Dynamic factor analysis of carbon allowances prices: From classic Arbitrage Pricing Theory to Switching Regimes

The aim of this paper is to identify the fundamental factors that drive the allowances market and to built an APT-like model in order to provide accurate forecasts for CO2. We show that historic dependency patterns emphasis energy, natural gas, oil, coal and equity indexes as major factors driving the carbon allowances prices. There is strong evidence that model residuals are heavily tailed and asymmetric, thereby generalized hyperbolic distribution provides with the best fit results. Introducing dynamics inside the parameters of the APT model via a Hidden Markov Chain Model outperforms the results obtained with a static approach. Empirical results clearly indicate that this model could be used for price forecasting, that it is effective in and out of sample producing consisten results in allowances futures price prediction.Carbon, EUA, energy, Abritrage Pricing Theory, switching regimes, hidden Markov Chain Model, forecast.

Heavy tail analysis for functional and internet anomaly data

2021 Summer.Includes bibliographical references.This dissertation is concerned with the asymptotic theory of statistical tools used in extreme value analysis of functional data and internet anomaly data. More specifically, we study four problems associated with analyzing the tail behavior of functional principal component scores in functional data and interarrival times of internet traffic anomalies, which are available only with a round-off error. The first problem we consider is the estimation of the tail index of scores in functional data. We employ the Hill estimator for the tail index estimation and derive conditions under which the Hill estimator computed from the sample scores is consistent for the tail index of the unobservable population scores. The second problem studies the dependence between extremal values of functional scores using the extremal dependence measure (EDM). After extending the EDM defined for positive bivariate observations to multivariate observations, we study conditions guaranteeing that a suitable estimator of the EDM based on these scores converges to the population EDM and is asymptotically normal. The third and last problems investigate the asymptotic and finite sample behavior of the Hill estimator applied to heavy-tailed data contaminated by errors. For the third one, we show that for time series models often used in practice, whose non–contaminated marginal distributions are regularly varying, the Hill estimator is consistent. For the last one, we formulate conditions on the errors under which the Hill and Harmonic Moment estimators applied to i.i.d. data continue to be asymptotically normal. The results of large and finite sample investigations are applied to internet anomaly data

Dynamic factor analysis of carbon allowances prices: From classic Arbitrage Pricing Theory to Switching Regimes

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/cesdp2010.htmlDocuments de travail du Centre d'Economie de la Sorbonne 2010.62 - ISSN : 1955-611XThe aim of this paper is to identify the fundamental factors that drive the allowances market and to built an APT-like model in order to provide accurate forecasts for CO2. We show that historic dependency patterns emphasis energy, natural gas, oil, coal and equity indexes as major factors driving the carbon allowances prices. There is strong evidence that model residuals are heavily tailed and asymmetric, thereby generalized hyperbolic distribution provides with the best fit results. Introducing dynamics inside the parameters of the APT model via a Hidden Markov Chain Model outperforms the results obtained with a static approach. Empirical results clearly indicate that this model could be used for price forecasting, that it is effective in and out of sample producing consisten results in allowances futures price prediction.Le but de cet article est d'identifier les facteurs fondamentaux qui influencent le marché du CO2 et de construire un modèle APT à partir de ces fondamentaux. Nous montrons l'importance du gaz naturel, du pétrole, du charbon et des indices boursier. Il est manifeste que les résidus du modèle sont asymétriques et sont correctement modélisés à partir d'une distribution généralisée hyperbolique. Le modèle APT est étendu par l'introduction de paramètres évoluant à l'aide d'une chaine de Markov. Les résultats empiriques indiquent clairement que ce modèle pourrait être utilisé pour la prévision des prix, qu'il est efficace dans et hors de l'échantillon et produit des résultats cohérents pour les allocations de prévision des prix à terme

Financial Risk Measurement for Financial Risk Management

Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also cross-fertilize the academic and practitioner communities, promoting improved market risk measurement technologies that draw on the best of both.Market risk, volatility, GARCH

Extremes of multitype branching random walks: Heaviest tail wins

We consider a branching random walk on a multitype (with Q types of particles), supercritical Galton-Watson tree which satisfies the Kesten-Stigum condition. We assume that the displacements associated with the particles of type Q have regularly varying tails of index α, while the other types of particles have lighter tails than the particles of type Q. In this paper we derive the weak limit of the sequence of point processes associated with the positions of the particles in the nth generation. We verify that the limiting point process is a randomly scaled scale-decorated Poisson point process using the tools developed by Bhattacharya, Hazra, and Roy (2018). As a consequence, we obtain the asymptotic distribution of the position of the rightmost particle in the nth generation

Multi-period conditional distribution functions for heteroscedastic models with applications to VaR.

For a GARCH(1,1) process, we study the large deviation asymptotics at the horizon k and their consequences for extreme quantile estimation. The results are relevant for the estimation of multi-period Value at Risk and prove that the heuristic “square k” rule used in financial risk management is false in the context of GARCH processes.GARCH models – Large deviation probabilities – Laplace integrals – Value at risk.

Extremes of multitype branching random walks: Heaviest tail wins

We consider a branching random walk on a multi(Q)-type, supercritical Galton-Watson tree which satisfies Kesten-Stigum condition. We assume that the displacements associated with the particles of type Q have regularly varying tails of index α, while the other types of particles have lighter tails than that of particles of type Q. In this article, we derive the weak limit of the sequence of point processes associated with the positions of the particles in the nth generation. We verify that the limiting point process is a randomly scaled scale-decorated Poisson point process (SScDPPP) using the tools developed in \cite{bhattacharya:hazra:roy:2016}. As a consequence, we shall obtain the asymptotic distribution of the position of the rightmost particle in the nth generation