Risk Parity Allocator

Equal weights by capital don't create equal risk contribution. If you put 25% each in equity (14% vol), gilts (6% vol), gold (13% vol), and currency (9% vol), the equity allocation dominates your risk. Equity contributes a disproportionate share of portfolio risk despite being only 25% of capital. True diversification requires equalizing risk contribution, which means low-vol assets need more capital and high-vol assets need less. This is what risk parity achieves.

It depends on how you access the markets. Full exchange-traded futures carry large notional - one FTSE 100 future is ~£90,000 (£10 per index point) and a Long Gilt future is £100,000 nominal - so true futures-based risk parity across 4 assets really needs £75,000-£100,000+ to size positions properly. Most retail traders instead use CFDs or spread bets, which allow fractional sizing (e.g., £1-£5 per point), so a meaningful 3-4 asset portfolio can be run from around £10,000. Either way, keep a healthy cash buffer for margin and remember FCA leverage caps apply to retail CFD/spread-bet accounts.

Yes - and this is a genuine advantage over markets that lack them. The ICE Long Gilt future (£100,000 nominal, the benchmark for the intermediate UK government bond curve) gives you a real bond leg. That means you can build the classic four-quadrant All-Weather portfolio - equities, government bonds, commodities, currency - the way it was originally designed, rather than improvising a bond substitute. You can also access gilts via gilt ETFs or CFDs/spread bets if you prefer smaller sizing. Bonds are the defensive anchor of risk parity, so having a liquid gilt future is a meaningful structural benefit.

Recalculate volatilities weekly or monthly for most implementations. Daily recalculation is overkill unless you're managing a very large portfolio or trading very actively. Volatility changes slowly enough that weekly updates capture meaningful shifts. For correlation updates, monthly is typically sufficient. What matters more is catching regime changes - if the VIX/VSTOXX spikes suddenly, that's worth an out-of-schedule recalculation. Build a routine: Friday evening calculate new volatilities, check if rebalancing needed.

Risk parity isn't about maximizing returns - it's about maximizing risk-adjusted returns. Unlevered risk parity typically returns 6-9% annually with 5-8% volatility (Sharpe ~0.8-1.2). With leverage to a 10% target vol, expect 8-12% returns but with proportionally higher risk. The key benefit: much lower drawdowns than equity-heavy portfolios. During 2008-style crashes, risk parity lost 15-25% vs 50%+ for equity. You give up some upside for dramatically better downside protection. Long-term compounding favours avoiding big losses. These are illustrative ranges, not guarantees.

Negative correlations are valuable - they reduce portfolio risk below what individual volatilities would suggest. In full risk parity optimization, negative correlations are properly handled: assets with negative correlation to the portfolio get slightly higher weight because they provide hedging. In inverse volatility (simple method), correlations are ignored, so you miss this benefit. Practical impact: the FTSE 100 is often negatively correlated to sterling (weak GBP lifts overseas earnings), so a long-USD/short-Cable position can hedge equity risk and earn extra weight in full optimization. Monitor correlations - if a previously negative correlation turns positive (regime change, as gilts and equities did in 2022), you lose that hedging benefit.

Leverage implementation guidelines: 1) Never exceed 2× leverage - tail risk magnifies dangerously beyond this. 2) Use futures for leverage (implicit in margin) or CFDs/spread bets within FCA caps (20:1 major indices and gold, 30:1 major FX, 10:1 other commodities). 3) Target portfolio volatility of 10-12% max initially. 4) Monitor realized volatility - if exceeding target by 50%, reduce positions. 5) Have drawdown stops - if portfolio drops 15%, reduce leverage. 6) Keep cash buffer (20-30% of portfolio) for margin calls. 7) Reduce leverage when VIX/VSTOXX is elevated (>20). Example: with £100k capital, unlevered portfolio vol 6%, target 10%, leverage 1.67×. Maintain £30k in cash/short-dated holdings, invest £70k × 1.67 = £117k exposure across assets.

It's a tradeoff between stability and responsiveness. Volatility: 60-day lookback is standard. Shorter (20-30 days) is more responsive but noisy. Longer (90-120 days) is more stable but slow to adapt. Consider EWMA (exponentially weighted) with half-life of 30-60 days for good balance. Correlation: use longer lookback (90-180 days) because correlations are noisier than volatilities. Shorter correlation estimates are very unstable. Some practitioners use different lookbacks for volatility (60 days) vs correlation (120+ days). Test different lookbacks on historical data to see impact on your specific assets.

Key metrics to track: 1) Actual risk contributions vs target - are they staying balanced or is one asset dominating? Calculate monthly. 2) Portfolio Sharpe ratio - should be 0.5-1.0+ over full cycle. 3) Maximum drawdown - should be lower than equity-only portfolio (15-25% vs 40-50%). 4) Correlation of portfolio returns to equity market - should be low (0.3-0.5). If your portfolio tracks the FTSE 100 closely, risk parity isn't working. 5) Rebalancing turnover - excessive turnover suggests unstable weights. Compare to benchmark: 60/40 portfolio or pure FTSE 100. Risk parity should have lower volatility and smaller drawdowns.

