A multi-horizon scale for volatility

We decompose volatility of a stock market index both in time and scale using wavelet filters and design a probabilistic indicator for valatilities, analogous to the Richter scale in geophysics. The peak-over-threshold method is used to fit the generalized Pareto probability distribution for the extreme values in the realized variances of wavelet coefficients. The indicator is computed for the daily Dow Jones Industrial Averages index data from 1986 to 2007 and for the intraday CAC 40 data from 1995 to 2006. The results are used for comparison and structural multi-resolution analysis of extreme events on the stock market and for the detection of financial crises.Stock market, volatility, wavelets, multi-resolution analysis, financial crisis.

Price Dynamics in Market with Heterogeneous Investment Horizons and Boundedly Rational Traders

This paper studies the effects of multiple investment horizons and investors' bounded rationality on the price dynamics. We consider a pure exchange economy with one risky asset, populated with agents maximizing CRRA-type expected utility of wealth over discrete investment periods. An investor's demand for the risky asset may depend on the historical returns, so that our model encompasses a wide range of behaviorist patterns. The necessary conditions, under which the risky return can be a stationary iid process, are established. The compatibility of these conditions with different types of demand functions in the heterogeneous agents' framework are explored. We find that conditional volatility of returns cannot be constant in many generic situations, especially if agents with different investment horizons operate on the market. In the latter case the return process can display conditional heteroscedasticity, even if all investors are so-called "fundamentalists" and their demand for the risky asset is subject to exogenous iid shocks. We show that the heterogeneity of investment horizons can be a possible explanation of different stylized patterns in stock returns, in particular, mean-reversion and volatility clustering.Asset pricing, heterogeneous agents, multiple investment scales, volatility clustering.

Investigating value and growth : what labels hide ?

Value and growth investment styles are a concept which has gained extreme popularity over the past two decades, probably due to its practical efficiency and relative simplicity. We study the mechanics of different factors' impact on excess returns in a multivariate setting. We use a panel of stock returns and accounting data from 1979 to 2007 for the companies listed on NYSE without survivor bias for clustering, regression analysis and constructing style based portfolios. Our findings suggest that value and growth labels often hide important heterogeneity of the underlying sources of risks. Many variables, conventionally used for style definitions, cannot be used jointly, because they affect returns in opposite directions. A simple truth that more variables does not necessarily mean better model nicely summaries our results. We advocate a more flexible approach to analyzing accounting-based factors of outperformance treating them separately before or instead of aggregating.Style analysis, value puzzle, pricing anomalies, equity.

Predicting Stock Returns in a Cross-Section : Do Individual Firm chatacteristics Matter ?

It is a common wisdom that individual stocks' returns are difficult to predict, though in many situations it is important to have such estimates at our disposal. In particular, they are needed to determine the cost of capital. Market equilibrium models posit that expected returns are proportional to the sensitivities to systematic risk factors. Fama and French (1993) three-factor model explains the stock returns premium as a sum of three components due to different risk factors : the traditional CAPM market beta, and the betas to the returns on two portfolios, "Small Minus Big" (the differential in the stock returns for small and big companies) and "High Minus Low" (the differential in the stock returns for the companies with high and low book-to-price ratio). The authors argue that this model is sufficient to capture the impact on returns of companies' accounting fundamentals, such as earnings-to-price, cash flow-to-price, past sales growth, long term and short-term past earnings. Using a panel of stock returns and accounting data from 1979 to 2008 for the companies listed on NYSE, we show that this is not the case, at least at individual stocks' level. According to our findings, fundamental characteristics of companies' performance are of higher importance to predict future expected returns than sensitivities to the Fama and French risk factors. We explain this finding within the rational pricing paradigm : contemporaneous accounting fundamentals may be better proxies for the future sensitivity to risk factors, than the historical covariance estimates.Accounting fundamentals, equity performance, style analysis, value and growth, cost of capital.

Predicting Stock Returns in a Cross-Section : Do Individual Firm chatacteristics Matter ?

