Editors’ introduction: Heavy tails and stable Paretian distributions in econometrics

Dufour, Jean–Marie; Kurz‐Kim, Jeong‐Ryeol

doi:10.1016/j.jeconom.2013.11.006

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…[2]), social sciences (see e.g. [3]) or econometrics (see [4] and the references therein). For this reason, the stable Paretian distribution has been proposed as an alternative model to the Gaussian distribution in many different frameworks.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic stochastic dominance rules for sums of i.i.d. random variables

Ortobelli

Lando

Petronio

et al. 2016

Journal of Computational and Applied Mathematics

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Section: Introductionmentioning

confidence: 99%

Asymptotic stochastic dominance rules for sums of i.i.d. random variables

Ortobelli

Lando

Petronio

et al. 2016

Journal of Computational and Applied Mathematics

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“…Clauset et al (2009)), the social sciences (see e.g. Kendall (1961)), and econometrics (see Dufour and Kurz-Kim (2014) and the references therein). For this reason, the stable Paretian distribution has been proposed as an alternative model to the Gaussian distribution in many different frameworks, especially in finance.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Multivariate Dominance: A Financial Application

Lozza

Lando

Petronio

et al. 2016

Methodol Comput Appl Probab

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We propose a multivariate stochastic dominance relation aimed at ranking different financial markets/sectors from the point of view of a non-satiable risk averse investor. In particular, we assume that the vector of returns of a given market is in the domain of attraction of a symmetric stable Paretian law in order to take into account the asymptotic behaviour of the financial returns. We determine the stochastic dominance rule for stable symmetric distributions, where the stability parameter plays a crucial role. Consequently, the multivariate rule for ordering markets is based on a comparison between i) location parameters, ii) dispersion parameters, and iii) stability indices. Finally, we apply the method to the equity markets of the four countries with the highest gross domestic product in 2013, namely, the US, China, Japan and Germany. In this empirical comparison we examine the ex ante and ex post dominance between stock markets, either assuming that the returns are jointly (or conditionally, for a robust approach) Gaussian distributed, or in the domain of attraction of a stable sub-Gaussian law.

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“…J times sj,α = M m=1 ( m j=1 ej) −1/α where {ej} is drawn from the exponential distribution. 7 See, for example, two special issues on heavy-tailed data, Paolella, Renault, Samorodnitsky, and Varedas (2013) andDufour and Kims (2014).…”

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confidence: 99%

Time Series Estimation of the Dynamic Effects of Disaster-Type Shock

Davis¹,

Ng²

2021

Preprint

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The paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to study the dynamic effects of disaster-type shocks with infinite variance. We show that the least squares estimates of the VAR are consistent but have non-standard properties. Second, it is shown that the restrictions imposed on a SVAR can be validated by testing independence of the identified shocks. The test can be applied whether the data have fat or thin tails, and to over as well as exactly identified models. Third, the disaster shock is identified as the component with the largest kurtosis, where the mutually independent components are estimated using an estimator that is valid even in the presence of an infinite variance shock. Two applications are considered. In the first, the independence test is used to shed light on the conflicting evidence regarding the role of uncertainty in economic fluctuations. In the second, disaster shocks are shown to have short term economic impact arising mostly from feedback dynamics.

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Editors’ introduction: Heavy tails and stable Paretian distributions in econometrics

Cited by 3 publications

References 9 publications

Asymptotic stochastic dominance rules for sums of i.i.d. random variables

Asymptotic stochastic dominance rules for sums of i.i.d. random variables

Asymptotic Multivariate Dominance: A Financial Application

Time Series Estimation of the Dynamic Effects of Disaster-Type Shock

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