The International Financial Reporting Standard (“IFRS”) 9 and the Financial Accounting Standard Board’s (“FASB”) Current Expected Credit Loss (“CECL”) model significantly raise the accuracy bar for valuation and credit risk analytics for all organizations who report under their aegis.
In both cases, the visibility of the organization’s valuation and credit risk assessment moves from the back office or middle office, seen primarily by risk experts, to center stage under a bright spot light
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Our analysis May 4 reported on the bond market view of HSBC Holdings PLC (HSBC). Citigroup Inc. plays a similar role in the financial services business across the Atlantic. From a bond market perspective, how does Citigroup Inc. compare to HSBC Holdings PLC? We answer that question in this note in light of our analysis of Citigroup Inc. on October 1, 2014.
The first thing to note is that Citigroup Inc., not surprisingly, is much more heavily traded in the U.S. fixed rate corporate bond market, as shown in the trading of fixed-rate senior non-call debt on May 4, 2015.
Eleven bonds of HSBC Holdings PLC and 34 bonds of HSBC USA Inc. traded, and 56 bonds of Citigroup Inc. traded. The underlying principal amount traded on the Citigroup Inc. bonds was $157 million, compared to $107 million for six HSBC Holdings PLC-related issuers.
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Traditionally, liquidity has been defined as:
- A Russian problem;
- An Asian problem;
- Someone else’s problem;
- A broker’s problem;
- Not something to worry about since it is guaranteed by the Central Bank; or,
- All of the above.
Even the Bard has commented on liquidity with the rather pithy ‘put money in thy purse’!
On March 13, 2014, we pointed out the many reasons why the Federal Reserve-mandated stress testing process will be a less accurate measure of financial institutions’ risk than the market’s price on those institutions’ promise to pay a dollar in the future. The market place considers all scenarios, not just three as in the Fed’s CCAR stress tests. The market place invests cold hard cash to price various financial institutions’ promises to pay. In the stress testing process, those who prepare the stress tests are often in a conflict of interest position, since it normally serves them best financially if the CCAR results are prepared on the sunny side of the street.
In this note, we update our results from March 13, 2014 with the bond market assessments of financial institutions whose bonds were traded in the U.S. corporate bond market on Friday, January 23. We use 1,281 trades on the bonds of 51 different legal entities in the financial services industry with underlying principal of $1.4 billion to rank those firms by riskiness. We rank the institutions by credit spread and by spread to the U.S. Dollar Cost of Funds Index.
While often attempted history proves that one cannot repeal the business and credit cycle. The cycle always seems to be the same although the triggers and environment may be different. Losses peak, loan demand and supply dry up, the appetite for risk evaporates while households and businesses begin the process of repairing their respective balance sheets. Slowly investors start stretching for yield and lenders (banks, shadow banks and capital markets) begin to ease credit terms, soon followed by increased usage of leverage. A review of the Federal Reserve Senior Credit Officer Survey bears out this cycle.
This cyclical nature of credit and default risk can clearly be seen from the history of the Kamakura Troubled Company Index going back to its introduction in 1990.
The author wishes to thank his colleague, Managing Director for Research Prof. Robert A. Jarrow, for twenty years of guidance and helpful conversations on this critical topic.
As zero interest rate policies and negative interest rates ripple through world financial markets, many legacy interest rate risk systems and asset and liability management systems have been unable to keep pace. In this note, we use 100,000 scenarios from a modern 9 factor Heath, Jarrow and Morton interest rate simulation from Kamakura Corporation to illustrate the model validation issues that arise when one admits that negative interest rates have a probability that is not zero.
The model validation procedures we outline are used by Kamakura in both its Kamakura Risk Information Services macro factor scenario sets and in Kamakura Risk Manager (“KRM”). KRM has allowed users to simulate and analyze negative interest rates for more than 20 years.
Negative Interest Rates: An International Perspective
Even with negative interest rates making the headlines in European markets daily, one sometimes hears the phrase “It can’t happen in the United States.” The same phrase, of course, was used to deny the possibility that home prices could fall in the United States prior to the 2006-2010 financial crisis. Negative interest rates have already been observed in the secondary market for U.S. Treasury securities, as confirmed by this phrase quoted from the February 17 version of the U.S. Department of the Treasury yield reporting web page:
The Asian Institute of Chartered Bankers has just published “A Best Practice Approach to Modeling Sovereign Defaults” in the December 2014 issue of its Banking Insight magazine. Since the article, which I co-authored with my colleagues Suresh Sankaran and Dr. Clement Ooi, was targetted toward the Asia market, it is helpful to emphasize some of the most important points in modeling sovereign default risk from a world-wide perspective. There are three key points in modeling sovereign default risk that we explain in the rest of this article:
- The credit default swap market is a very problematic source of credit information and, at best, it is reliable only for a short list of reference names.
