Risk Management in the Emerging Context Sunando Roy

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Risk
Management in the Emerging ContextSunando Roy
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Overview

• What is risk?
• What can we expect from a Risk Manager?
• The Key Ingredients of a Risk Manager.
• Knowledge of Macroeconomy
• Understanding of Financial Markets, Institutions
  and Instruments
• KRIs
• Regulatory Environment
• Techniques

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Risk

• Robert Frost Two roads diverged in a wood, and
  I... I took the one less traveled by, and that
  has made all the difference.
• Vaclav Havel Vision is not enough, it must be
  combined with venture. It is not enough to stare
  up the steps, we must step up the stairs.

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Managing Risk

• "Risk comes from not knowing what you're
  doing."  --  Warren Buffett
• "When you are in any contest, you should work as
  if there were--to the very last minute--a chance
  to lose it. This is battle, this is politics,
  this is anything."  --  Dwight D. Eisenhower
• The process of identifying, assessing, and
  controlling, risks arising from operational
  factors and making decisions that balance risk
  cost with mission benefits

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Role of A Risk Manager

• Identification
• Measurement
• Reporting
• Strategy
• Revisiting Risks are not time invariant

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What a Risk Manager Should Know

• Macro Economic Situation
• Financial Markets, Institutions and Instruments
• Key Risk indicators
• Regulatory Environment
• Technique

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The Risk Management Story so far

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International Initiatives in Managing Risks

• Till the 1980s, a professional risk manager was
  unheard of
• Late 1980s, US Financial Firms started using VaR
• Basel I 1988
• 415 spreadsheet of JP Morgan
• Riskmetrics,1995
• BIS - a series of risk management guidelines for
  Banks worldwide
• Market Risk Guidelines of Basel, 1996
• Basel II process ( November 2005 Document)
• .

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India Changing Financial landscape

• Easing of Financial Repression
• Move towards market determined system since
  1992.
• New Financial instruments (Floating Rate
  Bonds,Bonds with Call and Put, STRIPS)
• PD System ,1996
• With the development of G-Sec market, financial
  institution participation increased in bond
  markets, exposing them to risks
• Liquidity Adjustment Facility, 2001
• Risk management becoming relevant in the present
  context

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• Types of risk
• Market risk
• Credit risk
• Operational risk
• Liquidity Risk
• Settlement Risks
• Other Risks ( Legal Risk, Reputational Risk,
  Political Risk, Catastrophic Risk))

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I. Macro Picture

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Risks in Indian Economy.Macro Risks.

• Macroeconomic risks in India
• Positive Factors
• Robust growth Performance
• Strong Balance of Payments
• Inflation under control
• Public Sector Performance shows marginal
  improvement.
• Negative Factors
• Oil Prices hardening a matter of concern
• Asset Prices
• Agricultural Performance Subdued, concerns on
  wheat stocks
• Poverty and unemployment, inclusive policies

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II. Financial Markets, Institutions, Instruments

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Financial Sector Risks

• Emanates from the activities in the financial
  sector, such as trading, lending, other
  operations, policies
• Markets and Linkages
• Institutions
• Instruments

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III. KRIs

• To understand the financial sector risks, let us
  look at the banking sector performance and its
  key risk indicators,

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• Capital Adequacy

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CRAR declined. Tier I capital rose, Tier II
capital fell, RWAs showed substantial increase
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CRAR- Bank Group s
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Asset Quality
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NPA Ratios continued their declining trend
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Exposure to Capital Markets increased..
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Exposure to Commercial Real Estate increased..
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OBS Exposures.
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• Profitability

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Marginal Decline in ROA
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Cost Income Ratio is stable
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IV. Regulatory Environment

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Risk Management Guidelines in India

• ALM Guidelines, February,1999
• Operating Guidelines on Risk Management , October
  7, 1999 covering broad contours for management of
  credit, liquidity, interest rate, foreign
  exchange and operational risks.
• December 2000 Capital Adequacy Guidelines for
  Primary Dealers covering Credit and Market Risk
• On September 20, 2001, two Working Groups were
  constituted in Reserve Bank of India drawing
  experts from select banks and FIs for preparing
  detailed Guidance Notes on Credit Risk and Market
  Risk management by banks.
• identified further steps to be taken by banks for
  Improving their existing risk management
  framework, suiting to Indian conditions

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Risk Regulation in India

• 2005 Detailed capital adequacy guidelines for
  Banks to move towards Basel II, 2007- final
  guidelines
• 2006 April 17, the ALM framework of 1999
  updated.
• 2007- Pillar II guidelines expected

