arXiv subjects

Lorenzo Viola

Publications and source records attributed to Lorenzo Viola.

3 recordsLinked to original sources

arXiv2026

Scenario Generation for Time Series and Curves: A Comparison of Nonparametric and Semiparametric Bootstrap

Generating stochastic trajectories for asset classes is an increasingly relevant task in quantitative finance. Traditional approaches, such as the stationary bootstrap, preserve by construction the empirical distribution of asset-class returns, but do not ensure that each individual simulated path is economically realistic: scenarios may be valid in distribution while single trajectories fail to represent plausible states of the world. To address this limitation, we review semiparametric simulation methodologies that combine a parametric structure, which enforces realistic dynamics, with the resampling of model residuals, which preserves the stochastic component observed in historical data. The issue is particularly acute for interest rates, where direct resampling of rate changes may produce implausible yield-curve evolutions despite correct distributional properties. Our empirical analysis shows the effectiveness of semiparametric bootstrap methods based on autoregressive or mean-reverting specifications. In the fixed-income setting, combining these methods with fully parametric term-structure models yields more coherent and realistic simulations of yield-curve dynamics.

q-fin.ST↗

arXiv2026

Reverse Stress Testing for Multivariate Scenarios: A Conditional Framework for Stressed Time Series

This paper develops a methodological framework for reverse stress testing (RST) in which a multivariate stress scenario, coherent with the empirical dependence structure of a market, is reconstructed from a single exogenous shock prescribed on one asset class. The problem is formulated as the maximisation of the conditional density given the imposed shock, and is solved under three progressively weaker distributional assumptions. In the parametric setting, joint Gaussianity of the returns yields a closed-form modal scenario coinciding with the conditional mean of the non-shocked components. In the semiparametric setting, the modal scenario is estimated nonparametrically through the empirical likelihood methodology and the surrounding stressed trajectories are generated via a Gaussian or Student-t local sampling scheme. In the fully nonparametric setting, stressed trajectories are obtained by inverse-distance resampling of the historical observations within a Mahalanobis neighbourhood of the estimated scenario. The three variants are validated on real market data. The simulated scenarios prove to be economically coherent and capable of reproducing the standard risk-reward asymmetry observed in stressed market regimes.

q-fin.RM↗

arXiv2025

Physical Climate Risk in Asset Management

Climate-related phenomena are increasingly affecting regions worldwide, manifesting as floods, water scarcity, and heat waves, significantly impairing companies' assets and productivity. It is essential for asset managers to quantify the exposure of their portfolios to such risk. To this aim, we develop a framework based on the Vasicek model for credit risk that introduces downward jumps due to climate phenomena in a company asset's dynamics. These negative shocks are designed to mirror the negative effect of extreme climate events. The model calibration relies on companies' asset intensity and geographical exposure. We apply the new multivariate firm value model with jumps to assess the impact of climate-related extreme events on expected and unexpected portfolio losses. Our findings indicate that expected losses increase over time, with pronounced differences in exposure observed across sectoral indices. From an environmental policy perspective, these results suggest the need for additional capital buffers to offset losses arising from physical climate risks, particularly in sectors with high asset intensity.

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