Modeling Financial System with Interbank Flows, Borrowing, and Investing

Maheshwari, Aditya; Sarantsev, Andrey

doi:10.3390/risks6040131

Cited by 5 publications

(3 citation statements)

References 34 publications

(94 reference statements)

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“…This function is used when forming an optimal portfolio for an investor focused on income generation. Maheshwari and Sarantsev (2018) is considering an optimal risk management model. The investors are private entities that borrowed money from the central bank at a controlled interest rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of investment portfolio management

Oliinyk¹,

Kozmenko²

2019

Serb J Management

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When creating an investment portfolio, certain financial resources are needed. To form an effective portfolio, a financial institution (enterprise) lacks equity funds, and it attracts external investors to use borrowed funds. In a joint investment project, every participant has his own financial interests and determines his strategy of participation in this project. Therefore, external investors invest money in such a project to obtain additional profit as well as the financial institution expects to get the maximum net profit.The purpose of this study is to develop a strategy for creating an investment portfolio and its further management, taking into

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of investment portfolio management

Oliinyk¹,

Kozmenko²

2019

Serb J Management

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“…Starting from Fouque and , several other papers on systemic risk have studied different versions of this mean-reverting setup (mostly without the common noise). These contributions can be loosely grouped into: systems with stabilisation by a central agent Garnier et al [2013Garnier et al [ , 2017, games with delay Carmona et al [2018], Fouque and Zhang [2018], games with model uncertainty Huang and Jaimungal [2017], utility optimisation by the individual banks and a central bank Maheshwari and Sarantsev [2017], methods for introducing heterogeneity Chong and Kluppelberg, Fang et al [2017], jump-diffusion dynamics Bo and Capponi [2018], Borovykh et al [2018], Benazzoli et al and connections to the theory of risk measures Biagini et al [2019a]. Still focusing on mean-reversion, constraints on the state space have been considered via Feller type square root diffusions in Bo and Capponi [2018], Fouque and , Shkolnikov and Ichiba [2013], Sun [2018] (with various additional features) and, in such a framework, Capponi et al [2019] has recently proposed a network structure with finitely many clusters of banks, where each cluster mean-reverts around different predetermined levels modelling the presence of target leverage ratios.…”

Section: Mean Field Approachesmentioning

confidence: 99%

A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field

Feinstein¹,

Søjmark²

2019

Preprint

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In this work we introduce a model of default contagion that combines the approaches of Eisenberg-Noe interbank networks and dynamic mean field interactions. The proposed contagion mechanism provides an endogenous rule for early defaults in a network of financial institutions. The main result is to demonstrate a mean field interaction that can be found as the limit of the finite bank system generated from a finite Eisenberg-Noe style network. In this way, we connect two previously disparate frameworks for systemic risk, and in turn we provide a bridge for exploiting recent advances in mean field analysis when modelling systemic risk. The mean field limit is shown to be well-posed and is identified as a certain conditional McKean-Vlasov type problem that respects the original network topology under suitable assumptions.

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“…Studies have talked about the effect of risk on performance and competition. Maheshwari and Sarantsev (2018) evaluated risk in a bank and its effect on banks' business through a financial model. Traditional Lerner Index based measure does not account for the risk, particularly NPA (Non-Performing Assets) provision and cost for capital adequacy in the pricing process.…”

Section: Introductionmentioning

confidence: 99%

Bank Competition in India: Some New Evidence Using Risk-Adjusted Lerner Index Approach

Arrawatia

Misra

Dawar

et al. 2019

Risks

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Banks in India have been gone through structural changes in the last three decades. The prices that bank charge depend on the competitive levels in the banking sector and the risk the assets and liabilities carry in banks’ balance sheet. The traditional Lerner Index indicates competitive levels. However, this measure does not account for the risk, and this study introduces a risk-adjusted Lerner Index for evaluating competition in Indian banking for the period 1996 to 2016. The market power estimated through the adjusted Lerner Index has been declining since 1996, which indicates an improvement in competitive condition for the overall period. Further, as indicated by risk-adjusted Lerner Index, the Indian banking system exerts much less market power and hence are more competitive contrary to what is suggested by traditional Lerner index.

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Modeling Financial System with Interbank Flows, Borrowing, and Investing

Cited by 5 publications

References 34 publications

Optimization of investment portfolio management

Optimization of investment portfolio management

A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field

Bank Competition in India: Some New Evidence Using Risk-Adjusted Lerner Index Approach

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