates simple and composite poverty ratios advanced in the literature and their limitations to represent observed poverty time path and the lack of structural socioeconomic policy implications. The multivariate analysis of poverty advances forward the French social exclusion theory, and Sen’s analysis of functioning and capability, making them operational, in the sense of providing a poverty ratio and deriving its policy implications. In effect, to these analyses are applied the fuzzy set theory to obtain: (i) the poverty ratio of each household; (ii) the poverty ratio of a population of households; and (iii) very important for its policy implications, the poverty ratio of the population by retained attribute, such as, years of schooling of the household head and spouse (if present), house size and condition, and house endowment of sanitary and other services (drinking water, bath, electricity, etc.). Point (iii) allows the researcher to identify the main causes of structural poverty, i.e., the lack of those attributes that contribute to reproduce poverty from generation to generation. The outcome of this research is applied to the data base provided by the Bank of Italy sample surveys in 1993, 1995, 1998 and 2000, and a comparative analysis of the uni- and multi-variate approaches to the measurement of poverty and their policy implications for Italy completes this study.

A Reduced Rank Regression Approach to Tests of Asset Pricing

Both the Arbitrage Pricing Theory (APT) and the Capital Asset Pricing Model (CAPM) place restrictions of the cross sectional variation of conditional expectations of asset returns and of macro-indicators. The authors show that these restrictions imposed on the reference statistical models lead to special cases of the reduced rank regression model. The maximum likelihood problem is solved by canonical correlation analysis. Likelihood ratio tests about the number of factors underlying stock returns are straightforward to calculate, thus allowing to discriminate between competing financial theories. Moreover LR tests on the relevance of each macroeconomic indicator within a chosen model can be implemented. Some of the tests are illustrated by an application to Italian stock market data. Copyright 1997 by Blackwell Publishing Ltd

Firm size and the Italian Stock Exchange

The presence of a relation between firm size and asset returns is investigated by referring to the Italian Stock Exchange. In order to explain asset return variability, the excess return on a market portfolio as well as the difference between the return on a portfolio of small stocks and the return on a portfolio of large stocks are considered. The resultant two-factor model seems to improve the explanation of the returns of the portfolios formed on size.

The Role of the Normal Distribution in Financial Markets

The hypothesis that financial variables are normally distributed is often rejected in both theoretical studies and extremely specific cases. In the “real” world of financial investors — where risk averse agents mainly hold government bonds, a few equities and do not hold derivatives — the normal distribution still plays a lead role. To show this result, in this paper we focus on a number of efficient portfolios subject to several constraints which make them close to the portfolios held by most of financial agents. A multivariate approach is proposed, which refers to the case of a financial asset manager who cannot only pay attention to the average return of all of his portfolios, but must evaluate the risks associated to each of his portfolios jointly.

A new approach to stock price modelling and the efficiency of the Italian stock exchange

In this paper, we propose a new model of asset prices which takes account of the investment strategies of three different kinds of agents: the market-makers, who operate rationally on the basis of the asset fundamentals, the smart buy-and-sell agents, who intervene when the prices reach particular levels and the non-smart buy-and-sell agents, who trade infrequently, mainly following psychological motivations. The different behavior of these groups of agents can determine temporary inefficiences on financial markets and we show that, by considering these inefficiences, it is possible to improve forecasting of asset prices.

Dynamic component detection in a multifactor model for stock returns

In this paper the factorial structure of some asset returns quoted at the Milan stock exchange is analyzed in order to detect the presence of a dynamic component. The maximum likelihood estimates of the dynamic model, for which a space-state representation and the Kalman filter were used, are compared with the estimates of the static model via the information criteria. There is no evidence of dynamic factors underlying the analyzed samples of asset returns, while one static factor seems to be relevant.