Free Market Risk Management Portal For Small Banks And Companies

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This is a brief description of a free service started by IFMR (www.ifmr.ac.in/cafs) for small banks and small companies who are unable to manage their market risk / currency risk exposures.

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Free Market Risk Management Portal For Small Banks And Companies

  1. 1. Risk Management Portal PROF. R. L. SHANKAR ED, CENTRE FOR ADVANCED FINANCIAL STUDIES AT IFMR [email_address]
  2. 2. Agenda <ul><li>Motivation </li></ul><ul><li>A Quick Demo of the Portal </li></ul><ul><li>A Note on the Methodology </li></ul><ul><li>Key Features of the Portal </li></ul><ul><li>Next Steps </li></ul>
  3. 3. Motivation <ul><li>Let us consider a toy trading desk with this simple portfolio: </li></ul><ul><ul><li>Long 1mn of 3 month AUD/INR forward </li></ul></ul><ul><li>The middle office wants to compute the 1day 99%ile VaR </li></ul><ul><ul><li>What does it mean? </li></ul></ul><ul><li>If the 99%ile 1-day VaR turns out to be 1mn INR, </li></ul><ul><ul><li>There is only a 1% probability that the desk’s one-day loss will exceed 1mn INR </li></ul></ul><ul><ul><li>This number, INR 1mn, is the Value at Risk – a measure of risk that is very popular in the finance/banking industry </li></ul></ul>
  4. 4. Motivation <ul><li>Here is how a naïve risk manager would evaluate the VaR </li></ul><ul><li>Data Used: </li></ul><ul><ul><li>3month AUD/USD forward rate: 0.925 </li></ul></ul><ul><ul><li>1-day 99%ile VaR for a 3month USD/INR forward is 1.04 INR </li></ul></ul>
  5. 5. Motivation <ul><li>Data Used: </li></ul><ul><ul><li>3month AUD/USD forward rate: 0.925 </li></ul></ul><ul><ul><li>1-day 99%ile VaR for a 3month USD/INR forward is 1.04 INR </li></ul></ul><ul><li>Algorithm Used: </li></ul><ul><ul><li>1mn of 3mo AUD/INR fwd= 0.925mn of 3mo USD/INR fwd </li></ul></ul><ul><ul><li>VaR for the position is 1.04 X 0.925mn INR = 0.962mn INR </li></ul></ul><ul><li>Issues? </li></ul><ul><ul><li>It is clear that some risk managers don’t have the capability to measure risk; they rely on FEDAI numbers even if it means they ignore significant risks such as the AUD/USD risk </li></ul></ul><ul><ul><li>This is for a single asset; how does it look for a portfolio? </li></ul></ul>
  6. 6. Motivation <ul><li>Let us tweak the portfolio of the toy trading desk a bit </li></ul><ul><ul><li>Long 1mn of 3 month AUD/INR forward </li></ul></ul><ul><ul><li>Short 1mn of 6 month AUD/INR forward </li></ul></ul><ul><li>It has the following data from FEDAI; what is its VaR? </li></ul>
  7. 7. Motivation <ul><li>Data Used: </li></ul><ul><ul><li>3month & 6-month AUD/USD forward rate: 0.925 (assume) </li></ul></ul><ul><ul><li>1-day 99%ile VaR for a 3month USD/INR forward is 1.04 INR </li></ul></ul><ul><ul><li>1-day 99%ile VaR for a 6month USD/INR forward is 1.27 INR </li></ul></ul><ul><li>Algorithm Used: </li></ul><ul><ul><li>VaR for the 3m position is 1.04 X 0.925mn INR = 0.962mn INR </li></ul></ul><ul><ul><li>VaR for the 6m position is 1.27 X 0.925mn INR = 1.174mn INR </li></ul></ul><ul><ul><li>Total VaR = 2.136mn INR </li></ul></ul><ul><li>Issues? </li></ul><ul><ul><li>Is VaR additive? </li></ul></ul><ul><ul><li>Complete disrespect for risks in a long-short trategy </li></ul></ul><ul><ul><li>Ignoring skewness? </li></ul></ul>
  8. 8. Motivation <ul><li>There is a significant demand for a tool that calculates the VaR for banks/trading desks that don’t have the capability to measure risk (either in-house or through external support) </li></ul><ul><li>There are service providers such as Algorithmics who provide these services online; however, they charge a heavy fee for the same </li></ul><ul><li>Hence, we decided to build a tool that would let banks and FIs enter their portfolios and compute their risk </li></ul><ul><ul><li>Funded completely by IFMR Trust </li></ul></ul>
  9. 9. Motivation <ul><li>“ Requirement of Independent VaR model: From next year onwards bankers will have to maintain capital based on daily VaR based on historic or Monte Carlo simulation. Though some vendors claim to have built models, it is doubtful how many of them will be able to pass RBI validation required as per norms. </li></ul><ul><li>Besides owning the engine banks will have to maintain it ensuring constant data feed and with occasional stress tests. Given the capability it may be pretty tough for each bank to own and maintain such systems. </li></ul><ul><li>There is an urgent need for developing a hosted model (algorithmic type) that can be used by several banks without losing their privacy or confidentiality. “ </li></ul><ul><ul><ul><ul><ul><li>– ED, Federal Bank </li></ul></ul></ul></ul></ul>
  10. 10. A QUICK DEMO OF THE PORTAL
  11. 11. Accessing the Portal <ul><li>http://122.183.251.6/var/ username: ifmr_test </li></ul><ul><li>password: ifmr_test </li></ul><ul><li>Once you login, you can also take a quick demo that’s available at http://122.183.251.6/var/FlashDemo/FlashDemo.html </li></ul>
