Systemic Risk in a Structural Model of Bank Default Linkages

Kreis, Yvonne; Leisen, Dietmar

doi:10.2139/ssrn.2699242

Cited by 3 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This so-called systemic risk defines some trigger events (Schwarcz, 2008), such as a big economic shock or institutional distress, and causes spillover-effects that jeopardise the stability of the whole financial system. Previous researches have been conducted in analyzing this in the framework of financial institutions (Abbassi et al, 2017, Aldasoro and Alves, 2018, and Kreis and Leisen, 2018. Recently, regulators and supranational agencies pay increasing attention to the embedded consequences of tail events in risk management analysis.…”

Section: Introductionmentioning

confidence: 99%

Financial Risk Meter Based on Expectiles

Ren¹,

et al. 2021

SSRN Journal

View full text Add to dashboard Cite

Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use:Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.

show abstract

Section: Introductionmentioning

confidence: 99%

Financial Risk Meter Based on Expectiles

Ren¹,

et al. 2021

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Structural models are well suited for modeling commodity prices such as electricity prices, as they can explicitly incorporate considerations such as network constraints or various supply types that can directly impact the prices. In finance, structural models have been proposed to value credit default swaps (Gökgöz et al 2014) and to describe systemic risk in the banking sector (Aldasoro et al 2017, Kreis andLeisen 2018). Structural models have also been proposed in behavioral economics (Moore andViscusi 1990, DellaVigna 2018) and marketing (Chintagunta et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Implied Markov transition matrices under structural price models

Defourny

Moazeni

2021

Quantitative Finance

View full text Add to dashboard Cite

This paper proposes an approach to compute the implied transition matrices from observations of market data on financial derivatives, when the price of the underlying originates from a structural model and the payoffs are received over a period of time. The structural price model involves a price formation mechanism which computes the price based on a set of Markovian inputs and constrained optimization processes. The developed inference method relies on a linear description of the derivative values in terms of occupation measures of the payoff duration. We establish closed-form expressions between occupation measures and state transitions, which then enable us to characterize implied state transition probabilities consistent with the market data on the derivative values. We develop methods to solve the optimization problem with the resulting nonlinear occupation measure equation. Numerical illustrations of the approach are presented for financial derivatives on network capacities. By applying the method to an electric network, we investigate the relation between financial transmission correct contract values and a range of implied probabilities of congestion in the network.

show abstract