Credit risk analysis using Hidden Markov Model

Author(s):  
Hasan Tahsin Oguz ◽  
Fikret S. Gurgen
Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 66
Author(s):  
Ioannis Anagnostou ◽  
Drona Kandhai

One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian Motion (GBM) is a widely used method for modeling the evolution of exchange rates. An important limitation of GBM is that, due to the assumption of constant drift and volatility, stylized facts of financial time-series, such as volatility clustering and heavy-tailedness in the returns distribution, cannot be captured. We propose a model where volatility and drift are able to switch between regimes; more specifically, they are governed by an unobservable Markov chain. Hence, we model exchange rates with a hidden Markov model (HMM) and generate scenarios for counterparty exposure using this approach. A numerical study is carried out and backtesting results for a number of exchange rates are presented. The impact of using a regime-switching model on counterparty exposure is found to be profound for derivatives with non-linear payoffs.


2018 ◽  
Vol 35 (1-2) ◽  
pp. 51-72 ◽  
Author(s):  
Camilla Damian ◽  
Zehra Eksi ◽  
Rüdiger Frey

AbstractIn this paper we study parameter estimation via the Expectation Maximization (EM) algorithm for a continuous-time hidden Markov model with diffusion and point process observation. Inference problems of this type arise for instance in credit risk modelling. A key step in the application of the EM algorithm is the derivation of finite-dimensional filters for the quantities that are needed in the E-Step of the algorithm. In this context we obtain exact, unnormalized and robust filters, and we discuss their numerical implementation. Moreover, we propose several goodness-of-fit tests for hidden Markov models with Gaussian noise and point process observation. We run an extensive simulation study to test speed and accuracy of our methodology. The paper closes with an application to credit risk: we estimate the parameters of a hidden Markov model for credit quality where the observations consist of rating transitions and credit spreads for US corporations.


2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.


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