Investigating the effects of illiquidity on credit risks via new liquidity augmented stochastic volatility jump diffusion model

Date

2021-12-01

Author

Gaygısız Lajunen, Esma
Hekimoglu, Alper

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Liquidity is extremely important not only within the context of financial markets but also in every scale of economic transactions. In this study, within the realm of financial markets, we configure liquidity as an independent stochastic process moderating the fluidity of all transactions and hence dynamically changing asset values. This study's asset value process ignoring liquidity is modelled with a stochastic volatility jump-diffusion (SVJ) model and that model is augmented with the incorporation of a liquidity process. The new model is called liquidity augmented stochastic volatility jump-diffusion (LASVJ) model. The simulation results suggest that LASVJ model outperforms the models without liquidity. The application of LASVJ model on the estimation of probabilities of default and credit risk spreads, using actual financial data of the selected companies listed in Dow Jones 30, reveals that neglecting the liquidity dimension in asset valuation might lead to inefficient assessments of risks. The models without an illiquidity process underestimate the probabilities of default and credit spread risks in comparison to the liquidity augmented model, LASVJ model, and we believe this might have accounted for the ignored risks that caused the 2007-2008 financial crisis in a great degree.

Subject Keywords

Liquidity, stochastic volatility, probability of default, credit spread, ASSET, OPTIONS

URI

https://hdl.handle.net/11511/95343

Journal

OPTIMIZATION

DOI

https://doi.org/10.1080/02331934.2021.2013842

Collections

Department of Economics, Article

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Citation Formats

E. Gaygısız Lajunen and A. Hekimoglu, “Investigating the effects of illiquidity on credit risks via new liquidity augmented stochastic volatility jump diffusion model,” OPTIMIZATION, pp. 0–0, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/95343.