References of "Hambuckers, julien"
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See detailTail risk and style dependence in the fund industry: a multivariate extreme value approach
Hambuckers, Julien ULiege; Mhalla, Linda; Lambert, Marie ULiege

Conference (2019, December)

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See detailLASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
Groll, Andreas; Hambuckers, Julien ULiege; Kneib, Thomas et al

in Computational Statistics and Data Analysis (2019), 140

For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting constantly from ... [more ▼]

For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of the response distribution. One of the most established models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high-dimensional data set-ups, classical fitting procedures for GAMLSS often become rather unstable and methods for variable selection are desirable. Therefore, a regularization approach for high-dimensional data set-ups in the framework of GAMLSS is proposed. It is designed for linear covariate effects and is based on L1-type penalties. The following three penalization options are provided: the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, and both group and fused LASSO for categorical predictors. The methods are investigated both for simulated data and for two real data examples, namely Munich rent data and data on extreme operational losses from the Italian bank UniCredit. [less ▲]

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See detailOperational risk, uncertainty, and the economy: a smooth transition extreme value approach
Hambuckers, Julien ULiege; Kneib, Thomas

Conference (2019, June 27)

Detailed reference viewed: 27 (2 ULiège)
See detailTail risk and style dependence in the fund industry: a multivariate extreme value approach
Mhalla, Linda; Hambuckers, Julien ULiege; Lambert, Marie ULiege

Scientific conference (2019, June)

Detailed reference viewed: 25 (1 ULiège)
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See detailInterest rate differentials and the dynamic asymmetry of exchange rates
Hambuckers, Julien ULiege; Ulm, Maren

E-print/Working paper (2019)

In this paper, we revisit the predictive content of interest rates for daily exchange rate returns, taking into account dependencies of higher orders. In particular, we allow for time-varying asymmetries ... [more ▼]

In this paper, we revisit the predictive content of interest rates for daily exchange rate returns, taking into account dependencies of higher orders. In particular, we allow for time-varying asymmetries in the distribution of exchange rates. Then, we study the USD/EUR currency pair over the period 1999-2019. We find a dynamic asymmetry component to be significant and driven by interest rate differentials, but also by general uncertainty and past unexpected shocks. In line with recent currency crash theories, our study suggests that the high yield currency is more likely to appreciate, but is exposed to a higher likelihood of currency crashes. To assess the economic significance of our results, we introduce a directional forecasting approach derived from our model. We show that a trading rule based on these forecasts provides better in-sample and out-of-sample economic performance compared to benchmark models. [less ▲]

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See detailOperational risk, uncertainty, and the economy: a smooth transition extreme value approach
Hambuckers, Julien ULiege; Kneib, Thomas

Conference (2019, May 26)

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See detailFood properties influence grasping strategies in strepsirrhines
Peckre, Louise; Fabre, Anne-Claire; Hambuckers, Julien ULiege et al

in Biological Journal of the Linnean Society (2019)

Though hand-grasping is ubiquitous in primate species, its origins remain uncertain. This is in part because uncertainty about hand skills and grasping strategies persists in strepsirrhines, a ... [more ▼]

Though hand-grasping is ubiquitous in primate species, its origins remain uncertain. This is in part because uncertainty about hand skills and grasping strategies persists in strepsirrhines, a monophyletic group of primates located near the base of the primate tree. In this study, we report and discuss our observations of the different grasping strategies adopted by 85 captive individuals belonging to 22 species of strepsirrhines during grasping of food items having different size and consistency. Our results indicate that even though strepsirrhines do not present variability in their hand-grip types (sole whole-hand power-grip), they are able to adjust their grasping strategy depending on the food properties. Notably, they use the mouth when more precision is needed (i.e. to grasp small items). Moreover, grasping strategies adopted for big items differ depending on food consistency, revealing a new potential essential factor to consider in future research on grasping abilities. We believe that by looking across this important set of species in unconstrained standardized conditions, this study provides valuable insight for further comparative research discussing the potential selective pressures involved in the evolution of hand-grasping. [less ▲]

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See detailSmall Neotropical primates promote the natural regeneration of anthropogenically disturbed areas
Heymann, Eckhard W.; Culot, Laurence ULiege; Knogge, Christoph et al

in Scientific Reports (2019), 9

Increasingly large proportions of tropical forests are anthropogenically disturbed. Where natural regeneration is possible at all, it requires the input of plant seeds through seed dispersal from the ... [more ▼]

