Market Risk Management

How do you really measure market risk? The theoretical way to answer this question is to list the many risk metrics centered around market risk and price. The unconventional way is to quote Michael Lewis from The Big Short about the usage of price risk and volatility in risk models, “The risk is the stupid trade that should have never happened”.

As a practitioner both responses leave a bit to be desired. The theoretical approach is fraught with issues and can lead to uncomfortable silences in boardrooms. The Lewis approach is a great one liner but has no follow through process that can be implemented.

A practical approach sits somewhere in between the two responses. A focus on metrics and measures that track volatility and positions combined with a focus on exposures, limits and reporting hierarchies that allows senior management team to keep an eye on the sensitivity of their P&L to volatility and market price movements.

Which is where the Sunoida Risk Manager Platform comes in. In addition to meeting regulatory compliance requirements, our platform focus on tools and infrastructure required to graduate from just compliance to risk management.

Credit Risk Management

From policy to proposal, from proposal to approval, from approval to disbursement, from disbursement to analytics, the credit management function seems to have its fingers everywhere. If you run the credit management function you need to be comfortable with all the dimensions of the function.

The challenge in credit risk however is the inherent conflict built in the nature of these dimensions. Proposals and approvals are market driven. Documentation and charges are legal. Analytics are performance and behavior driven. Provisions, recoveries and special assets use a completely different language and rely on negotiations, positions, etc. Hence the requirement for the head of credit risk to come with experience in both business development and corporate banking as well as special assets and credit administration. To this mix add technology and analytics.

Credit Analytics

Our credit analytics include:

• Breakdown by product, region, branches and segments of the days a payment is overdue (days past due or DPD analysis) across the entire banking franchise and how it compares with the overall industry average
• Client or industry specific credit downgrade by internal or external credit rating systems
• Changes in sector, segment, region provisions or loan classifications
• Changes in recoveries and write offs
• Bank exposures concentration across sectors, segments, products, markets and clients

The DPD tracking piece is the most crucial analytic generated by the credit management function. It is used not just in collections tracking and client management but also in provisions projections and capital management. But the source and control of DPD data is crucial. If the credit management function relies on branches to generate and compile DPD data they are just asking for trouble. For DPD data to be reliable and effective it should be generated automatically without manual intervention by any concerned or related party.

Credit Risk Management in Vision ERM

The Vision ERM platform offers two separate components:

• A BASEL II calculation engine that supports simplified, standardized and Internal Ratings Based (IRB) approach for calculation of capital adequacy. Features include risk weight configuration, risk calculation parameters and exposure tracking across categories
• A DPD and Provisions tracking calculator used for calculation of PD, LGD and EAD estimates

Operational Risk Management

Integrating Capital Adequacy Ratio Calculations

Our Capital Adequacy Ratio (CAR) dashboard pulls together CAR estimates by combining approved methods for Credit, Market and Operational Risk. On a single screen you can easily see the impact of your selection of methods on risk capital and capital adequacy ratios. In addition, it is also possible to compare 8 different combination of CAR figures created by mixing allowed approaches across credit, market and operational risk capital calculations.

Estimating Operational Risk Capital

The final step in the capital adequacy ratio estimation calculation is incorporating the impact of risk capital allocated to operational risk. The Vision ERM platform supports two operational risk capital calculation methodologies. Inclusion of Operational Risk Capital allows for the integrated calculation of capital adequacy ratio for the bank on one comprehensive platform.

The Basic Indicator Approach

Banks using the Basic Indicator Approach must hold capital for operational risk equal to the average over the previous three years of a fixed percentage (denoted alpha) of positive annual gross income. Figures for any year in which annual gross income is negative or zero should be excluded from both the numerator and denominator when calculating the average

The Standardized Approach

In the Standardized Approach, banks’ activities are divided into eight business lines: corporate finance, trading & sales, retail banking, commercial banking, payment & settlement, agency services, asset management and retail brokerage.

Within each business line, gross income is a broad indicator that serves as a proxy for the scale of business operations and thus the likely scale of operational risk exposure within each of these business lines. The capital charge for each business line is calculated by multiplying gross income by a factor (denoted beta) assigned to that business line. Beta serves as a proxy for the industry-wide relationship between the operational risk loss experience for a given business line and the aggregate level of gross income for that business line. It should be noted that in the Standardized Approach gross income is measured for each business line, not the whole institution, i.e. in corporate finance, the indicator is the gross income generated in the corporate finance business line.

The total capital charge is calculated as the three-year average of the simple summation of the regulatory capital charges across each of the business lines in each year. In any given year, negative capital charges (resulting from negative gross income) in any business line may offset positive capital charges in other business lines without limit. However, where the aggregate capital charge across all business lines within a given year is negative, then the input to the numerator for that year will be zero.

