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Exploiting Distributional Temporal Difference Learning to Deal with Tail Risk
Risks, 2020 In traditional Reinforcement Learning (RL), agents learn to optimize actions in a dynamic context based on recursive estimation of expected values. We show that this form of machine learning fails when rewards (returns) are affected by tail risk, i.e ...
Peter Bossaerts +2 more
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Tail Asymptotics of Deflated Risks [PDF]
, 2013 Random deflated risk models have been considered in recent literatures. In this paper, we investigate second-order tail behavior of the deflated risk X=RS under the assumptions of second-order regular variation on the survival functions of the risk R and
Hashorva, E., Ling, C., Peng, Z.
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SSRN Electronic Journal, 2018 AbstractStrong regulatory actions are needed to combat climate change, but climate policy uncertainty makes it difficult for investors to quantify the impact of future climate regulation. We show that such uncertainty is priced in the option market. The cost of option protection against downside tail risks is larger for firms with more carbon-intense ...
Ilhan, Emirhan +2 more
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SSRN Electronic Journal, 2013 AbstractWe test for the presence of a systematic tail risk premium in the cross section of expected returns by applying a measure of the sensitivity of assets to extreme market downturns, the tail beta. Empirically, historical tail betas help predict the future performance of stocks in extreme market downturns.
Maarten van Oordt, Chen Zhou
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Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model
Mathematics, 2023 Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution.
Hang Lin, Lixin Liu, Zhengjun Zhang
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Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure [PDF]
, 2019 Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector ...
Fairbrother, Jamie +2 more
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Implied Tail Risk and ESG Ratings
Mathematics, 2021 This paper explores whether the high or low ESG rating of a company is related to the level of its implied tail risk, measured on the basis of derivative data by implied skewness and implied kurtosis.
Jingyan Zhang +2 more
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Individual Investors’ Attention to Left Tail Risk [PDF]
Journal of Asset Management and Financing, 2020 Objective: Left tail risk shows the probability of the occurrence of undesirable events. Investors who undergo the left tail risk are likely to experience considerable negative returns since the left tail risk oftentimes continues to the next period ...
Mahshid Shahrzadi, Daryoosh Forooghi
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SSRN Electronic Journal, 2015 SummaryThis paper presents an early warning system as a set of multi‐period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1–2014:12. Pseudo‐real‐time forecasts are generated from: (a) sets of autoregressive and factor‐augmented vector autoregressions (VARs), and (b) sets ...
De Nicolò, Gianni, LUCCHETTA, Marcella
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Tail approximation for credit risk portfolios with heavy-tailed risk factors [PDF]
The Journal of Risk, 2006 We consider a portfolio credit risk model in the spirit of CreditMetrics [15]. The multivariate normally distributed underlying risk factors in that model are replaced by more general multivariate elliptical factors with heavy-tailed marginals, introducing tail-dependence. We consider a full-scale version of the model, i.e.
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