scholarly journals On the Study of Reduced-Form Approach and Hybrid Model for the Valuation of Credit Risk

2015 ◽  
Vol 05 (02) ◽  
pp. 129-141
Author(s):  
Olaronke Helen Edogbanya ◽  
Sunday Emmanuel Fadugba
Keyword(s):  
Credit Risk ◽  
Hybrid Model ◽  
Reduced Form ◽  
Form Approach

scholarly journals Valuation of Exchange Option with Credit Risk in a Hybrid Model

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2091
Author(s):  
Geonwoo Kim
Keyword(s):  
Credit Risk ◽  
Hybrid Model ◽  
Structural Model ◽  
Risk Model ◽  
Probabilistic Approach ◽  
Option Price ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Reduced Form ◽  
Exchange Option

In this paper, the valuation of the exchange option with credit risk under a hybrid credit risk model is investigated. In order to build the hybrid model, we consider both the reduced-form model and the structural model. We adopt the probabilistic approach to derive the closed-form formula of an exchange option price with credit risk under the proposed model. Specifically, the change of measure technique is used repeatedly, and the pricing formula is provided as the standard normal cumulative distribution functions.

Credit Risk and the Term Structure of Lease Rates: A Reduced Form Approach

2008 ◽  
Vol 37 (3) ◽  
pp. 281-298 ◽  
Author(s):  
Brent W. Ambrose ◽  
Yildiray Yildirim
Keyword(s):  
Credit Risk ◽  
Term Structure ◽  
Reduced Form ◽  
Form Approach

Union Impact: A Reduced Form Approach

1977 ◽  
Vol 59 (4) ◽  
pp. 503 ◽  
Author(s):  
Lawrence M. Kahn
Keyword(s):  
Reduced Form ◽  
Union Impact ◽  
Form Approach

Replication of Contingent Claims in a Reduced-Form Credit Risk Model with Discontinuous Asset Prices

2006 ◽  
Vol 22 (4) ◽  
pp. 661-687 ◽  
Author(s):  
Tomasz R. Bielecki ◽  
Monique Jeanblanc ◽  
Marek Rutkowski
Keyword(s):  
Credit Risk ◽  
Asset Prices ◽  
Risk Model ◽  
Contingent Claims ◽  
Reduced Form ◽  
Credit Risk Model

Demystifying the environmental Kuznets curve: turning a black box into a policy tool

1997 ◽  
Vol 2 (4) ◽  
pp. 465-484 ◽  
Author(s):  
THEODORE PANAYOTOU
Keyword(s):  
Economic Growth ◽  
Low Income ◽  
Reduced Form ◽  
Kuznets Curve ◽  
Income Levels ◽  
Policy Tool ◽  
Explicit Consideration ◽  
Speed Up ◽  
Form Approach

The reduced-form approach to the income–environment relationship has been a useful first step towards answering the question of how economic growth affects the environment. However, without an explicit consideration of the underlying determinants of environmental quality, the scope for policy intervention is unduly circumscribed. In this paper a modest attempt is made to incorporate explicit policy considerations into the income–environment relationship and to explore its determinants as a step towards a better understanding of this relationship and its potential as a policy tool. The role of the rate of economic growth and population density is also explored. A main finding is that at least in the case of ambient SO2 levels, policies and institutions can significantly reduce environmental degradation at low income levels and speed up improvements at higher income levels, thereby flattening the EKC and reducing the environmental price of economic growth.

Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

2019 ◽  
Vol 28 (05) ◽  
pp. 1950017 ◽  
Author(s):  
Guotai Chi ◽  
Mohammad Shamsu Uddin ◽  
Mohammad Zoynul Abedin ◽  
Kunpeng Yuan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Logistic Regression ◽  
Credit Risk ◽  
Risk Prediction ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Hybrid Models ◽  
Multilayer Perceptrons ◽  
Inference Systems

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

scholarly journals CREDIT RISK AND INCOMPLETE INFORMATION: FILTERING AND EM PARAMETER ESTIMATION

2010 ◽  
Vol 13 (05) ◽  
pp. 683-715 ◽  
Author(s):  
CLAUDIO FONTANA ◽  
WOLFGANG J. RUNGGALDIER
Keyword(s):  
Parameter Estimation ◽  
Credit Risk ◽  
Financial Market ◽  
Risk Model ◽  
Information Filtering ◽  
Credit Spreads ◽  
Reduced Form ◽  
Contagion Effect ◽  
Credit Risk Model ◽  
Filtering Approach

We consider a reduced-form credit risk model where default intensities and interest rate are functions of a not fully observable Markovian factor process, thereby introducing an information-driven default contagion effect among defaults of different issuers. We determine arbitrage-free prices of OTC products coherently with information from the financial market, in particular yields and credit spreads and this can be accomplished via a filtering approach coupled with an EM-algorithm for parameter estimation.

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