BASEL & Capital Adequacy Compliance

Our BASEL II compliant risk management solution is comprised of two components:

• Vision Risk Manager, a comprehensive enterprise risk management solution enabling the user bank to adequately cover Credit, Market and Operational Risk capital adequacy calculations
• A BASEL II compliant report generator with multiple reports for meeting BASEL II requirements, and ensuring compliance with reporting regulations relating to risk, asset liability management and stress testing

The two components together includes a broad array of additional functions which include ALM (Asset Liability Management), back testing, portfolio benchmarking and VaR (Value at Risk) for traditional products.

This includes BASEL Pillar I and Pillar II reporting, ALM, Market Risk, Credit Risk, Operational Risk, Reports & Dashboards.

Value at Risk Calculator

This includes BASEL Pillar I and Pillar II reporting, ALM, Market Risk, Credit Risk, Operational Risk, Reports & Dashboards.

• Variance /Covariance Method
• Historical Simulation Method
• The Monte Carlo Simulation Method

All methods have a common base but then diverge in how they actually calculate Value at Risk. They also have a common problem in assuming that the future will follow the past. This shortcoming is normally addressed by supplementing any VAR figures with appropriate sensitivity analysis and/or stress testing.

Our VaR calculation follows five steps:

• Identification of positions
• Identification of risk factors affecting valuation of positions.
• Assignment of probabilities (or statistical distribution) to possible risk factors values.
• Creation of pricing functions for positions as a function of values of risk factors.
• Calculation of VaR

Variance/Covariance Method

The Variance-Covariance method makes a number of assumptions. The accuracy of the results depends on how valid these assumptions are. The method gets its name from the variance-covariance matrix of securities that is used to calculate VaR.
The method starts by calculating the standard deviation and correlation for the risk factor and then uses these values to calculate the standard deviations and correlation for the changes in the value of the individual securities that form the position. If price, variance and correlation data is available for individual securities then this information is used directly. The values are then used to calculate the standard deviation of the portfolio. VaR for a specific confidence interval is then calculated by multiplying the standard deviation by the appropriate normal distribution factor.
In some cases a method equivalent to the variance covariance approach is used to calculate VaR. This method does not generate the variance covariance matrix. The modified approach can be used where, due to the nature of the institutions strategies, a number of positions would net close to zero on a portfolio basis and also where the set of securities employed is so large that a variance – covariance approach would have significant resource/time requirements

Historical Simulation Method

This approach requires fewer statistical assumptions for underlying market factors. It applies the historical (100 days) changes in price levels to current market prices in order to generate a hypothetical data set. The data set is then ordered by the size of gains/losses. VAR is the value that is equaled or exceeded the required percentage of times (1, 5, 10).

Monte Carlo Simulation

The approach is similar to the Historical simulation method described above except for one big difference. The hypothetical data set used is generated by a statistical distribution rather than historical price levels. The assumption is that the selected distribution captures or reasonably approximates price behavior of the modeled securities.

Balance Sheet Stress Testing Engine

In markets with increasing volatility and an ever changing economic environment which impacts financial systems banks need a way to quantify the extent of these impacts; stress testing provides that way. Stress Testing is a name given to a wide range of quantitative techniques which are employed by the banks to test the vulnerabilities of their financial systems. The idea is to apply shocks of various degrees on different portfolios and instruments and gauge their affect on the bank’s capital base. Stress Testing is also a regulatory requirement for most regulators. A sample stress testing framework based on a regional central bank prudential requires the banks to submit a stress test report on a semiannual basis.

We support three prominent stress testing techniques:

Simple Sensitivity Analysis

In this type of testing a shock is applied to an independent variable and its impact is assessed independently. There is no consideration for any underlying relationship which may exist between the tested variable and other variables in the system.

Scenario Analysis

In this type of testing a shock is applied to a variable and its impact assessed on all other dependent variables and the system as a whole.

Extreme Value/Maximum Shock

In this type of testing the level of shock which is applied is so high that it eliminates the capital base completely. Of the three techniques mentioned above, the Simple Sensitivity Analysis is the one required by the State Bank of Pakistan and it is the technique which we shall review for the remaining article.

Focus Areas & Shocks
The regulatory stress testing focuses on the areas of interest rate, non performing loans, stock prices, foreign exchange rate and liquidity risk. Each area is a subjected to minor, moderate and major level shocks. Then the impact of the revised numbers is assessed on the eligible capital, risk weighted assets and the capital adequacy ratio.

Asset Liability Management (ALM)

Asset Liability Management (ALM) involves taking decisions and actions regarding assets and liabilities in an integrated manner in order to manage the business of the entity and meet the organization’s financial objectives. It is a continuing process that involves formulating, implementing, monitoring and revising strategies related to its assets and liabilities keeping in mind the entity’s risk tolerances and constraints.

