References of "Hambuckers, julien"
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See detailA new methodological approach for error distributions selection in Finance
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Conference (2014, April)

In this article, we propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the ... [more ▼]

In this article, we propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we don't use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure (Mercurio and Spokoiny, 2004): the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures (Berk-Jones tests, kernel density-based selection, censored likelihood score, coverage probability) based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behavior in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲]

Detailed reference viewed: 19 (4 ULg)
Peer Reviewed
See detailA new methodological approach for error distributions selection
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Conference (2013, December 15)

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We ... [more ▼]

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we do not use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure: the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures tests based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behaviour in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲]

Detailed reference viewed: 11 (4 ULg)
See detailA new methodological approach for error distributions selection
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Scientific conference (2013, November)

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We ... [more ▼]

Since 2008 and its financial crisis, an increasing attention has been devoted to the selection of an adequate error distribution in risk models, in particular for Value-at-Risk (VaR) predictions. We propose a robust methodology to select the most appropriate error distribution candidate, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we do not use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure: the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures tests based on the so-obtained residuals. These methods enable to assess the global fit of a given distribution as well as to focus on its behaviour in the tails. Finally, we illustrate our methodology on three time series (UBS stock returns, BOVESPA returns and EUR/USD exchange rates). [less ▲]

Detailed reference viewed: 9 (0 ULg)
See detailNew issues for the Goodness-of-fit test of the error distribution : a comparison between Sinh-arscinh and Generalized Hyperbolic distribution
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Scientific conference (2013, April 30)

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional ... [more ▼]

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional estimation and model selection procedures (Berk-Jones (1978) tests, Sarno and Valente (2004) hypothesis testing, Diks et al. (2011) weighting method), based on the local volatility estimator of Mercurio and Spokoiny (2004) and the bootstrap methodology to compare the fit performances of candidate density functions. In particular, we introduce the sinh-arcsinh distributions (Jones and Pewsey, 2009) and we show that this family of density functions provides better bootstrap IMSE and better weighted Kullback-Leibler distances. [less ▲]

Detailed reference viewed: 34 (11 ULg)
See detailNew issues for the Goodness-of-fit test of the error distribution : a comparison between Sinh-arcsinh and Generalized Hyperbolic distributions
Hambuckers, julien ULg; Heuchenne, Cédric ULg

Scientific conference (2013, April 19)

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional ... [more ▼]

In this article, we consider a multiplicative heteroskedastic structure of financial returns and propose a methodology to study the goodness-of-fit of the error distribution. We use non-conventional estimation and model selection procedures (Berk-Jones (1978) tests, Sarno and Valente (2004) hypothesis testing, Diks et al. (2011) weighting method), based on the local volatility estimator of Mercurio and Spokoiny (2004) and the bootstrap methodology to compare the fit performances of candidate density functions. In particular, we introduce the sinh-arcsinh distributions (Jones and Pewsey, 2009) and we show that this family of density functions provides better bootstrap IMSE and better weighted Kullback-Leibler distances. [less ▲]

Detailed reference viewed: 22 (8 ULg)
See detailComments to 'The time inconsistency factor: how banks adapt to their savers mix' (C. Laureti and A. Szafarz, working paper, 2012)
Hambuckers, julien ULg

Scientific conference (2012, October 23)

Comments about 'The time-Inconsistency Factor: How banks adapt to their savers Mix' by C. Laureti and A. Szafarz (working paper, 2012).

Detailed reference viewed: 34 (4 ULg)
Full Text
See detailModélisation d'évènements rares à l'aide de distributions non normales : application en finance avec la fonction sinh-arcsinh
Hambuckers, julien ULg

Master's dissertation (2011)

In 2008, the financial crisis put forward the relative inaccuracy of the market risk forecasting models in the financial industry. In particular, extreme events were shown to be regularly underestimated ... [more ▼]

In 2008, the financial crisis put forward the relative inaccuracy of the market risk forecasting models in the financial industry. In particular, extreme events were shown to be regularly underestimated. This problematic, initially developed in the seminal work of Mandelbrot (1963), is mainly due to financial models using the normal law while empirical evidence show strong leptokurticity in financial time series. This stylized effect is particularly damaging the forecasting of indicators like Value-at-Risk (VAR). In this study, we try to tackle problem by testing a newly-developed probability distribution, never used in finance: sinh-arcsinh function. By creating different datasets from non-parametric and GARCH models, we adjust common functions (normal, t location-scale, GED, gen. hyperbolic) and sinh-arcsinh function on the data. We show that, regarding the leptokurtic datasets extracted from the DJA and the NIKKEI 225, the sinh-arcsinh function performs a better adjustment than any other function tested. We also tested simple VAR models using normal laws, Student’s t or sinh-arcsinh functions, to assess the operational efficiency of the sinh-arcsinh function. We show that models using sinh-arcsinh functions provide more accurate and better in-sample and out-of-sample VAR forecasts than any other model using the normal laws. [less ▲]

Detailed reference viewed: 74 (16 ULg)