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/CESFramDP2009.htmClassification JEL : E44, G11, E32.Documents de travail du Centre d'Economie de la Sorbonne 2009.37 - ISSN : 1955-611XIt is a common wisdom that individual stocks' returns are difficult to predict, though in many situations it is important to have such estimates at our disposal. In particular, they are needed to determine the cost of capital. Market equilibrium models posit that expected returns are proportional to the sensitivities to systematic risk factors. Fama and French (1993) three-factor model explains the stock returns premium as a sum of three components due to different risk factors : the traditional CAPM market beta, and the betas to the returns on two portfolios, "Small Minus Big" (the differential in the stock returns for small and big companies) and "High Minus Low" (the differential in the stock returns for the companies with high and low book-to-price ratio). The authors argue that this model is sufficient to capture the impact on returns of companies' accounting fundamentals, such as earnings-to-price, cash flow-to-price, past sales growth, long term and short-term past earnings. Using a panel of stock returns and accounting data from 1979 to 2008 for the companies listed on NYSE, we show that this is not the case, at least at individual stocks' level. According to our findings, fundamental characteristics of companies' performance are of higher importance to predict future expected returns than sensitivities to the Fama and French risk factors. We explain this finding within the rational pricing paradigm : contemporaneous accounting fundamentals may be better proxies for the future sensitivity to risk factors, than the historical covariance estimates.Il est généralement accepté que les rendements des actions individuelles sont difficiles à modéliser et prédire, et pourtant il est souvent nécessaire de disposer de telle modélisation. En particulier, des estimations des rendements attendus au niveau des actions individuelles sont requises pour déterminer les coûts de capital. Les modèles d'équilibre du marché impliquent que les espérances des rendements excessifs sont proportionnelles à leurs sensibilités aux facteurs de risque systémique. Le modèle à trois facteurs de Fama et French (1993) représente la prime de risque comme une somme de trois composantes, qui correspondent au béta de marché, issu de MEDAF traditionnel, et aux sensibilités aux rendements des deux portefeuilles appelés "Small Minus Big" (la différence entre les rendements des actions des entreprises à faible capitalisation boursière par rapport à celles à capitalisation élevée) et "High Minus Low" (la différence entre les rendements des actions des entreprises ayant les ratios Valeur comptable/Prix boursier élevés et faibles). Les auteurs affirment que le modèle à trois facteurs est suffisant pour capter l'impact des indicateurs comptables fondamentaux sur les rendements, ces indicateurs étant, par exemple, le ratio entre le prix de l'action d'une entreprise et son bénéfice par action, la croissance historique des ventes ou des bénéfices par action. En utilisant un échantillon des titres cotés sur NYSE pendant la période de 1979 à 2008, nous montrons que ce n'est pas le cas, au moins si l'analyse est faite au niveau des valeurs individuelles. D'après nos résultats, les indicateurs fondamentaux ont plus d'importance pour prédire les futurs rendements que les sensibilités aux facteurs de Fama et French. Dans le cadre du paradigme rationnel de l'évaluation des actifs, nous proposons une interprétation à ce résultat : les ratios comptables contemporains peuvent mieux approximer les sensibilités futures à des facteurs de risque que les estimations historiques des covariances

Investigating value and growth : what labels hide ?

