ASSET ALLOCATION (RISK PARITY) VIA QUANTITATIVE MODELING IN EXCEL – AllQuant
Previously known as
ALL-WEATHER INVESTING VIA QUANTITATIVE MODELING IN EXCEL – AllQuant
COURSE OVERVIEW
This program institutionalizes risk parity portfolio construction—the methodology pioneered by Ray Dalio at Bridgewater Associates—into a deployable Excel-based system. Participants build an all-weather portfolio model that dynamically allocates capital across asset classes based on risk contribution rather than capital weighting. The curriculum emphasizes resilience during market dislocations while requiring approximately five minutes of daily operation time. No programming, chart reading, or continuous market monitoring is required.
Core Value Proposition: Construct a quantitative, risk-balanced portfolio that delivers equity-like returns with 30-40% lower volatility than traditional 60/40 allocations, using only free data sources and Excel.
LEARNING OBJECTIVES
Upon completion, participants will demonstrate competency in:
- Risk Parity Mathematics: Equalizing marginal risk contributions across asset classes rather than using nominal weights
- All-Weather Portfolio Theory: Building allocations that perform across inflation, deflation, growth, and recession regimes
- Quantitative Investing Framework: Distinguishing systematic, risk-based allocation from conventional capital-weighted approaches
- Leverage Mechanics: Applying prudent leverage to low-volatility assets to achieve risk-balanced target returns
- Rebalancing Optimization: Calendar-based versus drift-based rebalancing triggers
- Transaction Cost Integration: Modeling ETF expense ratios and trade commissions for realistic performance attribution
- Performance Metrics: Computing Sharpe ratio, risk-adjusted returns, and maximum drawdown for leveraged portfolios
- VBA Automation: Scripting data updates and weight optimization to minimize manual intervention
COURSE CONTENT STRUCTURE
Total Duration: Approximately 8 hours across 7 sections
- SECTION 1: INTRODUCTION (30 minutes)
- Pitfalls of buy-and-hold and traditional 60/40 allocations
- Risk parity philosophy: why equal risk matters more than equal capital
- Course roadmap and performance expectations
- SECTION 2: CONCEPT OF ALL-WEATHER INVESTING (90 minutes)
- Bridgewater's all-weather framework: four economic regimes (rising/falling growth, rising/falling inflation)
- Asset class behavior across regimes: equities, bonds, commodities, gold
- Risk contribution versus capital contribution: mathematical intuition
- Leverage rationale: why borrowing to increase low-risk asset exposure is rational
- Weaknesses analysis: interest rate regime changes, leverage-induced drawdowns
- SECTION 3: EXCEL CRASH COURSE (60 minutes)
- Critical functions: VLOOKUP, INDEX/MATCH, array formulas, conditional logic
- Data structuring for time-series analysis across multiple asset classes
- VBA fundamentals: recording and editing macros for automation
- Error checking protocols for leveraged portfolio models
- SECTION 4: FINANCIAL MATHEMATICS (90 minutes)
- Logarithmic returns for volatility calculation
- Rolling volatility and correlation matrix estimation
- Risk contribution calculation: covariance matrix decomposition
- Leverage modeling: margin requirements, borrowing costs, leverage drag
- Sharpe ratio derivation: handling leveraged portfolios and risk-free rates
- SECTION 5: BUILDING THE ALL-WEATHER RISK PARITY MODEL (180 minutes)
- Data acquisition: Yahoo Finance bulk download for equity, bond, commodity ETFs
- Covariance matrix construction: rolling 60-day window optimization
- Risk budget calibration: equalizing 25% risk contribution per asset class
- Leverage implementation: applying 2:1 leverage to bond positions via mathematical modeling
- Rebalancing logic: 10% drift threshold versus calendar-month rebalancing
- Transaction cost integration: ETF expense ratios (0.05-0.15%) and commission drag
- SECTION 6: RISK PARITY OPERATIONS (30 minutes)
- Daily workflow: 5-minute data update and signal verification protocol
- Risk monitoring: tracking leverage utilization and margin cushion
- Performance logging: separating beta returns from risk parity alpha
- Crisis protocol: deleveraging triggers during correlation breakdown (e.g., March 2020)
- SECTION 7: BONUS: VBA SCRIPTS (30 minutes)
- Automated data refresh: button-click update across all asset classes
- Weight optimization: Solver automation for risk budget rebalancing
- Error handling: managing missing data and corporate actions
DELIVERABLES & RESOURCES
- Fully Completed Model File: Live-ready Excel workbook with risk parity allocation engine, covariance matrix calculator, and leverage monitor
- VBA Automation Scripts: Pre-written macros for daily data updates and weight optimization
- Guided Build Templates: Step-by-step worksheets with partial completion scaffolding
- Practice Exercises: Risk contribution mathematics problem sets with detailed solutions
- Performance Analytics Worksheet: Portfolio-level Sharpe ratio, drawdown, and risk-adjusted return calculator
- Decision Dashboard: Interactive interface showing current weights, target risk budgets, and rebalancing alerts
TARGET AUDIENCE PROFILE
Optimal Fit:
- Investment advisors constructing resilient portfolios for high-net-worth clients
- Portfolio managers at family offices seeking institutional-grade asset allocation without Bloomberg terminals
- Sophisticated self-directed investors managing ₹50+ lakh portfolios requiring systematic risk management
- Risk officers evaluating portfolio construction frameworks for pension funds or endowments
- Quantitative analysts building strategic asset allocation models for asset management firms
Suboptimal Fit:
- Individuals seeking high-return stock picking strategies (focus is risk reduction)
- Participants uncomfortable with mathematical modeling of leverage and correlation
- Investors unable to maintain daily 5-minute discipline (model requires consistent updates)
- Traders requiring intraday tactical signals (strategy operates on daily/monthly frequencies)
PREREQUISITES & TECHNICAL REQUIREMENTS
Intellectual Prerequisites:
- Portfolio theory: understanding of correlation, covariance, efficient frontier concepts
- Statistics: mean, standard deviation, percentile calculations
- Derivatives basics: ETF structures, margin mechanics (for leverage module)
- Algebraic comfort: matrix multiplication concepts (simplified in Excel)
Technical Prerequisites:
- Microsoft Excel 2016 or later with VBA macros enabled
- Stable internet connection for daily Yahoo Finance data retrieval
- No prior VBA or programming knowledge required
- Software Provision: All analysis uses free resources; no mandatory data subscriptions
INSTRUCTOR BIOGRAPHIES
ENG GUAN – CO-FOUNDER & LEAD INSTRUCTOR
Quantitative investment practitioner with 15+ years spanning sovereign wealth funds, investment banks, proprietary trading desks, and multi-strategy hedge funds. Most recent role: key Portfolio Manager at a Singapore-based multi-strategy hedge fund, managing cross-asset systematic strategies with direct P&L responsibility. Holds MSc in Financial Engineering specializing in derivatives pricing and optimal execution algorithms.
