Archimedean copulae for risk measurement

Luca, Giovanni De; Rivieccio, Giorgia

doi:10.1080/02664760802520785

Cited by 8 publications

(11 citation statements)

References 13 publications

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“…Considering the dependence structure between the stock returns via copula [17,26,34] is another interesting research topic. A first approach to develop NPI in order to take such dependence into account has recently been published [13] and research on this topic is ongoing.…”

Section: Discussionmentioning

confidence: 99%

Nonparametric predictive inference for stock returns

Baker¹,

Coolen‐Maturi

Coolen

2016

Journal of Applied Statistics

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalise the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the inferences to adapt to the available data and that the level of imprecision in inferences can reflect the amount of data on which these are based. This paper introduces nonparametric predictive inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their future aggregate returns. The proposed NPI methods are illustrated using historical stock market data.

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Section: Discussionmentioning

confidence: 99%

Nonparametric predictive inference for stock returns

Baker¹,

Coolen‐Maturi

Coolen

2016

Journal of Applied Statistics

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“…The non-parametric bivariate coefficient of lower tail dependence, λ L NP , can be obtained as (De Luca and Rivieccio, 2009) λ L NP (k) = P(X 2 ≤ x 2 * |X 1 ≤ x 1 * ), or conversely, where x i * is assumed to be µ i − kσ i . This statistic depends on k.…”

Section: Non Parametric Tail Dependence Measuresmentioning

confidence: 99%

“…A multivariate generalization of the tail dependence coefficients (De Luca and Rivieccio, 2009) consists in to consider h variables and the conditional probability associated to the remaining n -h var iables, given, respectively, by It is simple to show that Ĉ is strictly related to the copula function through the following relationship…”

Section: Non Parametric Tail Dependence Measuresmentioning

confidence: 99%

A Copula-Based Approach to Investigate Vertical Shock Price Transmission in the Italian Hog Market

Capitanio

Adinolfi

Goodwin³

et al. 2019

New Medit

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It is a stylized fact that the Italian farmers do not benefit of casual structure along value chain. Conversely, retailers could advantage of any positive shock price changes occurred in the wholesale supply chain. We investigate the presence of shock price vertical contagion in the Italian hog market, describing the dependence structure along the supply chain and assessing the degree of extreme value dependence. The approach followed is non linear and copula-based, applied on weekly data of hog price changes referred to Italian farm, wholesale and retail branch chain, over the period 1994-2015. In particular, the objective of the analysis consists in to obtain a measure of the relationship between extreme events of returns, estimating the tail dependence coefficients of copula functions involved in the analysis. The empirical findings highlight the asymmetry of price transmission along the hog Italian supply chain, more relevant for wholesale-retail pair.

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“…However, intraday time series of returns for each asset are considered here, so a different marginal model class is specified. We choose copula methods to account for multivariate dependence because of their versatility in reproducing nonlinear dependence: see for example Cherubini et al (2004), De Luca andRivieccio (2009), and Kurowicka and Joe (2011). As far as we know, this is the first study using MCGARCH and D-vine copula models to forecast IVaR.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, this is the first study using MCGARCH and D-vine copula models to forecast IVaR. We choose copula methods to account for multivariate dependence because of their versatility in reproducing nonlinear dependence: see for example Cherubini et al (2004), De Luca andRivieccio (2009), and Kurowicka and Joe (2011).…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting intraday VaR of an exchange rate portfolio

Abbara

Zevallos

2018

Journal of Forecasting

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The main task of this work was to predict, for the next 15 minutes, the value‐at‐risk (VaR) of an equally weighted portfolio composed of four exchange rates against the American dollar: Japanese yen, euro, Australian dollar and Swiss franc. The dataset consists of transaction prices of each asset recorded every 15 minutes, from January 7, 2013 to December 31, 2013. For each time series, the multiplicative‐component generalized autoregressive conditional heteroskedasticity model of Engle and Sokalska (Journal of Financial Econometrics, 2012, 10, 54–83) is fitted, and the dependence among the series is modeled by a D‐vine pair‐copula. VaR predictions are estimated based on simulated observations of the fitted model following the proposal of Berg and Aas (European Journal of Finance, 2009, 15, 639–659). The proposed method presents good results in terms of out‐of‐sample intraday VaR forecasting.

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Archimedean copulae for risk measurement

Cited by 8 publications

References 13 publications

Nonparametric predictive inference for stock returns

Nonparametric predictive inference for stock returns

A Copula-Based Approach to Investigate Vertical Shock Price Transmission in the Italian Hog Market

Modeling and forecasting intraday VaR of an exchange rate portfolio

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