Les regroupements de 2002 ont-ils influencé la valeur marchande des propriétés résidentielles à Montréal?

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Mesure de probabilité et les jeux fictifs.

Rapport de recherche

Mesure du Progres Technique: Theories et Methodes

Ce Texte Constitue un Survol des Differentes Approches Destines a Mesurer le Progres Technique. Nous Utilisons une Notation Uniforme Tout au Long des Demonstrations Mathematiques et Nous Faisons Ressortir les Hypotheses Qui Rendent L'application des Methodes Proposees Envisageable et Qui En Limitent la Portee. les Diverses Approches Sont Regroupees D'apres une Classification Suggeree Par Diewert (1981) Selon Laquelle Deux Groupes Sont a Distinguer. le Premier Groupe Contient Toutes les Methodes Definissant le Progres Technique Comme le Taux de Croissance D'un Indice des Outputs Divise Par un Indice des Inputs (Approche de Divisia). L'autre Groupe Inclut Toutes les Methodes Definissant le Progres Technique Comme Etant le Deplacement D'une Fonction Representant la Technologie (Production, Cout, Distance). Ce Second Groupe Est Subdivise Entre L'approche Econometrique,La Theorie des Nombres Indices et L 'Approche Non Parametrique. une Liste des Pricipaux Economistes a Qui L'on Doit les Diverses Approches Est Fournie. Cependant Ce Survol Est Suffisamment Detaille Pour Etre Lu Sans Se Referer aux Articles Originaux.

Décentralisation financière et pays en dévelopement: concepts, mesure et évaluation

Ce texte présente ce qu’est la décentralisation fiscale, fait ressortir ses forces et ses faiblesses et identifie les raisons de son succès, le tout dans le contexte de huit pays en développement en faisant appel à de l’information sur l’Argentine, la Chine, la Colombie, l’Inde, l’Indonésie, le Maroc, le Pakistan et la Tunisie. Le texte est divisé en trois parties. La première expose les concepts pertinents, la seconde présente un certain nombre d’indicateurs quantitatifs et la troisième évalue les conditions de succès de la décentralisation.

Risk Aversion, Intertemporal Substitution, and Option Pricing

This paper develops a general stochastic framework and an equilibrium asset pricing model that make clear how attitudes towards intertemporal substitution and risk matter for option pricing. In particular, we show under which statistical conditions option pricing formulas are not preference-free, in other words, when preferences are not hidden in the stock and bond prices as they are in the standard Black and Scholes (BS) or Hull and White (HW) pricing formulas. The dependence of option prices on preference parameters comes from several instantaneous causality effects such as the so-called leverage effect. We also emphasize that the most standard asset pricing models (CAPM for the stock and BS or HW preference-free option pricing) are valid under the same stochastic setting (typically the absence of leverage effect), regardless of preference parameter values. Even though we propose a general non-preference-free option pricing formula, we always keep in mind that the BS formula is dominant both as a theoretical reference model and as a tool for practitioners. Another contribution of the paper is to characterize why the BS formula is such a benchmark. We show that, as soon as we are ready to accept a basic property of option prices, namely their homogeneity of degree one with respect to the pair formed by the underlying stock price and the strike price, the necessary statistical hypotheses for homogeneity provide BS-shaped option prices in equilibrium. This BS-shaped option-pricing formula allows us to derive interesting characterizations of the volatility smile, that is, the pattern of BS implicit volatilities as a function of the option moneyness. First, the asymmetry of the smile is shown to be equivalent to a particular form of asymmetry of the equivalent martingale measure. Second, this asymmetry appears precisely when there is either a premium on an instantaneous interest rate risk or on a generalized leverage effect or both, in other words, whenever the option pricing formula is not preference-free. Therefore, the main conclusion of our analysis for practitioners should be that an asymmetric smile is indicative of the relevance of preference parameters to price options.

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.