Pedagogical Edge: Direct hedge fund implementation experience ensures instruction reflects operational realities: transaction cost management, leverage constraints, institutional risk mandates. Sovereign wealth fund background provides long-horizon capital preservation principles essential for strategic asset allocation.
PATRICK LING – CO-FOUNDER & SENIOR INSTRUCTOR
15+ years comprehensive investment industry experience across private banking (UBS), investment banking (Goldman Sachs), and hedge fund portfolio management. As a key Portfolio Manager at the same Singapore-based multi-strategy hedge fund, he co-managed systematic equity strategies and developed proprietary risk analytics. Holds MSc in Wealth Management integrating quantitative techniques with high-net-worth client portfolio construction.
Pedagogical Edge: Private banking experience translates quantitative allocation concepts into executable processes for non-institutional investors. Hedge fund tenure provides insight into multi-strategy portfolio integration and factor diversification—critical context for preventing over-leverage in risk parity implementation.
Joint Credibility: Both instructors maintain parallel practitioner careers, ensuring curriculum evolves with current industry standards rather than academic abstraction.
METHODOLOGICAL APPROACH
The course employs a "build-operate-stress test" framework. Participants construct the risk parity model, operate it through four economic regimes (1990s inflation, 2000s growth, 2008 crisis, 2020 pandemic), then stress-test leverage limits and correlation breakdowns. Each section concludes with scenario validation: what happens to the model during bond bear markets or equity crashes.
Instruction emphasizes risk-based thinking over return forecasting, teaching participants to view assets through marginal risk contribution lens rather than expected return lens.
Time Commitment: Video instruction totals 8 hours; practical implementation requires estimated additional 4-6 hours for independent model building and parameter calibration. Five-minute daily operation assumes stable model and reliable data feeds.
STRATEGY SCOPE & LIMITATIONS
Geographic Application: Explicit model calibrated for U.S. asset classes (equity ETFs: SPY, QQQ; bond ETFs: TLT, IEF; commodities: GLD, DBC) to ensure data availability. Mathematical architecture is transferable to Indian asset classes (Nifty 50 ETFs, Gilt funds, Gold ETFs) where historical data exists.
Capacity Considerations: Risk parity portfolios require minimum capital of ₹25 lakh for effective implementation across multiple ETFs while keeping transaction costs below 0.10% annually.
Performance Expectations: Model targets 10-12% annual returns with 10-12% volatility (Sharpe ratio ≈ 0.9-1.0), significantly improving on traditional 60/40's 8-10% returns with 12-15% volatility. During 2008 and 2020 crises, backtests show drawdowns of 15-20% versus 40-50% for 60/40 portfolios.
Leverage risk: Bond leverage creates 30-40% faster drawdowns during interest rate spikes (e.g., 2022 bond bear market)
Correlation breakdown: During liquidity crises, all asset correlations → 1.0, temporarily defeating risk parity diversification
Regime dependency: Strategy underperforms during strong equity bull markets when 100% equity allocation would dominate
BOTTOM-LINE ASSESSMENT
This program delivers exactly what it specifies: a hedge fund-caliber risk parity allocation system built entirely in Excel, with VBA automation and crisis-tested risk controls. The instructors' practitioner credentials provide rare authenticity, and the curriculum addresses the most dangerous aspects of risk parity (leverage, correlation breakdown) rather than presenting a sanitized version.
Critical Differentiator: Unlike academic risk parity courses, this explicitly teaches leverage implementation and contingency planning for correlation failures—the two factors that separate successful institutional implementation from retail disasters.
For investment advisors, portfolio managers, and sophisticated HNW investors seeking systematic, risk-balanced allocation without Bloomberg or Python infrastructure, this represents a professionally rigorous, operationally viable solution. The primary risk is behavioral: maintaining leveraged bond positions during rate spikes requires conviction that most retail investors lack.
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