The Weekly Print
1. Market Regime Snapshot
VIX Term Structure
| Tenor | Level | vs. Spot |
|---|---|---|
| VIX Spot | 14.9 | β |
| VIX 3M | 20.5 | +5.6 (Contango) |
Realized vs. Implied Vol: SPY 20D RV = 14.4%, VIX = 14.9. Spread = -0.5pp (realized vol below implied β calmer than feared).
Cross-Asset Momentum (1-Month)
| Asset | 1M Return | Signal |
|---|---|---|
| SPY (Equity) | +2.4% | π Positive |
| TLT (Bonds) | -1.6% | π Negative |
| GLD (Gold) | +5.7% | π Positive |
| UUP (US Dollar) | -1.1% | π Negative |
Regime: Mixed/transitional regime
Last updated: 2026-08-09 17:22 UTC
2. Factor Performance Dashboard
Source: ETF Proxies (MTUM, VLUE, QUAL, USMV, IWM vs SPY)
Note: ETF proxy returns include market beta and are not directly comparable to factor-neutral French library returns.
| Factor | Weekly | 1M | 3M | Mean (52W wkly) | Std (52W wkly) | Z |
|---|---|---|---|---|---|---|
| Momentum | +3.25% | -3.70% | +5.36% | +0.50% | 2.74% | +1.00 |
| Value | +3.40% | +1.30% | +13.45% | +1.13% | 2.53% | +0.90 |
| Quality | +2.90% | +3.29% | +7.94% | +0.40% | 1.53% | +1.64 |
| Low Volatility π¨ | +2.75% | +3.09% | +7.06% | +0.18% | 1.17% | +2.19 |
| Size | +0.05% | -1.39% | +1.07% | +0.23% | 1.56% | -0.11 |
π¨ Factor Stress: Low Volatility (z=+2.19) β weekly return β₯ 2Ο from trailing mean.
Last updated: 2026-08-09 17:22 UTC
3. Macro Signal Tracker
| Indicator | Current | 1W Change | Signal |
|---|---|---|---|
| HY Spread (OAS) | 271bps | -13bps | β Tightening |
| IG Spread (OAS) | 78bps | -2bps | β Tightening |
| 2s10s Yield Curve | 0.46% | -0.01% | Normal |
| 3M10Y Yield Curve | 0.78% | -0.14% | Normal |
| Fed Funds Rate | 3.63% | N/A | β |
| HY β IG Spread | 193bps | β | Risk sentiment proxy |
Macro Summary: Risk-on macro backdrop
Last updated: 2026-08-09 17:22 UTC
4. Quant Research Digest
Three papers I found worth reading this week:
From Value Bounds to Policy-Distance and Active-Face Certificates: Same-Grid Duality for Constrained Dynamic Portfolios β Jeonggyu Huh arXiv
This paper provides a primal-dual framework to identify which constraints are actually binding when solving dynamic portfolios via numerical solvers or neural networks. It offers a useful mathematical diagnostic for verifying how far a numerical policy deviates from the true optimal boundary when exact analytical solutions are unavailable.
Cross-Sectional Heterogeneity in LSTM Networks for Financial Time Series β Julius DΓΆbelt arXiv
The author explores how standard LSTM architectures struggle with financial data because they fail to capture cross-sectional differences between assets. It highlights the importance of adapting deep learning models to handle asset-specific heterogeneity rather than treating an entire equities universe as uniform sequence data.
Portfolio Allocation under Heterogeneous Scales and Multifractality β Shinji Kakinaka et al. arXiv
This research structures a portfolio allocation model using multifractal cross-correlation analysis to handle financial signals that vary by time scale and fluctuation amplitude. It provides a highly mathematical alternative to standard covariance matrices by modeling how asset correlations inherently shift across different time horizons.
Last updated: 2026-08-09 17:22 UTC
5. Stat of the Week
| Stat | Value | Context |
|---|---|---|
| CBOE Skew Index | 133 | Elevated tail risk (>130) |
Low Volatility was the main outlier this week (+2.75%, z = +2.19). Itβs an interesting contrast to see low-vol assets up and Skew above 130 during a week where SPY also finished positiveβa good example of why checking factor breakdowns is useful.
Last updated: 2026-08-09 17:22 UTC
Generated: 2026-08-09 17:22 UTC