arXiv subjects

Giovanni Bonaccolto

Publications and source records attributed to Giovanni Bonaccolto.

6 recordsLinked to original sources

arXiv2026

Systemic Risk in the European Insurance Sector

This paper studies systemic-risk connectedness in the European insurance sector at three levels of granularity: across major segments of financial markets, across insurance subsectors, and across individual insurance companies. Using a common connectedness framework applied to returns, volatility, value-at-risk, and expected shortfall, we document that insurers are an important component of systemic-risk connectedness, especially during stress episodes. We also provide reduced-form evidence on economically relevant channels in the European institutional setting: aggregate insurer spillovers co-move with term spreads, sovereign spreads, and funding stress, and firm-level insurer-to-bank spillovers vary with sovereign risk and domestic sovereign-bond home bias in a way consistent with a balance-sheet channel. The analysis further reveals substantial heterogeneity across subsectors and identifies a stable core of systemically central insurers in firm-level networks.

q-fin.CP↗

arXiv2026

Asymptotic Theory for Graphical SLOPE: Precision Estimation and Pattern Convergence

This paper studies Graphical SLOPE for precision matrix estimation, with emphasis on its ability to recover both sparsity and clusters of edges with equal or similar strength. In a fixed-dimensional regime, we establish that the root-$n$ scaled estimation error converges to the unique minimizer of a strictly convex optimization problem defined through the directional derivative of the SLOPE penalty. We also establish convergence of the induced SLOPE pattern, thereby obtaining an asymptotic characterization of the clustering structure selected by the estimator. A comparison with GLASSO shows that the grouping property of SLOPE can substantially improve estimation accuracy when the precision matrix exhibits structured edge patterns. To assess the effect of departures from Gaussianity, we then analyze Gaussian-loss precision matrix estimation under elliptical distributions. In this setting, we derive the limiting distribution and quantify the inflation in variability induced by heavy tails relative to the Gaussian benchmark. We also study TSLOPE, based on the multivariate $t$-loss, and derive its limiting distribution. The results show that TSLOPE offers clear advantages over GSLOPE under heavy-tailed data-generating mechanisms. Simulation evidence suggests that these qualitative conclusions persist in high-dimensional settings, and an empirical application shows that SLOPE-based estimators, especially TSLOPE, can uncover economically meaningful clustered dependence structures.

math.ST↗

arXiv2023

Sparse Graphical Modelling via the Sorted L$_1$-Norm

Sparse graphical modelling has attained widespread attention across various academic fields. We propose two new graphical model approaches, Gslope and Tslope, which provide sparse estimates of the precision matrix by penalizing its sorted L1-norm, and relying on Gaussian and T-student data, respectively. We provide the selections of the tuning parameters which provably control the probability of including false edges between the disjoint graph components and empirically control the False Discovery Rate for the block diagonal covariance matrices. In extensive simulation and real world analysis, the new methods are compared to other state-of-the-art sparse graphical modelling approaches. The results establish Gslope and Tslope as two new effective tools for sparse network estimation, when dealing with both Gaussian, t-student and mixture data.

stat.ME↗

arXiv2019

Critical Decisions for Asset Allocation via Penalized Quantile Regression

We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, we investigate whether and to what extent the performance changes when moving from convex to nonconvex penalty functions. Third, we compare different methods to select the optimal tuning parameter which controls the intensity of the penalization. Empirical analyses on real\textendash world data show that these alternative methods outperform the simple LASSO. This evidence becomes stronger when focusing on the extreme risk, which is strictly linked to the quantile regression method.

q-fin.PM↗

arXiv2015

Asset Allocation Strategies Based on Penalized Quantile Regression

It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability measure, useful in evaluating financial portfolios under a pessimistic perspective, since the reward contribution is net of the most favorable outcomes. Moreover, as we consider large portfolios, we also cope with the dimensionality issue by introducing an l1-norm penalty on the assets weights.

q-fin.PM↗

arXiv2014

Modelling and Forecasting the Realized Range Conditional Quantiles

Several studies have focused on the Realized Range Volatility, an estimator of the quadratic variation of financial prices, taking into account the impact of microstructure noise and jumps. However, none has considered direct modeling and forecasting of the Realized Range conditional quantiles. This study carries out a quantile regression analysis to fill this gap. The proposed model takes into account as quantile predictors both the lagged values of the estimated volatility and some key macroeconomic and financial variables, which provide important information about the overall market trend and risk. In this way, and without distributional assumptions on the realized range innovations, it is possible to assess the entire conditional distribution of the estimated volatility. This issue is a critical one for financial decision-makers in terms of pricing, asset allocation, and risk management. The quantile regression approach allows how the links among the involved variables change across the quantiles levels to be analyzed. In addition, a rolling analysis is performed in order to determine how the relationships that characterize the proposed model evolve over time. The analysis is applied to sixteen stocks issued by companies that operate in differing economic sectors of the U.S. market, and the forecast accuracy is validated by means of suitable tests. The results show evidence of the selected variables' relevant impacts and, particularly during periods of market stress, highlights heterogeneous effects across quantiles.

stat.AP↗