The impact of asset price bubbles on liquidity risk measures from a financial institutions perspective

Jacobs, Michael

doi:10.1504/ijbd.2016.077192

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…Protter (2011 and Jarrow et al (2007Jarrow et al ( , 2011Jarrow et al ( , 2014Jarrow et al ( , 2015 apply these new insights to determine the impact that asset price bubbles have on the common risk measures used in practice for the determination of equity capital. Jacobs (2015b) and Jacobs (2016) provide an extension of the latter literature the realms of credit and liquidity risk, respectively. Jacobs (2017) extend Jacobs (2016) with an addition of a sensitivity analysis as well as an empirical implementation with an application to the stress testing of credit risk.…”

Section: Review Of the Literaturementioning

confidence: 99%

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The Detection of Asset Price Bubbles in the Cryptocurrency Markets with an Application to Risk Management and the Measurement of Model Risk

Jacobs¹

2023

IJEF

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This study presents an analysis of the impact of asset price bubbles on the markets for cryptocurrencies and con-siders the standard risk management measure Value-at-Risk (“VaR”). We apply the theory of local martingales, present a styled model of asset price bubbles in continuous time and perform a simulation experiment featuring one- and two-dimensional Stochastic Differential Equation (“SDE”) systems for asset value through a Constant Elasticity of Variance (“CEV”) process that can detect bubble behavior. In an empirical analysis across several widely traded cryptocurrencies, we find that the estimated parameters of one-dimensional SDE systems do not show evidence of bubble behavior. However, if we estimate a two-dimensional system jointly with an equity market index, we do detect a bubble, and comparing bubble to non-bubble economies it is shown that asset price bubbles result in materially inflated VaR measures. The implication of this finding for portfolio and risk management is that rather than acting as a diversifying asset class, cryptocurrencies may not only be highly correlated with other assets but have anti-diversification properties that materially inflate the downside risks in portfolios combining these asset types. We also measure the model risk arising from mispecifying the process driving cryptocurrencies by ignoring the relationship to another representative risk asset through applying the principle of relative entropy, where we find that across all cryptocurrencies studied that the distributions of a distance measure between the simulated distributions of VaR are almost all highly skewed to the right and very heavy-tailed. We find that in the majority of cases that the model risk “multipliers” range in about two to five across cryptocurrencies, estimates which could be applied to establish a model risk reserve as part of an economic capital calculation for risk management of cryptocurrencies.

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Section: Review Of the Literaturementioning

confidence: 99%

“…Jacobs (2015b) and Jacobs (2016) provide an extension of the latter literature the realms of credit and liquidity risk, respectively. Jacobs (2017) extend Jacobs (2016) with an addition of a sensitivity analysis as well as an empirical implementation with an application to the stress testing of credit risk.…”

Section: Review Of the Literaturementioning

confidence: 99%

The Detection of Asset Price Bubbles in the Cryptocurrency Markets with an Application to Risk Management and the Measurement of Model Risk

Jacobs¹

2023

IJEF

View full text Add to dashboard Cite

show abstract

The impact of asset price bubbles on liquidity risk measures from a financial institutions perspective

Cited by 1 publication

References 34 publications

The Detection of Asset Price Bubbles in the Cryptocurrency Markets with an Application to Risk Management and the Measurement of Model Risk

The Detection of Asset Price Bubbles in the Cryptocurrency Markets with an Application to Risk Management and the Measurement of Model Risk

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