scholarly journals Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data Via Default Probability Term Structures

2017 ◽  
Author(s):  
Brian Barnard
Keyword(s):  
Default Probability ◽  
Market Data ◽  
Bond Valuation ◽  
Term Structures ◽  
Probability Term

scholarly journals Rating Migration and Bond Valuation: Towards Ahistorical Rating Migration Matrices and Default Probability Term Structures

2019 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Brian Barnard
Keyword(s):  
Term Structure ◽  
Cash Flows ◽  
Default Probability ◽  
Reduced Form ◽  
Probability Of Default ◽  
Forward Rates ◽  
Bond Valuation ◽  
Term Structures ◽  
Probability Term ◽  
The Impact

The study examines rating migration, and default probability term structures obtained from rating migration matrices. It expands on the use of rating migration matrices with reduced form bond valuation models, by formally delineating the probability of default according to the likely rating paths of a bond, as implied by the rating migration matrix. Further, two alternatives are also considered. First, the cost of default is stipulated as the recovery of par according to the exit rating upon default. Also, in addition to stating the value of a bond in terms of expected cash flows, when considering the probability of default, the value of a bond is alternatively stated as the present value of all likely rating paths of the bond, discounted against the market risk-bearing bond forward rates of the different rating categories. The impact of term structure volatility and rating migration uncertainty on bond valuation is also considered.It is shown that the relationship between rating migration and default probability is complex, and the default probabilities of different rating categories are time-dependent and not isolated from each other. Also, rating migration resembles a delayed default process that influences default probabilities of subsequent intervals. The implications of a rating migration matrix may perhaps only be fully understood through simulation. This form one of the first points by which to evaluate rating migration matrices. The results of the valuation model show that historical rating migration matrices may not be optimal for pricing bonds ahistorically. A principal premise of the study is the dichotomy between historical values and ahistorical estimates, particularly with regards to rating migration. It is argued that historical estimates face two key shortcomings: they must be able to accurately forecast future rating migration and rating category intensities as a result, and they must specify a method to include rating migration uncertainty. An optimization model is delineated to extract ahistorical rating migration matrices from market prices. This too has implications that should be considered. In light of the above, reduced form models may have an advantage over structural models, in their ability to portray a far more sophisticated default process.

Interest rates calibration with a CIR model

2019 ◽  
Vol 20 (4) ◽  
pp. 370-387 ◽  
Author(s):  
Giuseppe Orlando ◽  
Rosa Maria Mininni ◽  
Michele Bufalo
Keyword(s):  
Interest Rates ◽  
Financial Market ◽  
Short Term ◽  
New Approach ◽  
Cir Model ◽  
Market Data ◽  
Content Type ◽  
Term Structures ◽  
Negative Interest Rates ◽  
Interest Rate Dynamics

Purpose The purpose of this paper is to model interest rates from observed financial market data through a new approach to the Cox–Ingersoll–Ross (CIR) model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek framework. However, there are a number of issues in describing interest rate dynamics within the CIR framework on which focus should be placed. Therefore, a new methodology has been proposed that allows forecasting future expected interest rates from observed financial market data by preserving the structure of the original CIR model, even with negative interest rates. The performance of the new approach, tested on monthly-recorded interest rates data, provides a good fit to current data for different term structures. Design/methodology/approach To ensure a fitting close to current interest rates, the innovative step in the proposed procedure consists in partitioning the entire available market data sample, usually showing a mixture of probability distributions of the same type, in a suitable number of sub-sample having a normal/gamma distribution. An appropriate translation of market interest rates to positive values has been introduced to overcome the issue of negative/near-to-zero values. Then, the CIR model parameters have been calibrated to the shifted market interest rates and simulated the expected values of interest rates by a Monte Carlo discretization scheme. We have analysed the empirical performance of the proposed methodology for two different monthly-recorded EUR data samples in a money market and a long-term data set, respectively. Findings Better results are shown in terms of the root mean square error when a segmentation of the data sample in normally distributed sub-samples is considered. After assessing the accuracy of the proposed procedure, the implemented algorithm was applied to forecast next-month expected interest rates over a historical period of 12 months (fixed window). Through an error analysis, it was observed that our algorithm provides a better fitting of the predicted expected interest rates to market data than the exponentially weighted moving average model. A further confirmation of the efficiency of the proposed algorithm and of the quality of the calibration of the CIR parameters to the observed market interest rates is given by applying the proposed forecasting technique. Originality/value This paper has the objective of modelling interest rates from observed financial market data through a new approach to the CIR model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek model (Section 2). However, there are a number of issues in describing short-term interest rate dynamics within the CIR framework on which focus should be placed. A new methodology has been proposed that allows us to forecast future expected short-term interest rates from observed financial market data by preserving the structure of the original CIR model. The performance of the new approach, tested on monthly data, provides a good fit for different term structures. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, clustered volatility and skewed tails), as well as the new ones added by the current market environment (particularly the need to model a downward trend to negative interest rates).

scholarly journals Interest Rate Term Structure Decomposition at the Instrument Level

2019 ◽  
Vol 6 (3) ◽  
pp. 7
Author(s):  
Brian Barnard
Keyword(s):  
Term Structure ◽  
Goodness Of Fit ◽  
Sequential Approach ◽  
Government Bonds ◽  
Bond Valuation ◽  
Term Structures ◽  
Structure Decomposition ◽  
Interest Rate Term Structure ◽  
Free Government ◽  
Instrument Level

The paper examines term structure decomposition at the instrument level – decomposing term structures for issues as well as the portfolio. Three different implementations are stipulated: axiomatic structural approaches, a sequential approach, and a base structure approach. The three different implementations are evaluated against a portfolio of risk-free government bonds. The goodness-of-fit and smoothness properties of instrument-level term structure decomposition are also considered. The conclusion points to remaining gaps in theory regarding instrument-level term structure decomposition, and considers areas of application – typically bond valuation.

Evidence-based regulation of labour migration in OECD countries: setting quotas, selection criteria, and shortage lists

2014 ◽  
Vol 11 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Jonathan Chaloff
Keyword(s):  
Labour Market ◽  
Selection Criteria ◽  
Oecd Countries ◽  
Market Analysis ◽  
Multiple Criteria ◽  
Labour Migration ◽  
Evidence Based ◽  
Periodic Review ◽  
Market Data ◽  
Almost All

The growing complexity of selection criteria for discretionary labour migration in OECD countries has been accompanied by an expanded demand for labour market analysis and consultation with stakeholders. While some features of general or detailed criteria may be fixed in legislation, numerical quotas or targets, shortage lists, and multiple-criteria points-based systems are generally subject to periodic review and revision based on labour market data and consultation with stakeholders. Official government bodies have maintained co-ordination of this process, with varying degrees of externalization. In most countries expertise is internal, with recourse to external mandated bodies rare. In almost all cases, however, the process is designed to promote consensus around the policy while maintaining political control.

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