  12. 12. A BRIEF NOTE ON THE METHODOLOGY
  13. 13. Historical Simulation <ul><li>Historical Simulation is highly popular among banks for one key reason </li></ul><ul><ul><li>Doesn’t require the banks to model the actual distribution of the underlying data – normal, log-normal, student-t etc </li></ul></ul><ul><li>Banks typically implement this technique in either of the following two variants </li></ul><ul><ul><li>Weighted Historical Simulation </li></ul></ul><ul><ul><li>Hybrid Hull-White Simulation </li></ul></ul><ul><li>Our portal implements both the techniques </li></ul>
  14. 14. Historical Simulation: Basic Version <ul><li>Put simply, HS assumes that the distribution of tomorrow’s returns is well approximated by the “observed” distribution of past n-days’ daily returns </li></ul><ul><li>To be more precise, here is how HS works: </li></ul><ul><ul><li>We do not make any assumptions about tomorrow’s returns </li></ul></ul><ul><ul><li>We gather last n days returns (say n = 100) </li></ul></ul><ul><ul><li>We assume that each of these returns “represent” a possible outcome of tomorrow’s return (random event) </li></ul></ul><ul><ul><li>Based on these “forecasted” returns, we calculate next day’s profit/loss </li></ul></ul><ul><ul><li>We sort these P/L and obtain the lowest 5%ile value – this corresponds to VaR </li></ul></ul>
  15. 15. Weighted Historical Simulation <ul><li>Assigns differential weights to past data </li></ul><ul><ul><li>Start with a sample of n past returns, {R t , R t-1 , R t-2 , R t-n+1 } </li></ul></ul><ul><ul><li>Assign probability weights that are declining exponentially through past as </li></ul></ul><ul><ul><li>It is customary to assume  to be between 0.94 and 0.99 </li></ul></ul><ul><ul><li>For very large  , i.e., for a very old data, weight is close to zero </li></ul></ul><ul><ul><li>The 100p% VaR is calculated by accumulating the weights of the ascending returns until 100p% is reached </li></ul></ul>
  16. 16. Hybrid Hull-White Simulation <ul><li>Hybrid Hull-White Methodology </li></ul><ul><ul><li>Obtain the volatility forecast for tomorrow, say σ t+1 </li></ul></ul><ul><ul><li>Build a sample of n past returns, {R t , R t-1 , R t-2 , R t-n+1 } </li></ul></ul><ul><ul><li>For each of these days, also obtain the one-day volatility forecast (using GARCH, EWMA etc), { σ t , σ t-1 , σ t-2 , σ t-n+1 } </li></ul></ul><ul><ul><li>Use the following modified return series for the Historical Simulation </li></ul></ul><ul><li>What have we achieved by this transformation? </li></ul>
  17. 17. Historical Simulation <ul><li>As mentioned, our portal uses both variants of simulation for its computation purposes </li></ul><ul><li>I haven’t delved into details, but it is probably a good idea to highlight this: since we let the user enter his position in a range of currencies and tenor points, we have done the hybrid simulation (volatility forecasts etc) for each of these points! </li></ul>
  18. 18. A SUMMARY OF KEY FEATURES
  19. 19. Key features <ul><li>Any user can create/modify multiple portfolios </li></ul><ul><ul><li>Currently, the user can enter position in six major currencies (constrained by availability of long data series) </li></ul></ul><ul><li>The user doesn’t have to regularly download market data as the portal does that automatically </li></ul><ul><li>To meet regulatory requirements, any risk computation must pass certain litmus tests, back-testing being the most crucial one. The portal lets the user performs this too </li></ul><ul><li>The risk numbers are computed for any required percentiles (unlike FEDAI which provides only the 99%ile VaR for USD) </li></ul>
  20. 20. Key features <ul><li>From an implementation perspective, the entire code was written in Matlab and interfaced with HTML code </li></ul><ul><li>This is an interesting approach as it lets us completely harness the power of Matlab </li></ul><ul><ul><li>If any of you are interested in developing a web-based quantitative service as part of your extended research project, this could be a good direction to think along </li></ul></ul>
  21. 21. NEXT STEPS
  22. 22. Product Innovations <ul><li>We have already built the Matlab code for currency options and will be soon rolling out the next release for the same </li></ul><ul><li>In the next phase, we will also include fixed income instruments (particularly Government Securities) and their derivatives in the product space </li></ul>
  23. 23. Methodological Innovations <ul><li>The portal currently uses the most popular VaR technique – the Historical Simulation </li></ul><ul><ul><li>To be more specific, we use the Hybrid Hull-White Historical Simulation that is recommended by FEDAI </li></ul></ul><ul><li>In the final stage, we will introduce Monte Carlo based VaR computation too </li></ul><ul><li>We would be glad to welcome anyone who is interested in these problems to collaborate with us </li></ul>
  24. 24. THANK YOU

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