SayPro Monthly June SCRR-22 SayPro Monthly Research Risk Metrics: Use risk metrics Start Date: 06-01-2025 End Date: 06-30-2025

SayPro Provide a structured, replicable process for extracting risk metrics.

SayPro Risk Metrics Extraction Process

Report: SayPro Monthly June SCRR-22
Period: June 1–30, 2025

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1. Define Objectives and Metrics

🎯 Goal:

Quantify and evaluate the risk profile for SayPro’s operations, investments, or financial instruments in June 2025.

📌 Common Risk Metrics:

• VaR (Value at Risk)
• CVaR (Conditional Value at Risk)
• Volatility (σ)
• Sharpe Ratio
• Max Drawdown
• Beta and Alpha (vs. benchmark)
• Exposure by asset class or region
• Stress Testing Scenarios

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2. Data Preparation

📂 Inputs:

• SCRR-22 report data (daily returns, positions, exposures, benchmark returns)
• Market data for risk-free rate and benchmarks

🧹 Cleaning:

• Ensure time series continuity (no missing dates)
• Normalize data (log returns if needed)
• Align benchmark and portfolio returns

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3. Calculate Risk Metrics

For June 1–30, 2025:

a. Daily Return Calculation:

pythonCopyEditdaily_return = (Price_t / Price_{t-1}) - 1

b. Volatility:

Standard deviation of daily returns annualized:

pythonCopyEditvolatility = std(daily_returns) * sqrt(252)

c. VaR (95% confidence):

pythonCopyEditVaR_95 = -quantile(daily_returns, 0.05)

d. CVaR (Expected Shortfall):

pythonCopyEditCVaR_95 = -mean(daily_returns[daily_returns < quantile(daily_returns, 0.05)])

e. Sharpe Ratio:

Assuming constant risk-free rate (Rf):

pythonCopyEditSharpe = (mean(daily_returns) - Rf/252) / std(daily_returns)

f. Max Drawdown:

Track peak-to-trough drop:

pythonCopyEditdrawdown = (current_value - peak_value) / peak_value

g. Beta and Alpha:

Regression against benchmark returns:

pythonCopyEditPortfolio_Return = Alpha + Beta * Benchmark_Return + error

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4. Stress Testing (Optional)

Simulate hypothetical shocks:

• Interest rate change
• Commodity price drop
• Market crash scenarios

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5. Reporting Format

Organize extracted metrics in a standardized template:

Metric	Value	Unit
Volatility	X.XX%	Annualized
VaR (95%)	-X.XX%	Daily
CVaR (95%)	-X.XX%	Daily
Sharpe Ratio	X.XX
Max Drawdown	-X.XX%	Period
Beta (vs Benchmark)	X.XX
Alpha	X.XX%

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6. Automation & Replication Tips

• Build a Python/R script to ingest SCRR data and compute metrics.
• Store historical metrics in a version-controlled repository.
• Maintain a metadata log: data source, timestamp, script version.