Developing Analytical Models for Public Debt Management: Notes from - - PowerPoint PPT Presentation

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Emre BALIBEK, PhD. Deputy Director General of Public Finance Turkish Treasury Sovereign Debt Management Forum October 29-31, 2012

Developing Analytical Models for Public Debt Management: Notes from Turkish Experience

Outline

• Why an in-house Model?
• Deterministic vs Stochastic Models

 Deterministic Model: Stress Testing and

Scenario Analyses

 Stochastic Model: Turkish Debt Simulation

Model (TDSM)

• Use of Models in Decision Making
• Resources and Challenges in Model

Development

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Why an in-house Model?

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Why an in-house Model?

An off-the-shelf model (commercial software)

• Theoretically provides

 Professionalism  Efficiency in model implementation

• Requires low internal support
• May prove to be

 Less flexible  Restrictive in maintenance and development  More expensive (in general)

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Why an in-house Model?

An in-house model

• Requires institutional capacity

 Programming skills  Software platform  IT support

• Provides

 Customized solutions (particularly important

for a developing country)

 Independence in terms of development and

maintenance

 Lower cost (in general)

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Why an In-house Model?

• A choice between off-the-shelf and in-house

models depends on

 User specific needs  Institutional capacity  Cost concerns

• Turkish Treasury chose to develop an in-house

model because of its need for flexibility and sophisticated institutional capacity

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Deterministic vs Stochastic Models

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Deterministic vs Stochastic Models

• Deterministic Models

 Simple  Limited number of scenarios  May not able to sufficently capture the dynamics of

the ‘system’

 Easy to build, interpret, and communicate

• Stochastic Models

 Complex  Better replication of the ‘system’  Hundreds/thousands scenarios  Harder to build, interpret, and communicate

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Deterministic vs Stochastic Models

• Not necessarily substitutes for each other
• Approaches similar in essence
• Choice should be based on

 Available resources  Degree of detail needed  Other country specific circumstances

• It is also possible to employ deterministic and

stochastic models as complementary tools

 Turkish Case

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Deterministic Model: Spreadsheet Model

• Simple MS Excel-based model
• Used to perform

 Scenario analyses and stress tests:

• Financing requirement and debt stock projections

under the baseline scenario

• Effects of changes in macro-fiscal variables
• Scenarios are built by means of expert judgement,

market analysis etc.

• Accounting approach is also used for debt

accumulation

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Stochastic Model: Turkish Debt Simulation Model (TDSM)

Objective

• Assess the sensitivity of public debt to market

movements

• Help quantify the costs and risks associated with

alternative financing strategies:

 Provide assistance in developing the strategic

guidelines

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Macroeconomic Scenario Generation Debt Database Alternative Strategies

Expected cost & risk

• f alternative

strategy

Cash Flow Modelling and Borrowing Requirement

Stochastic Model: Turkish Debt Simulation Model

Cost-at-Risk Simulation Framework

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• Decision Horizon: 5 years
• Granularity: quarterly
• Instruments: A representative selection
• Choice of Cost and Risk Metrics
• Choice of Debt Structure Metrics (Key Portfolio

Indicators) Model Building: The Conceptual Model

Stochastic Model: Turkish Debt Simulation Model

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TDSM v.1 (2003)

Stochastic Approach: Turkish Debt Simulation Model

• Cost Metric: Accrued interest on debt plus changes

in debt amortization due to FX rate movements

• Risk Metric: Cost-at-risk (C@R) at chosen

confidence level

• Platform: MS Visual Basic and Commercial

Statistical Softwares

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TDSM v.2 (2007)

Stochastic Approach: Turkish Debt Simulation Model

• Cost Metrics:

 Cash-based interest expenditures  Level of debt stock

• Risk Metrics: Conditional cost-at-risk (C@R)
• Platform: Matlab

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TDSM v.2.1 (2010)

Stochastic Approach: Turkish Debt Simulation Model

• Modifications based on changing market

conditions and instrument set

• Cost Metrics:

 Cash-based interest expenditures  Level of debt stock  Level of inflation adjusted debt stock

• Risk Metrics: Conditional cost-at-risk (C@R)
• Platform: Matlab

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Use of Model in Decision Making

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• TDSM results are used to determine strategic

benchmarks for debt and risk management

• Strategic bechmarks aimed at

 Composition of financing (before 2007)  Composition of the debt stock (after 2007)

• Allows for a holistic appoach to cover
• Outright sales
• Buy-backs and debt exchanges
• Derivative instruments

Use of Models in Decision Making

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Distribution of Interest Payments

42 44 46 48 50 52 54 50 100 150 200 250 300 Milyar YTL Frekans

CC@R

Billion TRL Frequency

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20 30 40 50 60 70 80 90 Borç Stoku/GSMH (%)

Years Distribution of Debt Stock Projections

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Stock/GDP

Illustrative Results

Use of Models in Decision Making

Interest Payments for Alternative Strategies Accrued Inflation Adjusted Stock (AIAS) for Alternative Strategies

Str-2

AIAS @ Risk AIAS

Str-4 Str-1 Str-5 Str-6 Str-3

Illustrative Results

Str-2

Interest Payments @ Risk Interest Payments

Str-4 Str-1 Str-5 Str-6 Str-3 20

Use of Models in Decision Making

• Reduce liquidity / refinancing risk:

 Maintain a certain level reserve of cash  Increase average maturity to the extent that market conditions allow  Decrease the share of debt maturing within 12 months

• Reduce currency risk:

 Borrow mainly in TL

• Reduce interest rate risk:

 Use fixed rate instruments as the major source of domestic borrowing  Decrease the share of debt which has interest rate re-fixing period

less than 12 months

Strategic Benchmarks

Use of Models in Decision Making

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Resources and Challenges in Model Development

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Resources and Challenges in Model Development

Resources Needed

• Institutional Capacity

 Committed and Skilled Staff  IT systems

• Financial Resources

 Training  Consulting  Software

• Management Support (probably the most important one)

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• In-house modeling

 Choice of modelling platform

 Ease of implemention vs.efficiency and speed (Excel vs. some

technical computing platform)

 Capacity building  Maintenance

• Input Modeling

 Lack of long data series  Stationarity problems in data

 Regime changes  Financial crises

 Distributional assumptions

 Do scenario generation options enough to cope with extreme

cases (event risk)?

Resources and Challenges in Model Development

Challenges

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For more information:

Turkish Treasury Simulation Model for Debt Strategy Analysis, World Bank Policy Research Working Paper 6091