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V. Technique

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Risk Measurement A primer

• Several methods of measuring Market risk
• Most popular Method VaR ( Value at Risk) A
  dynamic Method
• Alternatively, one may also adopt duration based
  approaches static in nature
• Well look at value at risk models
• To give a flavour of what risk managers do

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VaR

• VaR is defined as the maximum possible loss for a
  given position or portfolio within a known
  confidence interval over a specific time horizon,
  in a normal everyday market

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VaR Inputs

• VAR Estimation Period - The time over which
  P/L is estimated.
• Confidence Level - The frequency which actual
  losses VAR Inputs
• Position Size - The size of the instruments
  contained in the
• portfolio.
• Risk Factors
• Volatility - The magnitude of the underlying risk
  factor changes.
• Correlation - Degree to which changes in
  different risk factors move together.

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Volatility Measures

• Volatility information is a measure of how much
  prices and interest rates can be expected to
  change over time.
• Standard Deviation
• Simple Moving Average
• Exponential weighted moving average
• GARCH

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VaR methodologies

• Parametric or Variance Covariance
• Historical Simulation
• Monte Carlo Simulation
• Other Models

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Historical Simulation

• The Historic Simulation Full Revaluation approach
  calculates PL by revaluing the portfolio given
  historic movements in prices/yields.
• Have the price levels on each of the days for
  the past one year for all the risk factors.
• Calculate the daily percentage changes these
  many scenarios
• Calculate the portfolio value as of today
• Apply each of the percentage changes to get the
  expected value of the portfolio as of tomorrow.
• Sort the absolute value changes and determine
  the cutoff value at the lowest 1 percentile.

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Historical Simulation
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Monte Carlo Simulation
Monte Carlo Simulation generates numerous random
market scenarios using predetermined parameters
for price volatility and correlation and
calculates the PL for each scenario.
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Limitations

• Statistical orientation and assumptions
• Quality of data is of primary importance
• Doesnt capture event risk
• Thats why the need to supplement with Stress
  Tests

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Stress Tests
Stress tests are designed to estimate potential
economic losses in abnormal markets.
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Stress Tests
Stress tests can be framed around two central
questions 1. How much could I lose if a stress
scenario occurs, for example the Equity market
crashes? 2. What event could cause me to lose
more than a pre-defined threshold amount, for
example Rs. 10 crore? Good stress tests
should be relevant to current positions,
consider changes in all relevant market rates,
examine potential regime shifts, consider
market illiquidity.
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VI. Risk Management in Emerging Markets

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The First ConcernStructural Breaks in
Data

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Chow Breakpoint Test March 2001

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The Second Concern Fat Tails
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Tail Behaviour in Indian Debt Market
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Skewness Kurtosis
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Tests of Tail Behaviour
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Variance-Covariance Model Standard Form
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GARCH (1,1)
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Volatility Clustering and Higher Order GARCH
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Comparison of Alternative Models through
Backtesting

• Kupiecs POF-Test (Proportion of Failures)
• The Kupiec- test is also known as the
  Likelihood-Ratio-Test. The null hypothesis for
  these tests is, that the empirically determined
  probability matches the given probability.to
  check for this assumption. The corresponding
  LR-statistic is defined as
• LRuc -2ln(1-p)T-N pN 2 ln 1-(N/T)T-N
  (N/T)N
• It is asymptotically ?2 distributed with one
  degree of freedom. If the value of the
• LR-statistic exceeds the critical value of 3.84
  at the 95-quantile of the c2-distribution,the
  model performs well.

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Table Model Comparison through Backtesting
2001-2004
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Be careful about..

• Existence of fat tails in all segments (time
  buckets) of the Indian debt market. Can lead to
  misspecification of risks in Value at Risk
  models.
• The presence of significant structural break in
  the Indian debt market volatility since 2001.
  Reason LAF, Higher Turnover, Improved
  Settlement Practices.
• It is observed that both the unconditional
  parametric VaR model and the GARCH ( 1,1) model
  may lead to serious errors in estimation of risk
  in the Indian context. A higher order model of
  GARCH ( 6,1) led to much improved assessment of
  risk in Indian Government securities market.

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• The Third Concern
• Liquidity Risk

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The Problem

• Debt market in India is characterised by pockets
  of illiquidity, even though depth has increased
  and secondary market transactions have gone up.
• In the face of sudden and persisting off-market
  prices of some of the securities in their
  portfolio, the Indian financial organizations
  often found it difficult to offload these
  securities without booking significant trading
  losses.
• Market illiquidity have not been effectively
  incorporated into the Value-at-Risk (VaR) models.
• Measures of market risk fail to capture the costs
  of carrying illiquid assets in their portfolio.