Increasingly large proportions of tropical forests are anthropogenically disturbed. Where natural regeneration is possible at all, it requires the input of plant seeds through seed dispersal from the forest matrix. Zoochorous seed dispersal – the major seed dispersal mode for woody plants in tropical forests – is particularly important for natural regeneration. In this study, covering a period of more than 20 years, we show that small New World primates, the tamarins Saguinus mystax and Leontocebus nigrifrons, increase their use of an anthropogenically disturbed area over time and disperse seeds from primary forest tree species into this area. Through monitoring the fate of seeds and through parentage analyses of seedlings of the legume Parkia panurensis from the disturbed area and candidate parents from the primary forest matrix, we show that tamarin seed dispersal is effective and contributes to the natural regeneration of the disturbed area. [less ▲]

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See detailOperational risk, uncertainty, and the economy: a smooth transition extreme value approach
Hambuckers, Julien ULiege; Kneib, Thomas

E-print/Working paper (2019)

We study the link between the distribution of extreme operational losses and the economic context, a fundamental task to compute adequate risk measures over time. In particular, we allow for time-varying ... [more ▼]

We study the link between the distribution of extreme operational losses and the economic context, a fundamental task to compute adequate risk measures over time. In particular, we allow for time-varying dependencies due to structural changes, thanks to a newly-introduced smooth transition Generalized Pareto (GP) regression model. In this model, the parameters of the GP distribution are related to explanatory variables through regression functions, which depend themselves on a predictor of structural changes. Relying on this model, we study the dependence of the monthly loss severity distribution at UniCredit, over the period 2005-2014. As indicator of structural changes, we use the VIX, accounting for the general uncertainty on financial markets. We show that both the goodness-of- fit far in the tail and Value-at-Risk estimates of the total loss distribution obtained from such models are superior to a set of alternatives. We also show that in periods of high uncertainty, conditions favorable to a lax monetary policy are synonym of an increased likelihood of extreme losses. Finally, we discover evidence of a self-inhibition mechanism, where a high number of losses in a recent past are indicative of less extreme losses in the future, probably due to improved monitoring. [less ▲]

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See detailEstimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
Bee, Marco; Hambuckers, Julien ULiege; Trapin

in Quantitative Finance (2019)

The g-and-h distribution is a flexible model with desirable theoretical properties. Especially, it is able to handle well the complex behavior of loss data. However, parameter estimation is difficult ... [more ▼]

The g-and-h distribution is a flexible model with desirable theoretical properties. Especially, it is able to handle well the complex behavior of loss data. However, parameter estimation is difficult, because the density cannot be written in closed form. In this paper we develop an indirect inference method using the skewed-t distribution as instrumental model. We show that the skewed-t is a well suited auxiliary model and study the numerical issues related to its implementation. A Monte Carlo analysis and an application to operational losses suggest that the indirect inference estimators of the parameters and of the VaR outperform the quantile-based estimators. [less ▲]

Detailed reference viewed: 67 (9 ULiège)
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See detailModeling non-stationary operational risk: A smooth-transition distributional regression approach
Hambuckers, Julien ULiege; Kneib, Thomas

Conference (2018, December 15)

The distribution of operational losses is particularly challenging to model, due to the high probability of extremes and the existence of time-varying structural dependencies. In particular, operational ... [more ▼]

The distribution of operational losses is particularly challenging to model, due to the high probability of extremes and the existence of time-varying structural dependencies. In particular, operational loss severity distribution is often concerned with changes in regulations, business cycles or financial crises that affect the dependence structure with potential predictors. To help accounting for this empirical feature, we introduce smooth-transition (ST) Generalized Pareto (GP). In this time-varying regression model, the parameters of the GP distribution are related to explanatory variables through a regression function, which depends itself on a time-varying predictor of structural changes. First, we discuss the computational challenges associated to this class of models. Then, we propose several estimation strategies and investigate their finite sample properties in a simulation study. Eventually, we use our findings to study the time-varying dependence structure of monthly operational risks with market volatility and past extreme events. [less ▲]

Detailed reference viewed: 45 (0 ULiège)
See detailOperational risk, uncertainty, and the economy: a smooth transition extreme value approach
Hambuckers, Julien ULiege; Kneib, Thomas

