Chase Manhattan and their involvement with Drysdale Securities

Drysdale Government Securities traded in government securities in the secondary market. It had a capitalization of $20 million. They dealt with a relatively lesser known type of government securities called repos or repurchase and reverse repurchase agreements.

Repurchase Agreements (Repos): Under repo transactions, the central bank (Fed in US) purchases securities with a promise that the seller will repurchase them on a specific date at a specific price. The time period is usually two weeks. A repo temporarily increases the money base.

Reverse Repos: This is the reverse of a repo, where the central bank (Fed in US) sells securities with a promise that the buyer will sell them back on a specific date at a specific price. A reverse repo temporarily decreases the money base.

Drysdale in their deal with Chase Manhattan as an agent asked it to buy securities. It asked Chase to garner as many government securities as it could. When the time came for Drysdale to pay up the interest that was due, it defaulted. The positions they had taken had incurred losses and Drysdale had no means to pay Chase. The practice typically being that no actual securities were exchanged. A computer entry indicated that the ownership had changed by Chase.

The amount that Drysdale lost was $300 million. Banks treated borrowed securities as collateral. The accrued coupon interest was not considered. Drysdale borrowed large number of securities with high coupons and a short time until the next coupon could be cashed in.

Prior to the failure of Drysdale Government Securities in May 1982, it was common practice in the RP market to ignore the value of accrued interest in pricing RPs using coupon bearing securities. This practice enabled Drysdale to acquire a substantial amount of "undervalued" securities, despite its limited capital base. Drysdale used the securities it had reversed in to make short sales to a third party for an amount that included the accrued interest. Using the surplus cash generated, Drysdale was able to raise working capital and to make interest payments to its other repo counterparties. The strategy worked adequately until May 17, 1982, when cumulative losses on Drysdale's interest rate bets caused it to be unable to pay the interest on securities it had borrowed.

That the sheer size of the borrowing went un-noticed by Chase was surprising. It turned out that the bankers handling the Drysdale accounts were fairly inexperienced and thought that they were acting merely as agents with no liability. This they learnt was sadly not true about $270 million later.

This led to a far closer scrutiny of securities that were being treated as collateral. The risk rating of each and other features of the product started getting scrutinised. It was important banks learnt to contain the size of the positions of the Securities firms they were dealing with. The need for better risk control measures through internal checks, clarity of their role in the transaction was brought into focus.

This highlighted the risk to repo borrowers of not including accrued interest in the initial price of the repo security. Later that year, in response to the weaknesses exposed by the Drysdale affair, full accrual pricing, in which accrued interest is included in full in the initial purchase and resale prices, was adopted as standard market practice, largely at the impetus of the Federal Reserve Bank of New York.

Though the loss hardly made any real dent in Chase’s business it did raise questions as to how Drysdale managed to use a loophole in the valuation system to hoodwink it. If Chase Manhattan bank had only undertaken all the due diligence procedures it might have been saved itself from this loss.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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