UNCTAD Virtual Institute for Trade and Development
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Sovereign Asset and Liability Management: An E-Learning Training Course
I. Balance Sheet Risks
I.i. Currency Matching
I.ii. Interest Rate Matching
I.iii. Maturity Matching
II. Methodologies
II.i. Initial Methodologies
II.ii. Advanced Methodologies

Initial Methodologies

Scenario analysis and stress testing can serve as initial steps in measuring and mitigating risk as the data required for these analyses should be available to the sovereign.  The efficacy of these methodologies will depend upon the accuracy of the data and the rigor and extent to which possible scenarios are tested, including scenarios that have not occurred historically and have an extremely remote chance of occurring in the future.

“Scenario analysis examines the possible outcomes of future events.  For example, in examining its debt structure Canada generates 10,000 random interest rate scenarios using a standard term structure model calibrated to be representative of the interest rate environment that prevailed over the last ten (10) year period.  This assumes that the interest rate environment that prevailed over this period will continue going forward, and the scenarios represent the full range of plausible interest rates.  The choice of the model and the historical period is critical, since the relevance of the analysis depends on the plausibility of the scenarios” (OECD, 2005).  As the financial crisis of 2008 demonstrated, history is not necessarily a predictor of future events and worst case scenarios must be incorporated into the calculation of risk. 

Stress testing can be understood of as an extension of scenario analysis and examines how assets and/or liabilities will perform under extreme conditions or a crisis.  One variable or a combination thereof may be used to test how an asset or liability will respond individually or in conjunction with other assets and liabilities in hypothetical situations.  The financial crisis of 2008 demonstrated the need to vigorously measure the risk of banks and financial institutions if numerous adverse events occur simultaneously.  Worst case scenarios are often overlooked but must be included in an effective risk management framework.

“Authorities might assess the extent to which the national liquidity position is vulnerable to shocks.  Greenspan (1999) suggests calculating ‘liquidity-at-risk’ based on ‘a range of possible outcomes for relevant financial variables (exchange rates, commodity prices, credit spreads, etc.).’  Countries could then assess whether they held sufficient liquid assets to avoid new borrowing for a year with an x0/0 probability.  The calculations could (and indeed should) allow for use of credit lines or exercise of options etc.” (Hawkins and Turner, 2000).


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