Scientific conference (2018, April)

We study the link between the distribution of extreme operational losses and the economic context, a fundamental task to compute adequate risk measures over time. In particular, we allow for time-varying ... [more ▼]

We study the link between the distribution of extreme operational losses and the economic context, a fundamental task to compute adequate risk measures over time. In particular, we allow for time-varying dependencies due to structural changes, thanks to a newly-introduced smooth transition Generalized Pareto (GP) regression model. In this model, the parameters of the GP distribution are related to explanatory variables through regression functions, which depend themselves on a predictor of structural changes. Relying on this model, we study the dependence of the monthly loss severity distribution at UniCredit, over the period 2005-2014. As indicator of structural changes, we use the VIX, accounting for the general uncertainty on fi nancial markets. We show that both the goodness-of- t far in the tail and Value-at-Risk estimates of the total loss distribution obtained from such models are superior to a set of alternatives. We also show that in periods of high uncertainty, conditions favorable to a lax monetary policy are synonym of an increased likelihood of extreme losses. Finally, we discover evidence of a self-inhibition mechanism, where a high number of losses in a recent past are indicative of less extreme losses in the future, probably due to improved monitoring. [less ▲]

Detailed reference viewed: 10 (0 ULiège)
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See detailUnderstanding the Economic Determinants of the Severity of Operational Losses: A regularized Generalized Pareto Regression Approach
Hambuckers, julien ULiege; Groll, Andreas; Kneib, Thomas

in Journal of Applied Econometrics (2018), 33(6), 898-935

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit, covering a period of 10 years and 7 different event types. Our goal is to shed light on the dependence ... [more ▼]

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit, covering a period of 10 years and 7 different event types. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of macroeconomic, financial and firm-specific factors. To do so, we use Generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. In this complex distributional regression framework, we perform the selection of the relevant covariates with a state-of-the-art penalized-likelihood estimation procedure relying on $L_{1}$-norm penalty terms of the coefficients. A simulation study indicates that this approach efficiently selects covariates of interest and tackles spurious regression issues encountered when dealing with integrated time series. The results of our empirical analysis have important implications in terms of risk management and regulatory policy. In particular, we found that high Italian unemployment rate and low GDP growth rate in the European Union are associated with smaller probabilities of extreme severities, whereas high values of the VIX and high growth rates of housing prices are associated with more extreme losses. Looking at firm-specific factors, low leverage ratio and high deposit growth rate are associated with a higher likelihood of extreme losses. Lastly, we illustrate the impact of different economic scenarios on the requested capital for operational risk. We find important discrepancies across event types and scenarios. [less ▲]

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See detailA Markov-switching Generalized additive model for compound Poisson processes, with applications to operational losses models
Hambuckers, julien ULiege; Kneib, Thomas; Langrock, Roland et al

in Quantitative Finance (2018), 18(10), 1679-1698

This paper is concerned with modeling the behavior of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk ... [more ▼]

This paper is concerned with modeling the behavior of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk indicators. However, time-varying dependence structures make it a difficult task. To tackle these issues, we formulate a new Markov-switching generalized additive compound process combining Poisson and generalized Pareto distributions. This flexible model takes into account two important features: on the one hand, we allow all parameters of the compound loss distribution to depend on economic covariates in a flexible way. On the other hand, we allow this dependence to vary over time, via a hidden state process. A simulation study indicates that, even in the case of a short time series, this model is easily and well estimated with a standard maximum likelihood procedure. Relying on this approach, we analyze a novel dataset of 819 losses resulting from frauds at the Italian bank UniCredit. We show that our model improves the estimation of the total loss distribution over time, compared to standard alternatives. In particular, this model provides estimations of the 99.9% quantile that are never exceeded by the historical total losses, a feature particularly desirable for banking regulators. [less ▲]

Detailed reference viewed: 90 (12 ULiège)
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See detailOn conditional dynamic skewness and directional forecast of currency exchange rates
Hambuckers, julien ULiege

Conference (2017, December)

This paper studies dynamic skewness and kurtosis specifications for the purpose of directional forecasts of daily exchange rates. To do so, we formulate a GARCH-in-mean model where the innovations follow ... [more ▼]

