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Intra‐Horizon expected shortfall and risk structure in models with jumps
Author(s) -
Farkas Walter,
Mathys Ludovic,
Vasiljević Nikola
Publication year - 2021
Publication title -
mathematical finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.98
H-Index - 81
eISSN - 1467-9965
pISSN - 0960-1627
DOI - 10.1111/mafi.12302
Subject(s) - econometrics , horizon , mathematical economics , expected shortfall , model risk , jump , economics , risk premium , computer science , time horizon , risk management , mathematics , finance , physics , geometry , quantum mechanics
The present article deals with intra‐horizon risk in models with jumps. Our general understanding of intra‐horizon risk is along the lines of the approach taken in Boudoukh et al. (2004); Rossello (2008); Bhattacharyya et al. (2009); Bakshi and Panayotov (2010); and Leippold and Vasiljević (2020). In particular, we believe that quantifying market risk by strictly relying on point‐in‐time measures cannot be deemed a satisfactory approach in general. Instead, we argue that complementing this approach by studying measures of risk that capture the magnitude of losses potentially incurred at any time of a trading horizon is necessary when dealing with (m)any financial position(s). To address this issue, we propose an intra‐horizon analogue of the expected shortfall for general profit and loss processes and discuss its key properties. Our intra‐horizon expected shortfall is well‐defined for (m)any popular class(es) of Lévy processes encountered when modeling market dynamics and constitutes a coherent measure of risk, as introduced in Cheridito et al. (2004). On the computational side, we provide a simple method to derive the intra‐horizon risk inherent to popular Lévy dynamics. Our general technique relies on results for maturity‐randomized first‐passage probabilities and allows for a derivation of diffusion and single jump risk contributions. These theoretical results are complemented with an empirical analysis, where popular Lévy dynamics are calibrated to the S&P 500 index and Brent crude oil data, and an analysis of the resulting intra‐horizon risk is presented.