- The conflict of interest faced by legacy rating agencies is even more extreme in the sovereign case than it is in the well-documented corporate and structured products markets.
- Modern statistical modeling techniques are best practice and the only realistic alternative to the credit default swap market and legacy credit ratings.
We outline the reasons for these assertions in the rest of this note. Read more
In the past week, I have spoken with many regulators and bankers on the proper role of intuition in the econometric estimation of credit models for the Federal Reserve’s Comprehensive Capital Analysis and Review 2015. In our review ofbest practices for stress testing , value at risk, and credit value at risk on October 20, 2014, there was no role for “intuition,” just for science. The same is true for our November 13, 2014 update of model validation procedures for CCAR 2015
Why? In quotes from Kathryn Schultz, Nobel Prize Winner Daniel Kahneman, and Professors King and Soneji below, we explain that the very DNA of human beings leads us to be overconfident in our own intellectual powers. Rather than relying on modern econometric methods, most humans would rather guess an answer and would normally be supremely confident in its accuracy.
The Federal Reserve will announce the results of the “DFAST” stress tests on March 5. On March 13, 2014, we pointed out the many reasons why the Federal Reserve-mandated stress testing process will be a less accurate measure of financial institutions’ risk than the market’s price on those institutions’ promise to pay a dollar in the future. The market place considers all scenarios, not just three as in the Fed’s CCAR stress tests. The market place invests cold hard cash to price various financial institutions’ promises to pay.
In the stress testing process, those who prepare the stress tests are often in a conflict of interest position, since it normally serves them best financially if the CCAR results are prepared on the sunny side of the street. In this note, we update our results from January 27, 2015 with the bond market assessments of financial services firms whose bonds were traded in the U.S. corporate bond market on Monday, March 2. Many of the firms whose bonds are traded are not subject to the stress testing process, so a bond market analysis gives us a broader and more comprehensive risk assessment. We use 5,383 trades on the bonds of 127 different legal entities in the financial services industry with underlying principal of $1.8 billion to rank those firms by riskiness. We rank the institutions by credit spread, by spread to the U.S. Dollar Cost of Funds Index, and by “best value,” which we define as the ratio of credit spread to matched maturity default probability.
Conclusion: 25 financial institutions led by Berkshire Hathaway Finance Corporation (BRK.A) (BRK.B) have a better spread to the U.S. Dollar Cost of Funds Index TM than the best of the four “too big to fail” financial institutions in the United States, which we define as the grouping including Bank of America Corporation (BAC), Citigroup Inc. (C), JPMorgan Chase & Co. (JPM) and Wells Fargo & Co. (WFC).
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It has been clearly established that when customers default, it results in liquidity risk; when there is a fraud within the organisation, it impacts liquidity, when there is funding concentration, there is clear evidence of illiquidity if the funders do not renew credit lines, and when markets change, it changes the liquidity profile of an organization.
Liquidity is a second order risk, and one does not manage second order risks without adequately monitoring, measuring and controlling primary risks. If an organisation controls credit risk, it is, in part, controlling liquidity risk.
NEW YORK, January 5, 2015: Kamakura Corporation reported Monday that the Kamakura troubled company index ended the month of December at 6.42%, an increase of 1.31% from the end of November. The index reflects the percentage of the Kamakura 34,000 public firm universe that has a default probability over 1.00%. An increase in the index reflects declining credit quality while a decrease reflects improving credit quality.
As of the end of December, the percentage of the global corporate universe with default probabilities between 1% and 5% was 5.03%, up 0.92% from November; the percentage of the universe with default probabilities between 5% and 10% was 0.95%, up 0.27%; the percentage between 10% and 20% was 0.31%, up 0.6%; while the percentage of companies with default probabilities over 20% was 0.13%, up 0.06 from the previous month.
As the Federal Reserve’s 2015 Comprehensive Capital Analysis and Review stress testing exercise moves to its conclusion, a steady stream of well-intended but incorrect models are coming into public view. In particular, many analysts have been using lagged default probabilities as inputs to their 13 quarter stress tests, a modeling strategy that ProfessorsJoshua Angrist and Jorn-Steffen Pischke label “forbidden models” in their classic econometrics text “ Mostly Harmless Econometrics: An Empiricist’s Companion” (2009).
We explain why such models, however well intended, are usually invalid and unacceptable from a model validation point of view using quotes from Angrist and Pischke.
Read more at http://www.kamakuraco.com/Blog/tabid/231/EntryId/729/Stress-Testing-The-Use-and-Abuse-of-Lagged-Default-Probabilities-in-Forbidden-Credit-Models.aspx