Calculate VaR, expected shortfall, and stress losses for a portfolio
A credit memo or rating note with borrower profile, ratio analysis, risk rating, downside case, and monitoring triggers.
Build
Model, memo, dashboard, notebook, or deck.
Explain
Clear assumptions, insight, and recommendation.
Show
Resume bullet and interview story.
What you are doing
You are working as a Asset Management. Your manager asks you to use Risk modeling to answer a real business or investment question and present a decision-ready output.
Show that you can apply Risk modeling in a practical analyst workflow, not only explain the theory.
What to make
A credit memo or rating note with borrower profile, ratio analysis, risk rating, downside case, and monitoring triggers.
Deliverables
- Brief
- Model or notebook
- Charts or dashboard
- Resume bullet
- Source and assumption log
- One-page executive summary
- Final memo PDF
How to start
- Pick a borrower, issuer, or sector case.
- Collect financials, debt schedule, ratings, business profile, and management commentary.
- Define the lending/rating question and time horizon.
Step-by-step execution
- Analyze revenue quality, margins, leverage, coverage, liquidity, and working capital.
- Identify business, financial, management, and industry risks.
- Run base and stress cases for cash flow and debt servicing.
- Assign an internal risk view or rating direction.
- Write monitoring triggers and early warning signals.
Data and sources
- Annual report
- Credit rating rationale
- RBI/SEBI filings where relevant
- Sector data
- Company news and lender disclosures
Tools to use
- Python or Excel
- Excel or Google Sheets
- PowerPoint / Google Slides
- Public filings
Quality rubric
- Credit ratios are calculated consistently.
- Cash flow analysis matters more than accounting profit.
- Risk view is supported by evidence.
- Stress case is severe but plausible.
- Memo ends with a clear lend/avoid/watch recommendation.
Resume bullet
Built a a credit memo or rating note with borrower profile, ratio analysis, risk rating, downside case, and monitoring triggers. for Risk modeling, using Python or Excel to convert raw information into a decision-ready finance output.
Interview talk track
- Problem: explain the business question and why it matters for Asset Management.
- Method: describe the data collected, assumptions made, and analysis performed.
- Decision: state the recommendation, key risk, and what would change your view.