Portfolio Valuation for a Retail Bank using Monte Carlo Simulation and Forecasting for Risk Measurement

Banks today need to have a very good assessment of their portfolio value at any point in time . This is both a regulatory requirement and an operational metrics which helps banks to assess risk of their portfolio and also calculate the Capital Adequacy that they need to maintain at portfolio levels , product levels and all of these aggregated at Bank level.

This presentation will walk you through a case study which will discuss in detail how we went about calculating Portfolio value for a Home loan on a sample data . The bank wanted a scientific /statistical approach to this as they could take this to regulators for approval and thus convince them about the capital that they have for a particular portfolio.

The other interesting dimension was that in case the bank wants to sell a particular loan book to another bank /third party financial institutions they would be able to quote a price within the confidence interval of the calculated price. The same model/tool could be also shared with the buyer to convince them on quoted price and will make the negotiation and selling smooth.

We have used Monte Carlo Simulation on historical data of the portfolio to measure the Portfolio Value for the next 5 years of a Home loan Portfolio. It is a two step modeling process with Machine Learning Models to predict default and then further using simulation to calculate Portfolio value year on year for next 5 yrs taking in account diminishing returns too.

The presentation will take you through the approach and modeling process and how Monte Carlo Simulation helped us deliver the same to Customer with high accuracy and confidence level.

This is a real case study and will focus on why Risk Measurement is important and why Basel , CCAR implementation across banks worldwide helps the Central Banks to manage risks in case of a financial downturn or Black Swan events.

 
 

Outline/Structure of the Demonstration

1. Importance of Risk Assessment by Banks/ Financial Institutions - 2-3 mins

2. How Central Banks use the Regulatory Frame work to measure overall Capital Adequacy across Banks/FIs? - 2-3 mis

3. Discussion of a real case study of a Bank where the need was of Portfolio Valuation and pricing using Forecasting Techniques like Monte Carlo Simulation 5 mins

4. Solution Presentation with details on Modeling Approach 5 mins

5. How Bank benifitted from this Valuation Modeling? 2-3 mins

Learning Outcome

1. Clear understanding of Risk Assessment in Banks and why it is important

2. What is Capital Adequacy ratio and how does Central Banks/RBI ensure the same across all major Banks/Financial Institutions across India?

3. Understanding of Risk Models and Statistics behind them

4. Monte Carlo Simulation for Forecasting

Target Audience

Bankers , Data Scientists , MBA Finance , Financial Consultants , Risk Officers , Regulatory Compliance Officers ,Banking professionals who work in Regulatory Reporting / Model Validation, Data Modellers

Prerequisites for Attendees

Participants need to have an understanding of the Banking Process , Regulatory Guidelines and how Banks calculate Risk of portfolios using statistical models

schedule Submitted 5 months ago

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comment Suggest improvements to the Author
  • Natasha Rodrigues
    By Natasha Rodrigues  ~  5 months ago
    reply Reply

    Hi Kavita,


    Thanks for your proposal! Requesting you to update the Outline/Structure section of your proposal with a time-wise breakup of how you plan to use 20 mins for the topics you've highlighted?

    Thanks,

    Natasha

  • Ashay Tamhane
    By Ashay Tamhane  ~  5 months ago
    reply Reply

    Thanks for a very interesting proposal. Suggestion: Could you mention some of the practical challenges faced when you tried to implement the simulation? 

    • Kavita Dwivedi
      By Kavita Dwivedi  ~  5 months ago
      reply Reply

      Hi Ashay ,

      Thanks for reviewing the proposal. Practical Challenges where quiet a few while implementing this  and great that you brought in :

      1. The Portfolios to be valued generally where from different data bases and with different profile information as the banks generally have a consolidated portfolio which they buy over years from different banks. So we had to first work through to create a standard set of features that was the minimum required info defined as part of the portfolio data.

      2. As this was done for a quarterly regulatory requirement and Banks sometimes would need to value a portfolio within couple of days , to quickly negotiate the sell  a particular portfolio . So we had to create a tool that would help a Bank user to do the same using it. The tool was using Excel , VBA frontend and  Python in the background to complete the entire valuation with a click of button. So loads of customization and options had to be designed within the coding and in front end to meet Banks requirements.

      3. As the bank portfolios are quiet dynamic , we had to work through to see that the model accuracy remains good even if their is a sample with some bias. So we achieved it by building our model on data from different time periods and finally making a solution free of any sample biases.

       

      Thanks and regards,

      Kavita

       

       

       

       


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    Badri Narayanan Gopalakrishnan - Global Supply Chain Analytics

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