Credit portfolio risk and asset price cycles

2007 ◽  
Vol 5 (4) ◽  
pp. 337-354
Author(s):  
Klaus Rheinberger ◽  
Martin Summer
2014 ◽  
Vol 10 (4) ◽  
pp. 45-70 ◽  
Author(s):  
Pak-Wing Fok ◽  
Xiuling Yan ◽  
Guangming Yao

Author(s):  
Rudi Schäfer ◽  
Alexander F. R. Koivusalo ◽  
Thomas Guhr

Policy Papers ◽  
2017 ◽  
Vol 2017 (64) ◽  
Author(s):  

Effective liquidity management is important to promote macro-financial stability in the GCC countries. Fixed exchange rate regimes provide credible nominal anchors in the GCC countries, but combined with open capital accounts, they also entail limited monetary policy independence. At the same time, high dependence on hydrocarbon revenue has made the region vulnerable to oil price-driven liquidity swings. And the latter can affect monetary policy implementation, including by exacerbating credit and asset price cycles. This highlights the importance of frameworks aimed at forecasting liquidity and ensuring appropriate liquidity levels through the timely absorption or injection of liquidity by central banks. Over the past decade, liquidity management in the GCC countries has been based mainly on passive instruments. Abundant liquidity during times of high oil prices have placed liquidity absorption at the center of the central bank operations. Reserve requirements have helped absorb liquidity but have not been used very actively. Standing facilities, another key instrument, are more passive in nature, with the amount of liquidity absorbed or injected driven by banks rather than monetary authorities. Central banks bills or other instruments have also been used, but issuance has not systematically been based on market principles. In addition, these operations have been constrained by limited liquidity forecasting capability and the shallow nature of interbank and domestic debt markets.


2017 ◽  
Vol 16 (04) ◽  
pp. 1101-1124 ◽  
Author(s):  
Rongda Chen ◽  
Ze Wang ◽  
Lean Yu

This paper proposes an efficient simulation method for calculating credit portfolio risk when risk factors have a heavy-tailed distributions. In modeling heavy tails, its features of return on underlying asset are captured by multivariate [Formula: see text]-Copula. Moreover, we develop a three-step importance sampling (IS) procedure in the [Formula: see text]-copula credit portfolio risk measure model for further variance reduction. Simultaneously, we apply the Levenberg–Marquardt algorithm associated with nonlinear optimization technique to solve the problem that estimates the mean-shift vector of the systematic risk factors after the probability measure change. Numerical results show that those methods developed in the [Formula: see text]-copula model can produce large variance reduction relative to the plain Monte Carlo method, to estimate more accurately tail probability of credit portfolio loss distribution.


2016 ◽  
Vol 45 (1) ◽  
pp. 25-44 ◽  
Author(s):  
Kevin Jakob ◽  
Matthias Fischer

In this article we introduce the novel GCPM package, which represents a generalized credit portfolio model framework. The package includes two of the most popular mod- eling approaches in the banking industry namely the CreditRisk+ and the CreditMetrics model and allows to perform several sensitivity analysis with respect to distributional or functional assumptions. Therefore, besides the pure quanti?cation of credit portfolio risk, the package can be used to explore certain aspects of model risk individually for every arbitrary credit portfolio. In order to guarantee maximum ?exibility, most of the models utilize a Monte Carlo simulation, which is implemented in C++, to achieve the loss dis- tribution. Furthermore, the package also o?ers the possibilities to apply simple pooling techniques to speed up calculations for large portfolios as well as a general importance sample approach. The article concludes with a comprehensive example demonstrating the ?exibility of the package.


2018 ◽  
Vol 12 (2) ◽  
pp. 58-65
Author(s):  
T. S. Gaibov

In the article we analyzed international and Russian methodological approaches for classification of risk in project finance and identified crucial criteria which provide further framework for development of principles and management mechanism of specialized credit portfolio at commercial bank. Considering relevant literature and taking into account the main purpose of the study, the practical use of aggregation of project finance risk taxonomy was concluded and three groups of risk were proposed: product risk, counterparty risk and portfolio risk. For each group, it was highlighted list of significant risks all of which shall be subsequently integrated into overall assessment of risks’ concentration of specialized credit portfolio together with some management tools.


Sign in / Sign up

Export Citation Format

Share Document