We develop generalised indirect inference procedures that handle equality and inequality constraints on the auxiliary model parameters. We also show that the asymptotic efficiency of such estimators can never decrease by explicitly taking into account Lagrange multipliers associated with...

GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation...

GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation...

GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the...

In this paper, I first provide a unifying approach to Mean-Variance analysis and Value at Risk, which highlights both their similarities and differences. Then I use it to explain how fund managers can take investment decisions within the well-known Mean-Variance allocation framework that satisfy...

We compare the Sharpe rations of investment funds which combine one riskless and one risky asset following: i) timing strategies which forecast excess returns using simple regressions; ii) a strategy which uses multiple regression instead; and iii) a passive allocation which combines the funds in...

We study the effect of the predictability of an assets return on the prices of options on that asset, for models in which returns are serially uncorrelated, yet predictable on the basis of a larger information set. We show that return predictability may matter in a discrete time world,...

We develop generalized indirect estimation procedures that handle equality and inequality constraints on the auxiliary model parameters by extracting information from the relevant multipliers, and compare their asymptotic efficiency to maximum likelihood. We also show that, regardless of the...

We derive computationally simple and intuitive score tests of neglected serial correlation in unobserved component univariate models using frequency domain techniques. In some common situations in which the information matrix is singular under the null we derive extremum tests that are...