Insurance valuation: A two-step generalised regression approach

Karim Barigou (), Valeria Bignozzi () and Andreas Tsanakas ()
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Karim Barigou: LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon
Valeria Bignozzi: UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca
Andreas Tsanakas: The Business School (formerly Cass), City, University of London

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Abstract: Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.

Keywords: Solvency II; Cost-of-capital; Dynamic risk measurement; Market-consistent valuation; Quantile regression (search for similar items in EconPapers)
Date: 2021-12-03
New Economics Papers: this item is included in nep-ias and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03043244v2
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Published in ASTIN Bulletin, 2021, pp 1-35. ⟨10.1017/asb.2021.31⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03043244

DOI: 10.1017/asb.2021.31

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