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How to Capture?

• In this context, the paper looks examines the
  models of capturing liquidity risk.
• Using data on Indian Government securities
  market, the paper tries to provide an L-VAR model
  that incorporates liquidity risk in Value at Risk
  models.
• The paper tests the performance of L-VAR model
  vis-à-vis existing VAR models.
• The paper observes that in the Indian context,
  the liquidity risk is an important component of
  the aggregate risks borne by the financial
  institutions.

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Theoretical Backdrop

• Ad-hoc Approach (Lengthening Time Horizon)
• VaR turns out to be insufficient because the
  period used for its calculation does not allow
  for an orderly liquidation.
• Lengthening of the holding period ensures an
  orderly liquidation. The increase of the VaR
  number following the extension of the holding
  period can therefore be directly linked to the
  risk of liquidity.
• Optimal Liquidation Approach/ Transaction Cost
  Approach
• Lawrence and Robinson (1995), Bertsimas and Lo
  (1998), Almgren and Chriss (1998)
• They consider the trade-off of incurring a
  transaction cost by selling quickly vis-à-vis the
  exposure cost of holding on to the asset over a
  longer period.

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Theoretical Backdrop

• Liquidation Discount Approach
• Within the VaR framework, Jarrow and Subramanian
  1997 provide a market impact model of
  liquidity.
• The model of Jarrow and Subramanian is
  intuitively appealing but difficult to implement
  in practice as model derivation requires
  additional parameters for estimating Execution
  Lag Function.
• Exogenous Liquidity Approach
• Bangia, Diebold, Schuermann and Stroughair
  1999 provide a model of VaR adjusted for what
  they call exogenous liquidity defined as common
  to all market players and unaffected by the
  actions of any one participant.
• Bangia, Diebold, Schuermann and Stroughair (1999)
  argue that the deviation of this liquidation
  price from the mid-price are important components
  to model in order to capture the overall risk and
  derive an additive correction to a Gaussian
  single-asset VaR by computing the exogenous cost
  of liquidity.

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Methodology of the Study

• The return equation can be written as
• Rt ln(Pt)- ln (Pt-1)
  ..(1)
• Standard Parametric Value at Risk ( VaR) can be
  estimated as
• VaR Pt 1-e (-2.33 st)
  ..(2)
• The Conditional Volatility equation is based on
  the Generalized Autoregressive Conditional
  Heteroscedasticity model ( GARCH (1,1)
  represented by equations (3) and (4) below
• Yt Xt/ ? ?t
  ..(3)
• s t 2 ? a ?t-1 2 ß s t-1 2
  ..(4)
• where ? is the constant term, ?t-1 2 captures
  the news of volatility from previous period with
  the help of lagged squared residual of mean
  equation and s t-1 2 is the last periods
  forecast variance.

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Liquidity Risk in VAR models

• The liquidity Risk equation takes the following
  form
• COL ½ Pt (S ass) .(5)
• Where
• Pt mid-price of the asset
• S average relative spread, where relative spread
  is defined as (ask-bid)/mid. Relative spread
  acts as a normalizing devise among spreads.
• a is a scale factor to get 99 percent coverage.
• The Liquidity Adjusted Value at Risk Measure thus
  is
• LVAR Pt 1-e (-2.33 ? st) ½ Pt (S ass)
  ..(6)

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Table 2 Share in Outright Transactions of
Selected Securities 2003-04
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Table 7 Liquidity Risk in Indian Debt Market
end March 2004
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The Fourth Concern

• Interpreting Macro Data in the event of Shocks
• Ability to differentiate between short term and
  long term trends
• Episodic Evidence and scenario analysis may help
  risk managers in Indian Financial market. (
  Example below)

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Challenges to Risk modeling

• There are several risk models to tackle various
  kinds of risks
• There has been huge advances in the risk modeling
  literature
• Indian financial sector is trying to adopt best
  practices in risk modeling
• Commitment to Basel II

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Need to bridge the Gap

• There is a lot of academic research
• Only a fragment used by practitioners
• There is a clear need to enhance risk management
  knowledge in the country
• More interaction among risk managers a must
• Seminars, Workshops

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Role of PRMIA

• Seminars Basel II Masterclass
• Conferences India Risk Summit
• Networking, Discussions
• Online Search Engine Rose
• Free One day Workshops on Risk Management
• Collaborations with Institutions Worldwide
• More than 42,000 members.

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Thank You