Volatility tail risk under fractionality

Morelli, Giacomo; Magistris, Paolo Santucci de

doi:10.1016/j.jbankfin.2019.105654

Cited by 4 publications

(2 citation statements)

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“…However, most if not all of them fail to take LRD into account. The success of range dependent models for the volatility process ( Gatheral et al, 2018 ; El Euch and Rosenbaum, 2019 ; Abi Jaber et al, 2019 ; Morelli and Santucci de Magistris, 2019 ) inspires us to develop a credit analogue in the present paper. We extend the affine forward intensity (AFI) model ( Gatheral and Keller-Ressel, 2019 ) to incorporate range dependence for both short- and long-memory patterns.…”

Section: Introductionmentioning

confidence: 99%

“…We extend the affine forward intensity (AFI) model ( Gatheral and Keller-Ressel, 2019 ) to incorporate range dependence for both short- and long-memory patterns. Compared with models based on fractional Brownian motions in Biagini et al (2013) ; Morelli and Santucci de Magistris (2019) , our AFI model with LRD provides an explicit exponential transform formula, which is derived in Theorem 3.2 . It enables us to obtain a relatively efficient calibration on CDS spreads while maintaining the ability to detect short- and long-range dependence.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 and credit risk: A long memory perspective

Yin¹,

Han²,

Wong³

2022

Insurance: Mathematics and Economics

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The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.

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