During the last three decades various models have been proposed by the literature to predict the risk of bankruptcy and of firm insolvency, which make use of structural and empirical tools, namely rating system, credit scoring, option pricing and three alternative methods (fuzzy logic, efficient...

Volatility forecasting is one of the main issues in the financial econometrics literature. Volatility measures may be derived from statistical models for conditional variance, or from option prices. In recent times, indices have been suggested which summarize the implied volatility of widely...

This paper analyzes the linkages between banks� profitability and the main real and financial indicators. The results, derived by means of a reduced-form model for the period 1984-2002, highlight a strict relation between all income and cost components and the evolution of the economic...

La psicologia mostra che la probabilità soggettiva associata ad eventi economici futuri viene distorta in modo sistematico, rispetto a quella oggettiva, da elementi psicologici diffusi e persistenti. Lo stesso vale per l'interpretazione retrospettiva dei fatti economici. In particolare, si...

Nonlinear time series models can exhibit components such as long range trends and seasonalities that may be modeled in a flexible fashion. The resulting unconstrained maximum likelihood estimator can be too heavily parameterized and suboptimal for forecasting purposes. The paper proposes the use...

In this paper we address the issue of forecasting Value–at–Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two scales realized volatility, realized kernel as well as the daily range. We propose a dynamic model with a flexible trend...

The transmission mechanisms of volatility between markets can be characterized within a new Markov Switching bivariate model where the state of one variable feeds into the transition probability of the state of the other. A number of model restrictions and hypotheses can be tested to stress the...

Multiplicative Error Models (MEM) can be used to trace the dynamics of non–negative valued processes. Interactions between several such processes are accommodated by the vector MEM and estimated by maximum likelihood (Gamma marginals with copula functions) or by Generalized Method of Moments....

In financial time series analysis we encounter several instances of non–negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a...