ALM is an essential and critical process for any organization that invests to meet its future cash flow needs and capital requirements. The traditional application of ALM primarily dealt with managing risks associated with interest rate changes. But today ALM has a much wider focus encompassing equity risk, liquidity risk, legal risk, currency risk and sovereign or country risk.

Vision Risk Manager – ALM Suite has been designed from the ground up by ALCO and ALM Committee Members over many years at our banking clients. It brings the best of both world by using account level data and analysis and a powerful report suite that allows Board and ALCO Committee Members to answer benchmark ALM questions:

• What is the impact of rising rates on earnings and shareholders value?
• What is the impact of interest rate mismatch on earnings and shareholders value?
• Sensitivity and Stress Testing ALM results based on historical rate volatility
• The impact of paper trades and balance sheet re-structuring on ALM results
• Linking ALM, Value at Risk & Capital requirements using historical interest rate movements and yield curve shifts

The ALM Suite focuses on the analysis of the following key risks:

Interest Rate Risk

Interest rate risk is the risk to earnings and/ or capital arising from changes in the interest rates. There are four primary sources of interest rate risk. They are:

Re-pricing or Maturity Mismatch Risk:

Risk arising from timing difference in the maturity and re-pricing of assets, liabilities and off balance sheet items.

Yield Curve Risk:

Risk arising from unanticipated shifts in the yield curve indicating a change in the relationship between interest rates of different maturities relating to the same market.

Basis Risk:

Risk arising from the imperfect correlation between earned and paid rates on instruments having similar maturities and re-pricing characteristics. This imperfect correlation results in unexpected changes in cash flows and earning spread.

Option Risk:

Risk arising from the seller or holder of an asset, liability or off-balance sheet item having the right to alter the level and timing of its cash flows when interest rates change.

Liquidity Risk:

Liquidity risk is the risk of potential loss to an entity due to the non-availability or insufficiency of liquidity. This could mean that the entity fails to meet it financial commitments and obligations because of its inability to convert assets into cash, or because it cannot obtain enough funds at a reasonable cost. Liquidity risk could also arise because of a market disruption or liquidity squeeze, which could hamper the entity’s ability to sell off its exposures or to do so at a loss or significant discount.

Limits Manager

If you are responsible for monitoring limits for treasury products, interbank counterparties and other financial institutions (the FI function), you need:

• An objective, timely mechanism for monitoring counterparty and product specific limits
• Regulatory compliance with the Prudential Regulations regime for counterparty, group and product exposures
• A reporting engine for ensuring limit breaches and exceptions get tracked and presented to the relevant authority within your bank within stipulated time frames

With the Vision Limit Manager Module, you can, avoid the damage and the cost of out of date, subjective limits by using a powerful tool that tracks, benchmarks and optimizes limit allocation by products and counterparties

Vision Limit Manager allows you to define and manage:

• Counterparties
• Products
• Dealers
• Authorizers
• Middle Office Limit thresholds
• Reporting hierarchies

Vision Limit manager comes with a suite of built-in reports that allow you to track:

• Limit utilization by counterparties, products and dealers
• History of limit breaches and exceptions by dealers and trading desks
• Compliance with Middle Office and Prudential requirements issued by central bank

Reports & Dashboards

The in-built reporting engine consists of standard reports and dashboards based upon years of banking and Risk management experience. Some of the key reports that are generated automatically are:

ICAAP (Internal Capital Adequacy Assessment Process)

• Stress Testing PD (Stress test screen & report)
• Concentration Risk
• Liquidity Gap
• Fall in Market Value of Equity (MVE) Graph
• ICAAP (Summary report)
• Earning at Risk Graph

Credit, Market & Operational Risk

• Capital Adequacy Calculation

Asset Liability Management

• Price Sensitive
• Rate Sensitive
• Liquidity Gap
• Net Interest Income (NII) at Risk
• Earnings at Risk
• Make these
• bullet points as
• shown below
• Cost to Close
• Fall in Market Value of Equity (MVE)

Value at Risk

• Variance Covariance VaR Analysis – Position wise
• Variance Covariance VaR Analysis – Portfolio
• IRS Value at Risk
• VaR Comparison
• Fixed Income Bond Value at Risk (Summary)
• Value at Risk Comparison (Detailed)
• Duration Value at Risk Comparison (Summary)
• Value at Risk Analysis on Foreign Exchange (in USD)
• Currency Forwards Value at Risk
• Value at Risk (Histogram)
• Value at Risk (Line Graph)
• Currency Options Value at Risk
• Structured Products Value at Risk
• Value at Risk by Tenors for Fixed Income Securities
• Incremental Value At Risk
• Risk Dashboard Detail
• Risk Dashboard Summary
• Risk Dashboard Highlights
• VaR Trend
• VaR Summary (Traditional & Derivatives)

Cash Flow & Profit and Loss

• Cash Flow Maturity Report (Summary)
• Cash Flow Maturity Calendar
• Profit & Loss Statement
• Balance Sheet Daily P&L