Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
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
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
274
views
0
downloads
Cite This
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
Suggestions
OpenMETU
Core
EXAMINATION OF BOND RISK PREMIA FROM THE BANKING PERSPECTIVE
Orhan, Selim; Danışoğlu, Seza; Department of Financial Mathematics (2022-5-10)
Banks are considered as the marginal and sophisticated investors of financial markets. This is evident in the Haddad and Sraer (2020) study that examines the US government bond excess returns. This study extends the Haddad/Sraer analysis to the Turkish government bond market. According to the forecasting results, exposure ratio provides explanatory power over bond excess returns, especially for longer maturities. On the other hand, output gap and industrial growth present strong in-sample forecasting power ...
Empirical comparison of portfolio risk diversification algorithms
Yerli, Çiğdem; Kestel, Sevtap Ayşe; Schindler, Nilüfer; Department of Financial Mathematics (2018)
The enhanced correlations during global financial crisis has revealed that simple asset allocation portfolios prove to be not well-diversified across different risk factors, which makes the risk based asset allocation strategies popular. However, the strategies still construct the risk concentrated portfolios due to the correlation among the asset classes. As a result, risk allocation among uncorrelated risk factors instead of risk allocation among asset classes have become widely used. This thesis aims to di...
Efficient simulation and modelling of counterparty credit risk
Hekimoğlu, Alper Ali; Uğur, Ömür; Kestel, Sevtap Ayşe; Department of Financial Mathematics (2018)
After 2008-2009 crisis, measurement of Counterparty Credit risk has become an essential part of Basel-III regulations. The measurement involves a complex calculation, simulation and scenario generation process which involve a heavy computational cost. Moreover, the counterparty default calculation is an important part depending on scenario generation and state of the economy, state of the counterparty, liquidity as well as the bank itself. In this thesis we develop flexible structural credit risk models and...
Default risk of wage-indexed payment mortgage in Turkey
Erol, Işıl; Patel, K (2005-09-01)
This paper analyses default risk of wage-indexed payment mortgage (WIPM) in Turkey in comparison with other standard mortgage contracts originated in high inflationary economies. Emlak Bank launched WIPM linked to Civil Service employees' wage (CSW) index during high inflationary period of late 1990s. Concurrently, the government introduced a policy linking CSW index to semi-annual expected rate of inflation in an attempt to facilitate housing finance for the fastest growing sector of the population. We fin...
Probability of default modelling using macroeconomic factors.
Tokmak, Bahri; Özmen, Erdal; Department of Economics (2020)
As a consequence from the recent global financial crisis, regulatory frameworks are continuously improved in order to limit the banks’ risk exposure. Two of the amendments are Basel III and IFRS 9. Basel III regulates the capital a bank is required to hold while IFRS 9 is an accounting standard for how banks should classify their assets and estimate their future credit losses. Mutually for both Basel III and IFRS 9 is the estimation of future credit losses which include the probability of default in the cal...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
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.