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R. Jarrow, D. Lando, F. Yu (2003)
DEFAULT RISK AND DIVERSIFICATION: THEORY AND EMPIRICAL IMPLICATIONSMathematical Finance, 15
(2001)
Modeling Default Correlation in Bond Portfolio
Giacomo *, Mark Davis, M. Crowder (2005)
Analysis of default data using hidden Markov modelsQuantitative Finance, 5
S. Kauffman (1974)
The large scale structure and dynamics of gene control circuits: an ensemble approach.Journal of theoretical biology, 44 1
W. Ching, Xi Chen, N. Tsing, Ho-Yin Leung (2009)
Generating probabilistic Boolean networks from a prescribed transition probability matrix.IET systems biology, 3 6
S. Kauffman (1991)
Origins of Order: self-organization and selection in evolution
(2010)
Credit Risk and Business Cycles, Preprint. Department of Economics
Jiawen Gu, W. Ching, T. Siu (2011)
A Markovian infectious model for dependent default riskInt. J. Intell. Eng. Informatics, 1
(1974)
On the Pricing of Corporation Debt: The Risk Structure of Interest Rates
K. Giesecke (2008)
Portfolio Credit Risk: Top-Down vs. Bottom-Up ApproachesJournal of Risk
M. Davis, V. Lo (2001)
Infectious defaultsQuantitative Finance, 1
D. Hackbarth, Jianjun Miao, E. Morellec (2004)
National Centre of Competence in Research Financial Valuation and Risk Management Working Paper No . 229 Capital Structure , Credit Risk and Macroeconomic Conditions
E. Dougherty, Yufei Huang, Seungchan Kim, Xiaodong Cai, R. Yamaguchi (2012)
Genomic Signal Processing [Life Sciences]IEEE Signal Processing Magazine, 29
W. Ching, Ho-Yin Leung, Hao Jiang, Liang Sun, T. Siu (2010)
A Markovian network model for default risk managementInt. J. Intell. Eng. Informatics, 1
W. Ching, M. Ng, W. Ching (2005)
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
F. Black, Myron Scholes (1973)
The Pricing of Options and Corporate LiabilitiesJournal of Political Economy, 81
S. Kauffman, S. Kauffman (1969)
Metabolic stability and epigenesis in randomly constructed genetic nets.Journal of theoretical biology, 22 3
David Li (1999)
On Default Correlation: A Copula Function ApproachJournal of Financial Abstracts eJournal
Jianjun Miao, Pengfei Wang (2010)
Credit Risk and Business CyclesERN: Governance & Ownership (Topic)
W. Ching, Ximin Huang, M. Ng, T. Siu (2006)
Markov Chains: Models, Algorithms and Applications
P. Cristea (2004)
Genomic signal processing7th Seminar on Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004
(2003)
Modelling Dependence with Copulas and Applications to Risk Management, Handbook of Heavy Tailed Distributions in Finance: Editor Svetlozar
T. Siu, W. Ching, E. Fung, M. Ng (2005)
On a multivariate Markov chain model for credit risk measurementQuantitative Finance, 5
P. Embrechts, F. Lindskog, A. McNeil (2003)
Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management
(2010)
Credit Risk and Business Cycles, Preprint. Department of Economics, Boston University, Department of Economics, Hong Kong University of Science and Technology
(2003)
Modelling Dependence with Copulas and Applications to Risk Management, Handbook of Heavy Tailed Distributions in Finance: Editor Svetlozar Todorov Rachev
W. Ching, Shuqin Zhang, M. Ng, T. Akutsu (2007)
An approximation method for solving the steady-state probability distribution of probabilistic Boolean networksBioinformatics, 23 12
D. Madan, Haluk Unal (1998)
Pricing the risks of defaultReview of Derivatives Research, 2
Wing Woo, T. Siu (2004)
A dynamic binomial expansion technique for credit risk measurement: a Bayesian filtering approachApplied Mathematical Finance, 11
I. Shmulevich, E. Dougherty, Seungchan Kim, Wei Zhang (2002)
Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networksBioinformatics, 18 2
S. Kauffman (1969)
Homeostasis and Differentiation in Random Genetic Control NetworksNature, 224
C. Alexander (2001)
Mastering risk, volume 2: applications
D. Duffie, Guillaume Horel, Leandro Saita, Andreas Eckner (2006)
Frailty Correlated DefaultERN: Bayesian Analysis (Topic)
(1997)
Credit Risk+ a Credit Risk Management Framework
I. Shmulevich, E. Dougherty, Wei Zhang (2002)
From Boolean to probabilistic Boolean networks as models of genetic regulatory networksProc. IEEE, 90
R. Jarrow, S. Turnbull (1995)
Pricing Derivatives on Financial Securities Subject to Credit RiskJournal of Finance, 50
(2001)
Modeling Default Correlation in Bond Portfolio, In C. Alescander (ed.) Mastering Risk Volume 2: Applications
A 2 (010) ↔ (011) A 3 (000) ↔ (001), (100) ↔ (010) A
R. Merton (1974)
On the Pricing of Corporate Debt: The Risk Structure of Interest RatesWorld Scientific Reference on Contingent Claims Analysis in Corporate Finance
One of the central issues in credit risk measurement and management is modeling and predicting correlated defaults. In this paper we introduce a novel model to investigate the relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults using the Probabilistic Boolean Network (PBN). The key idea of the PBN is to decompose a transition probability matrix describing correlated defaults of different sectors into several BN matrices which contain information about business cycles. An efficient estimation method based on an entropy approach is used to estimate the model parameters. Using real default data, we build a PBN for explaining the default structure and making reasonably good predictions of joint defaults in different sectors.
Risk and Decision Analysis – IOS Press
Published: Jan 1, 2013
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