Quantitative Credit Portfolio Management: Practical by Arik Ben Dor, Lev Dynkin, Jay Hyman, Bruce D. Phelps

By Arik Ben Dor, Lev Dynkin, Jay Hyman, Bruce D. Phelps

An leading edge method of post-crash credits portfolio management

Credit portfolio managers ordinarily depend on basic learn for judgements on provider choice and area rotation. Quantitative researchers are inclined to use extra mathematical strategies for pricing versions and to quantify credits threat and relative worth. the data discovered right here bridges those methods. In an intuitive and readable variety, this booklet illustrates how quantitative recommendations can assist tackle particular questions dealing with state-of-the-art credits managers and danger analysts.

A specified quantity within the zone of credits, this trustworthy source comprises essentially the most contemporary and unique study during this box, which addresses between different issues very important questions raised through the credits challenge of 2008-2009. Divided into accomplished components, Quantitative credits Portfolio Management deals crucial insights into figuring out the dangers of company bonds—spread, liquidity, and Treasury yield curve risk—as good as handling company bond portfolios.

  • Presents finished assurance of every little thing from period time unfold and liquidity fee rankings to taking pictures the credits unfold premium
  • Written by way of the #1 ranked quantitative learn team for 4 consecutive years via Institutional Investor
  • Provides sensible solutions to tough query, together with: What diversification directions if you undertake to guard portfolios from issuer-specific chance? Are you well-advised to promote securities downgraded less than funding grade?

Credit portfolio administration maintains to conform, yet with this e-book as your advisor, you could achieve an outstanding realizing of ways to control advanced portfolios lower than dynamic occasions.

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Quantitative Credit Portfolio Management: Practical Innovations for Measuring and Controlling Liquidity, Spread, and Issuer Concentration Risk

An leading edge method of post-crash credits portfolio managementCredit portfolio managers commonly depend upon basic learn for judgements on supplier choice and area rotation. Quantitative researchers are likely to use extra mathematical suggestions for pricing types and to quantify credits hazard and relative price.

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Additional resources for Quantitative Credit Portfolio Management: Practical Innovations for Measuring and Controlling Liquidity, Spread, and Issuer Concentration Risk

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2% Note: Based on data for large issuers in the Communication sector of the Corporate Index, as of January 2001. The R2 values reported in the last column are based on 1,480 individual regressions (185 months × 8 sectors). Source: Barclays Capital. 10) yields identical slope coefficients and R2 to those generated by the “combined model,” but now the intercept α¯ represents the average spread change in the sample. This model expresses the month’s events as a parallel tightening of 45 bps coupled by an additional relative shift, with a slope of –28%, that captures how much more spreads move for issuers with aboveaverage spreads, and how much less they move for issuers with belowaverage spreads.

To reflect the view that higher spread credits represent greater exposures to systematic risks, we introduce a new risk sensitivity measure that utilizes spreads as a fundamental part of the credit portfolio management process. We represent sector exposures by contributions to duration times spread (DTS), computed as the product of market weight, spread duration, and spread. For example, an overweight of 5% to a market cell implemented by purchasing bonds with a spread of 80 basis points (bps) and spread duration of three years would be equivalent to an overweight of 3% using bonds with an average spread of 50 bps and spread duration of eight years.

These statistics are presented for different compositions of the SUBORD and SENIOR portfolios. 01 Note: Portfolios are constructed separately for each issuer; their composition is updated monthly based on the definition of senior and subordinated claims. The DTS of the SENIOR portfolio is scaled monthly to match that of the SUBORD portfolio and its excess return is adjusted accordingly. Source: Barclays Capital. For example, the second row reports the case in which the SUBORD and SENIOR portfolios include notes and senior notes, respectively.

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