URL des Documents de travail :http://ces.univ-paris1.fr/cesdp/CESFramDP2007.htmDocuments de travail du Centre d'Economie de la Sorbonne 2007.66 - ISSN : 1955-611XValue and growth investment styles are a concept which has gained extreme popularity over the past two decades, probably due to its practical efficiency and relative simplicity. We study the mechanics of different factors' impact on excess returns in a multivariate setting. We use a panel of stock returns and accounting data from 1979 to 2007 for the companies listed on NYSE without survivor bias for clustering, regression analysis and constructing style based portfolios. Our findings suggest that value and growth labels often hide important heterogeneity of the underlying sources of risks. Many variables, conventionally used for style definitions, cannot be used jointly, because they affect returns in opposite directions. A simple truth that more variables does not necessarily mean better model nicely summaries our results. We advocate a more flexible approach to analyzing accounting-based factors of outperformance treating them separately before or instead of aggregating.Les styles d'investissement valeur et croissance est un concept devenu extrêmement populaire durant les deux dernières décennies ce qui est dû à son efficacité pratique et sa simplicité. Nous étudions l'impact de différents facteurs sur les rendements excessifs dans le cadre multi-varié. Nous utilisons un panel de rendements de titres et de données comptables des entreprises cotées sur le NYSE sans biais de survie pour les années 1979 - 2007 afin d'effectuer un clustering, la régression approprié et la construction des portefeuilles valeur et croissance. On trouve une hétérogénéité importante des facteurs de risque sous-jacents. Parmi les variables traditionnellement utilisées pour définir les styles, on distingue celles qui sont complémentaires et celles qui agissent dans les directions opposées dans le cadre multi-varié, ce qui rend leur utilisation simultanée inefficace. L'ajout de variables supplémentaires n'améliore pas forcément le résultat. Une approche plus flexible à l'analyse de facteurs comptable est proposée

Reducing roundoff errors in numerical integration of planetary ephemeris

Modern lunar-planetary ephemerides are numerically integrated on the observational timespan of more than 100 years (with the last 20 years having very precise astrometrical data). On such long timespans, not only finite difference approximation errors, but also the accumulating arithmetic roundoff errors become important because they exceed random errors of high-precision range observables of Moon, Mars, and Mercury. One way to tackle this problem is using extended-precision arithmetics available on x86 processors. Noting the drawbacks of this approach, we propose an alternative: using double-double arithmetics where appropriate. This will allow to use only double precision floating-point primitives which have ubiquitous support

Current Scenarios for the Demographic Future of the World: The Cases of Russia and Germany

In this article, we explore the demographic future of the world with a focus on scenarios for Russia and Germany. We seek an alternative to the Western standards of scenarios for global demographic development. We consider demographic development both in a positive and negative sense. Our analysis rests on such theoretical structures as the general theory of population, the classical theory of demographic transition, the concepts of the 'second', 'third', and 'fourth' demographic transitions, and scenarios for the 'Eurasian demographic development path'. We employ a range of methods from comparative demography as well as historical analogies, expert evaluations and demographic forecasts. We analyse the patterns of current demographic development in Russia and Germany to explore various demographic scenarios. In the conclusion, we stress the need for Russia and other countries, including Germany, to embark on the 'Eurasian demographic development path' in view of the countries' geographical positions and demographic values, with children being a dominant one. Otherwise, both Germany and Russia may disappear as national states as early as this century. The findings of this study can be used to improve the demographic policies of Russia and Germany

A multi-horizon scale for volatility

URL des Documents de travail :http://ces.univ-paris1.fr/cesdp/CESFramDP2008.htmDocuments de travail du Centre d'Economie de la Sorbonne 2008.20 - ISSN : 1955-611XWe decompose volatility of a stock market index both in time and scale using wavelet filters and design a probabilistic indicator for valatilities, analogous to the Richter scale in geophysics. The peak-over-threshold method is used to fit the generalized Pareto probability distribution for the extreme values in the realized variances of wavelet coefficients. The indicator is computed for the daily Dow Jones Industrial Averages index data from 1986 to 2007 and for the intraday CAC 40 data from 1995 to 2006. The results are used for comparison and structural multi-resolution analysis of extreme events on the stock market and for the detection of financial crises.Nous décomposons la volatilité d'un indice boursier à la fois dans le temps et l'échelle, en utilisant les filtres d'ondelettes et construisons un indicateur probabiliste de la volatilité, analogue à l'échelle de Richter-Gutenberg en géophysique. La méthode des excès est utilisée pour approximer la loi généralisée de Pareto pour les valeurs extrêmes de la variance réalisée des coefficients d'ondelettes. L'indicateur est calculé pour les données journalières de l'indice Jones Industrial Average de 1896 à 2007 et pour les données en haute fréquence de CAC 40 de 1995 à 2006. Les résultats sont utilisés pour la comparaison et l'analyse structurelle multirésolution des évènements extrêmes sur le marché d'actions et pour la détection des crises financières