This paper studies dynamic skewness and kurtosis specifications for the purpose of directional forecasts of daily exchange rates. To do so, we formulate a GARCH-in-mean model where the innovations follow a non-Gaussian sinh-arcsinh distribution with time-varying asymmetry and shape parameters. The structural equations of these parameters allow for an effect of past stochastic shocks, autoregressive terms and interest rate differential on conditional dynamic. This model is used to predict the direction of change of three major currency pairs (USD/EUR, USD/GBP and USD/CHF) over the period 1999-2016. To account for structural breaks, we consider a state-of-the-art CUSUM test based on the probability integral transform [less ▲]

Detailed reference viewed: 20 (3 ULiège)
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See detailA Markov-switching Generalized additive model for compound Poisson processes, with applications to operational losses models
Hambuckers, julien ULiege

Conference (2017, July)

This paper is concerned with modeling the behavior of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk ... [more ▼]

This paper is concerned with modeling the behavior of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk indicators. However, time-varying dependence structures make it a dfficult task. To tackle these issues, we formulate a new Markov-switching generalized additive compound process combining Poisson and generalized Pareto distributions. This flexible model takes into account two important features: on the one hand, we allow all parameters of the compound loss distribution to depend on economic covariates in a flexible way. On the other hand, we allow this dependence to vary over time, via a hidden state process. A simulation study indicates that, even in the case of a short time series, this model is easily and well estimated with a standard maximum likelihood procedure. Relying on this approach, we analyze a novel dataset of 819 losses resulting from frauds at the Italian bank UniCredit. We show that our model improves the estimation of the total loss distribution over time, compared to standard alternatives. In particular, this model provides estimations of the 99.9% quantile that are never exceeded by the historical total losses, a feature particularly desirable for banking regulators. [less ▲]

Detailed reference viewed: 16 (0 ULiège)
Peer Reviewed
See detailA Markov-switching Generalized additive model for compound Poisson processes, with applications to operational losses models
Hambuckers, julien ULiege

Conference (2017, July)

This paper is concerned with modeling the behavior of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk ... [more ▼]

This paper is concerned with modeling the behavior of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk indicators. However, time-varying dependence structures make it a difficult task. To tackle these issues, we formulate a new Markov- switching generalized additive compound process combining Poisson and generalized Pareto distributions. This flexible model takes into account two important features: on the one hand, we allow all parameters of the compound loss distribution to depend on economic covariates in a flexible way. On the other hand, we allow this depen- dence to vary over time, via a hidden state process. A simulation study indicates that, even in the case of a short time series, this model is easily and well estimated with a standard maximum likelihood procedure. Relying on this approach, we analyze a novel dataset of 819 losses resulting from frauds at the Italian bank UniCredit. We show that our model improves the estimation of the total loss distribution over time, compared to standard alternatives. In particular, this model provides estimations of the 99.9% quantile that are never exceeded by the historical total losses, a feature particularly desirable for banking regulators. [less ▲]

Detailed reference viewed: 17 (2 ULiège)
Peer Reviewed
See detailUnderstanding the Economic Determinants of the Severity of Operational Losses: A regularized Generalized Pareto Regression Approach,
Hambuckers, julien ULiege

Conference (2017, June)

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit, covering a period of 10 years and 7 di erent event types. Our goal is to shed light on the dependence ... [more ▼]

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit, covering a period of 10 years and 7 di erent event types. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of macroeconomic, financial and fi rm-speci c factors. To do so, we use Generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. In this complex distributional regression framework, we perform the selection of the relevant covariates with a state-of-the-art penalized-likelihood estimation procedure relying on L1-norm penalty terms of the coefficients. A simulation study indicates that this approach efficiently selects covariates of interest but also tackles spurious regression issues encountered when dealing with integrated time series of covariates. The results of our empirical analysis have important implications in terms of risk management and regulatory policy. In particular, we found that high unemployment rate and low economic growth are associated with smaller probabilities of extreme severities, whereas high volatility on the financial market is associated with more extreme losses. Looking at firm speci c factors, a commercial strategy driven by non-interest incomes is associated with an increased likelihood of extreme severities. Last, we illustrate the impact of several economic scenarios on the requested capital of the total operational loss, and find important discrepancies across loss types and scenarios. [less ▲]

Detailed reference viewed: 24 (3 ULiège)