Futures advantages: built-in leverage (can achieve target vol without borrowing), more precise long/short positioning, deep liquidity in FTSE 100 and Long Gilt. Futures disadvantages: large notional per contract (lumpy sizing for smaller accounts), rollover and expiry management, basis risk. CFDs/spread bets advantages: fractional sizing, easy to go long/short, spread betting profits are currently tax-free for UK residents (no CGT, no stamp duty). CFDs/spread bets disadvantages: financing costs on held positions, wider effective spreads, FCA leverage caps. ETFs/ETCs advantages: simplest for long-only, can sit in an ISA/SIPP wrapper (gains then tax-free), no expiry. ETFs disadvantages: need margin to leverage, larger capital. Recommendation for the UK: Long Gilt and FTSE 100 futures for larger accounts; CFDs/spread bets for fractional sizing; gilt and gold ETFs/ETCs (e.g., in an ISA) for a simpler, tax-efficient long-only approximation.

HRP implementation steps: 1) Calculate correlation matrix for all assets. 2) Convert to distance matrix: d_ij = sqrt(0.5 × (1 - ρ_ij)). 3) Apply hierarchical clustering (Ward's method recommended). 4) Generate dendrogram showing asset clusters. 5) Quasi-diagonalize correlation matrix using cluster order. 6) Recursive bisection: at each node, split portfolio allocation between left and right branches inversely proportional to their variance. 7) Continue to leaf nodes (individual assets). Python implementation: use scipy.cluster.hierarchy for clustering, then custom recursion for allocation. Or use riskfolio-lib which has HRP built in. HRP produces weights that are more stable out-of-sample than traditional risk parity, especially with many assets.

Factor-based risk parity process: 1) Identify relevant factors for your universe. For the UK: equity market (FTSE 100 beta), duration (gilt/rates), commodity/inflation (Gold, Brent), currency (USD via Cable). 2) Calculate factor loadings via regression: regress each asset's returns on factor returns to get betas. 3) Estimate factor covariance matrix (volatilities and correlations of factors). 4) Set up optimization: find asset weights such that contribution to risk from each factor is equal. This is more complex than asset-level risk parity - you're equalizing factor-level risk contributions. 5) Constraints: long-only typically, weights sum to 1. Tools: Python with cvxpy for optimization, pandas for factor analysis. The result is a portfolio that's diversified across risk factors, not just across assets that may share the same factors.

Methods to incorporate tail risk: 1) CVaR-based risk parity: replace volatility with CVaR (5%) in the risk contribution calculation. Assets with fat tails get lower weight. Requires historical distribution estimation or fitting. 2) Fat-tail adjusted volatility: multiply volatility by kurtosis adjustment factor. Higher kurtosis = higher effective volatility. 3) Explicit tail hedge allocation: reserve 2-5% of portfolio for tail hedges (OTM FTSE 100 puts). Not part of risk parity optimization but overlay protection. 4) Regime-based adjustment: detect high-risk regimes (VIX/VSTOXX, correlation spikes) and reduce leverage/increase defensive allocation. 5) Stress testing: run portfolio through 2008, 2020 and the 2022 gilt crisis scenarios. If drawdowns exceed tolerance, adjust methodology. Recommendation: combine regime-based leverage reduction with small explicit tail hedge allocation.

Key limitations: 1) Volatility isn't risk - tail events not captured by historical vol. 2) Correlation instability - correlations spike during crises, exactly when diversification needed. 3) Leverage risk - levered risk parity can suffer severe losses when correlations spike + volatility spikes simultaneously. 4) Low expected returns - unlevered risk parity may not meet return targets. 5) Asset universe limitation - risk parity is only as good as your asset selection. 6) Estimation error - all inputs (vol, correlation) are estimated with error. Failure modes: 2008 crisis - levered risk parity suffered as correlations spiked and volatility exploded. The 2022 UK gilt crisis was especially instructive: gilts and equities fell together as rates surged, so the usual bond hedge failed and levered portfolios were hit hard. Mitigation: conservative leverage limits, regime detection, explicit tail hedges, avoid aggressive leverage during elevated volatility.

Institutional approaches: 1) Broader asset universe: global equities, government bonds (multiple countries including gilts, Treasuries, Bunds), credit, commodities, real estate, inflation-linked. 2) Factor tilts: overlay momentum, value, carry factors on base risk parity. 3) More sophisticated volatility models: GARCH, DCC (dynamic conditional correlation) for better forecasts. 4) Tail risk management: systematic tail hedging programmes, variance swaps. 5) Multiple timeframes: short-term tactical adjustments on strategic risk parity core. 6) Transaction cost optimization: optimize rebalancing considering market impact, bid-offer. 7) Cross-asset derivatives: use options and swaps for precise exposure management. 8) Dedicated risk systems: real-time monitoring, automated alerts, stress testing. Retail adaptation: can't replicate fully but adopt principles: diversified assets, volatility-based sizing, regime awareness, systematic rebalancing. Start simple, add sophistication as capital and expertise grow.