Credit Portfolio Loss Forecasts for Economic Downturns

2008 ◽  
Author(s):  
Daniel Roesch ◽  
Harald Scheule
Keyword(s):  
Economic Downturns ◽  
Credit Portfolio ◽  
Portfolio Loss

scholarly journals Asset correlation, portfolio diversification and regulatory capital in the Basel Capital Accord

2011 ◽  
Vol 1 (3) ◽  
pp. 31-39 ◽  
Author(s):  
Sylvia Gottschalk
Keyword(s):  
Basel Ii ◽  
Capital Adequacy ◽  
Probability Of Default ◽  
Regulatory Capital ◽  
Kmv Model ◽  
High Quantiles ◽  
Portfolio Loss Distribution ◽  
Credit Portfolio ◽  
Portfolio Loss ◽  
Asset Correlation

In this paper, we analyze the properties of the KMV model of credit portfolio loss. This theoretical model constitutes the cornerstone of Basel II’s Internal Ratings Based(IRB) approach to regulatory capital. Our results show that this model tends to overestimate the probability of portfolio loss when the probability of default of a single firm and the firms’ asset correlations are low. On the contrary, probabilities of portfolio loss are underestimated when the probability of default of a single firm and asset correlations are high. Moreover, the relationship between asset correlation and probability of loan portfolio loss is only consistent at very high quantiles of the portfolio loss distribution. These are precisely those adopted by the Basel II Capital Accord for the calculations of capital adequacy provisions. So, although the counterintuitive properties of the KMV model do not extend to Basel II, they do restrict its generality as a model of credit portfolio loss.

T-Vasicek Credit Portfolio Loss Distribution

2018 ◽  
Keyword(s):  
Default Probability ◽  
Additional Parameter ◽  
Fat Tails ◽  
Probability Estimate ◽  
Loss Distribution ◽  
Uncertainty Factor ◽  
Portfolio Loss Distribution ◽  
Credit Portfolio ◽  
Portfolio Loss ◽  
T Distribution

We derive and discuss a new analytical credit loss distribution. This new model, T-Vasicek, is a variant of the well-known and highly useful Vasicek distribution. We inject a t-distribution extension into Vasicek that preserves the analytical convenience of Vasicek while providing a richer credit loss framework with fat tails and an additional user-specified parameter. This additional parameter is directly tied to the t-distribution and represents the uncertainty in the default probability estimate. The classical Vasicek limit, then, is the case in which the analyst knows the pool default probability with certainty. We show how one may impose desired correlation among all debt instruments in the t-distribution framework. We derive closed-form, numerical, and analytical forms for T-Vasicek and check the numerical results with Monte Carlo simulation. We also determine suitable maximum likelihood estimators for the T-Vasicek parameters of default probability (PD), correlation (ρ), and PD-uncertainty factor (ν).

A NEW FRAMEWORK FOR DYNAMIC CREDIT PORTFOLIO LOSS MODELLING

2008 ◽  
Vol 11 (02) ◽  
pp. 163-197 ◽  
Author(s):  
JAKOB SIDENIUS ◽  
VLADIMIR PITERBARG ◽  
LEIF ANDERSEN
Keyword(s):  
Sufficient Conditions ◽  
Background Process ◽  
Loss Process ◽  
Advantages And Disadvantages ◽  
Novel Approach ◽  
Loss Distributions ◽  
Loss Modelling ◽  
Credit Portfolio ◽  
Portfolio Loss ◽  
New Framework

We present the SPA framework, a novel approach to the modeling of the dynamics of portfolio default losses. In this framework, models are specified by a two-layer process. The first layer models the dynamics of portfolio loss distributions in the absence of information about default times. This background process can be explicitly calibrated to the full grid of marginal loss distributions as implied by initial CDO tranche values indexed on maturity, as well as to the prices of suitable options. We give sufficient conditions for consistent dynamics. The second layer models the loss process itself as a Markov process conditioned on the path taken by the background process. The choice of loss process is non-unique. We present a number of choices, and discuss their advantages and disadvantages. Several concrete model examples are given, and valuation in the new framework is described in detail. Among the specific securities for which algorithms are presented are CDO tranche options and leveraged super-senior tranches.

PARTICLE METHODS FOR THE ESTIMATION OF CREDIT PORTFOLIO LOSS DISTRIBUTIONS

2010 ◽  
Vol 13 (04) ◽  
pp. 577-602 ◽  
Author(s):  
RENÉ CARMONA ◽  
STÉPHANE CRÉPEY
Keyword(s):  
Variance Reduction ◽  
Interacting Particle Systems ◽  
Particle Methods ◽  
Comparative Case Study ◽  
Quantitative Finance ◽  
Interacting Particle ◽  
Simulation Based ◽  
Loss Distributions ◽  
Credit Portfolio ◽  
Portfolio Loss

The goal of the paper is the numerical analysis of the performance of Monte Carlo simulation based methods for the computation of credit-portfolio loss-distributions in the context of Markovian intensity models of credit risk. We concentrate on two of the most frequently touted methods of variance reduction in the case of stochastic processes: importance sampling (IS) and interacting particle systems (IPS) based algorithms. Because the subtle differences between these methods are often misunderstood, as IPS is often regarded as a mere particular case of IP, we describe in detail the two kinds of algorithms, and we highlight their fundamental differences. We then proceed to a detailed comparative case study based on benchmark numerical experiments chosen for their popularity in the quantitative finance circles.

Aplication of Quantitative and Qualitative Methods in Risk Management of Credit Portfolio in the Conditions of the Banking System of the Republic of Tajikistan

2013 ◽  
Vol 8 (3) ◽  
pp. 249-268
Author(s):  
Basgul Fajzullohonovna Isupova
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Qualitative Methods ◽  
Banking System ◽  
Potential Losses ◽  
The Republic ◽  
Credit Portfolio ◽  
Quantitative And Qualitative Methods ◽  
Coefficient Method

In this article, an analysis of the fundamental methods of risk assessment and risk management of credit portfolio is conducted. In particular, complex and qualitative methods of risk management of credit portfolio studied in details, namely analytical, statistical and coefficient methods. Based on the coefficient method the author proposes a number of standards for the assessment of potential losses in credit activity. 

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