{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T10:46:07Z","timestamp":1792752367908,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":11,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Theoretical volatility harvesting models frequently fail in live emerging derivatives markets because continuous-time delta-hedging abstractions ignore discrete microstructural friction. This paper documents the forensic diagnosis and architectural evolution of a volatility and correlation harvesting framework on the National Stock Exchange (NSE) of India across both a high-frequency intraday horizon (60 trading days, 1, 374 bars at 15-minute frequency) and an 18-year macroeconomic horizon (2008-2026, T = 4, 547 daily sessions). In the intraday microstructure phase, we demonstrate that an unconstrained baseline Volatility Risk Premium (VRP) engine-combining a decoupled 2-year daily EGARCH(1,1) volatility forecaster [9], a raw-VIX 2-state Gaussian Hidden Markov Model (GHMM) [7], and a continuous discrete-time Kalman filter-generates +17.85% gross alpha but collapses into a-18.50% terminal net loss. Forensic audit reveals three structural failure modes: (i) 1, 374 unthrottled rebalances bleeding 36.35% in statutory Securities Transaction Tax (STT) and bid-ask spread drag; (ii) an uncalibrated measurement noise variance (V e = 0.01) choking the Kalman hedge ratio at \u03b2 t \u2248-0.015 against a true econometric elasticity of-5.05; and (iii) the unsupervised raw-VIX GHMM splitting a calm summer distribution at its median (VIX = 12.2), triggering 627 false alarms (79.2% cash lockout). Re-architecting the intraday regime filter to trigger strictly on structural tail expansions (10 genuine shock bars across 3 volatility clusters, &amp;lt; 10% cash lockout) and wrapping the calibrated Kalman state estimator inside a Whalley-Wilmott cubic-root hysteresis deadband (\u03b4 VRP = 0.40) [8, 10] slashes rebalance trades by 91.7% (1, 374 \u2192 114) and reverses the-18.50% loss into a +21.41% net gain (1.83 net Sharpe, 2.41 Sortino). Scaling the VRP architecture across an 18-year macroeconomic horizon (2008-2026) via a stationary Volatility Risk Ratio GHMM (VRR t = VIX t \/\u03c3 EGARCH,t) yields a +13.78% annualized CAGR (2.75 net Sharpe,-5.88% maximum drawdown) and survives the March 2020 COVID-19 Black Swan shock (VIX 83.61). However, higher-moment audit reveals a critical vulnerability: standalone VRP exhibits extreme fourth-moment kurtosis (\u03b3 4 = 500.6) and negative skewness (\u03b3 3 =-14.80 to-15.84) due to unhedged overnight gap risk. To eliminate this tail fragility, we introduce a point-in-time Dynamic Partial Principal Component Analysis (PCA) Dispersion Engine [1, 6] harvesting the Correlation Risk Premium (CRP) across&amp;nbsp;high-idiosyncratic-variance constituents (IVRi &amp;gt; 0.80, &amp;gt; 5% index heavyweights excluded, \u03b4CRP = 0.04 futures deadband). Over 18 years, standalone Dynamic CRP compounds at +12.25% CAGR with positive skewness (+0.48), low kurtosis (6.8), and near-zero correlation (\u03c1 = \u22120.022) to VRP. Blending an 80\/20 or 70\/30 Multi-Pod slashes portfolio kurtosis by 78% to 94% (500.6 \u2192 107.0 \u2192 29.8), compresses 18-year maximum drawdown to \u22124.20%\u2013\u22124.76%, elevates the Bailey\u2013L\u00f3pez de Prado Probabilistic Sharpe Z-score from +4.27 to +6.79, and passes the Extreme Value Theory Deflated Sharpe hurdle at DSR = 99.89% (p = 0.0011) [2, 3], delivering a 16\u00d7 Calmar ratio improvement (2.85\u20133.21) over conventional equity indices (Nifty 50, S&amp;amp;P 500, and Russell 2000).<\/jats:p>","DOI":"10.2139\/ssrn.7655539","type":"posted-content","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:54:16Z","timestamp":1792688056000},"source":"Crossref","is-referenced-by-count":0,"title":["Overcoming Execution Drag in Volatility Harvesting: Asymptotic Hysteresis, Partial PCA Orthogonalization, and Multi-Pod Higher-Moment Sterilization"],"prefix":"10.2139","author":[{"given":"Somay","family":"bansal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"ref1","first-page":"761","volume":"10","author":"M Avellaneda","year":"2010","journal-title":"Statistical arbitrage in the US equities market"},{"issue":"2","key":"ref2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.21314\/JOR.2012.255","article-title":"The Sharpe ratio efficient frontier","volume":"15","author":"D H Bailey","year":"2012","journal-title":"Journal of Risk"},{"issue":"5","key":"ref3","first-page":"94","article-title":"The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting, and non-normality","volume":"40","author":"D H Bailey","year":"2014","journal-title":"Journal of Portfolio Management"},{"issue":"11","key":"ref4","doi-asserted-by":"crossref","first-page":"4463","DOI":"10.1093\/rfs\/hhp008","article-title":"Expected stock returns and variance risk premia","volume":"22","author":"T Bollerslev","year":"2009","journal-title":"The Review of Financial Studies"},{"issue":"3","key":"ref5","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1093\/rfs\/hhn038","article-title":"Variance risk premiums","volume":"22","author":"P Carr","year":"2009","journal-title":"The Review of Financial Studies"},{"issue":"3","key":"ref6","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1111\/j.1540-6261.2009.01467.x","article-title":"The price of correlation risk: Evidence from equity options","volume":"64","author":"J Driessen","year":"2009","journal-title":"The Journal of Finance"},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3699-1","author":"E R Fernholz","year":"2002","journal-title":"Stochastic Portfolio Theory"},{"issue":"2","key":"ref8","doi-asserted-by":"crossref","first-page":"357","DOI":"10.2307\/1912559","article-title":"A new approach to the economic analysis of nonstationary time series and the business cycle","volume":"57","author":"J D Hamilton","year":"1989","journal-title":"Econometrica"},{"issue":"5","key":"ref9","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1111\/j.1540-6261.1985.tb02383.x","article-title":"Option pricing and replication with transactions costs","volume":"40","author":"H E Leland","year":"1985","journal-title":"The Journal of Finance"},{"issue":"2","key":"ref10","doi-asserted-by":"crossref","first-page":"347","DOI":"10.2307\/2938260","article-title":"Conditional heteroskedasticity in asset returns: A new approach","volume":"59","author":"D B Nelson","year":"1991","journal-title":"Econometrica"},{"issue":"3","key":"ref11","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1111\/1467-9965.00034","article-title":"An asymptotic analysis of an optimal hedging model for option pricing with transaction costs","volume":"7","author":"A E Whalley","year":"1997","journal-title":"Mathematical Finance"}],"container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T10:25:30Z","timestamp":1792751130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7655539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":11,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7655539","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}