Investigating the effects of illiquidity on credit risks via new liquidity augmented stochastic volatility jump diffusion model. (3rd August 2022)

• Record Type: Journal Article
• Authors: Gaygısız, Esma; Karasan, Abdullah; Hekimoğlu, Alper
• Is Part Of: Optimization. Volume 71:Number 8(2022)
• Page Start: 2421
• Page End: 2449
• Publication Date: 2022-08-03
• Subjects: Liquidity -- stochastic volatility -- probability of default -- credit spread; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2021.2013842
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract : 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 ( S V J ) model and that model is augmented with the incorporation of a liquidity process. The new model is called liquidity augmented stochastic volatility jump-diffusion ( L A S V J ) model. The simulation results suggest that L A S V J model outperforms the models without liquidity. The application of L A S V J 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, L A S V J model, and we believe this might have accounted for the ignored risks that caused the 2007–2008 financial crisis in a great degree.