Credit risk analysis using Hidden Markov Model

Oğuz, Hasan Tahsin; Gürgen, Fikret

doi:10.1109/iscis.2008.4717932

Cited by 11 publications

(2 citation statements)

References 7 publications

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“…According to Hassan and Nath (2005) the HMM model has the following advantages: (1) a strong statistical basis; (2) the ability to handle new information robustly; (3) the capacity to calculate and forecast similarity modes more efficiently. In recent years, the HMM model has been applied in economic, financial, and management fields, including economics in general (Leng and Wang, 2014) and the study of financial series fluctuations more specifically (Gregoir and Lenglart, 2000;Hassan and Nath, 2005;Korolkiewicz and Elliott, 2008;Oguz and Gurgen, 2008;Liu, 2010;Roamn et al, 2010;Zhu and Cheng, 2013). In the management field, it has been used to study customer relations management (Bouchaffra and Tan, 2004;Shen and Zhao, 2006;Netzer et al, 2008;Sepideh and Aaghaie, 2011) and online purchasing behaviors (Wu et al, 2005).…”

Section: Asian Economic and Financial Reviewmentioning

confidence: 99%

The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model

Wu¹,

Tung²,

Lee³

2017

Asian Economic and Financial Review

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This paper employ the discrete hidden Markov model (HMM) in order to Contribution/ OriginalityThis paper uses the HMM to capture the optimal path of state transition to observe the trends of fluctuations of out-of-sample data. The results confirm that trends of the real estate business cycle fluctuations are asymmetric and that the average duration of recession periods is longer than that of expansion periods.

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Section: Asian Economic and Financial Reviewmentioning

confidence: 99%

The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model

Wu¹,

Tung²,

Lee³

2017

Asian Economic and Financial Review

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“…In recent times, Hidden Markov models (HMM) have been applied for credit scoring e.g. [9] who compared the performance of HMM and logistic regression in the classification of customers and evaluation of the probability of default. [10] modeled the consumer's credit rating as a discrete time Markov chain process upon incorporating a latent variable which captures the prevailing economic conditions.…”

Section: Introductionmentioning

confidence: 99%

Credit Scoring with Ego-Network Data

Sewe¹,

Ngare²,

Weke³

2019

JMF

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This article investigates a stochastic filtering problem whereby the borrower's hidden credit quality is estimated using ego-network signals. The hidden credit quality process is modeled as a mean reverting Ornstein-Ulehnbeck process. The lender observes the borrower's behavior modeled as a continuous time diffusion process. The drift of the diffusion process is driven by the hidden credit quality. At discrete fixed times, the lender gets ego-network signals from the borrower and the borrower's direct friends. The observation filtration thus contains continuous time borrower data augmented with discrete time ego-network signals. Combining the continuous time observation data and ego-network information, we derive filter equations for the hidden process and the properties of the conditional variance. Further, we study the asymptotic properties of the conditional variance when the frequency of arrival of ego-network signals is increased.

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