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\title{\textbf{Seeing Through the ETF: Indicative NAV and Commodity Volatility Transmission }
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\author{Simon-Pierre Boucher\thanks{PhD student in finance, Universit\'e Laval, Quebec City QC Canada G1V 0A6, email: \href{mailto:simon-pierre.boucher.1@ulaval.ca}{simon-pierre.boucher.1@ulaval.ca}}
\and Marie-H\'el\`ene Gagnon\thanks{Professor of Finance and Research Fellow, CRREP, Universit\'e Laval, email: \href{mailto:marie-helene.gagnon@fsa.ulaval.ca}{marie-helene.gagnon@fsa.ulaval.ca}}
\and Gabriel J. Power\thanks{IG Wealth Management Chairholder, Professor of Finance and Research Fellow, CRREP and CRIB, Universit\'e Laval, email: \href{mailto:gabriel.power@fsa.ulaval.ca}{gabriel.power@fsa.ulaval.ca}}}

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\noindent
This paper builds a novel dataset of indicative Net Asset Value (iNAV) observations for commodity ETFs and measures volatility transmission between ETFs and their underlying baskets using high-frequency realized variance. Applying this design to crude oil, gold, silver, and natural gas, we show that the iNAV provides a sharper image of the volatility relationship. Decomposing realized variance into continuous and jump components, we establish that transmission runs primarily through jumps rather than diffusion, a channel that standard volatility connectedness measures may obscure. Sampling frequency also matters: 1-minute data reveal daily volatility transmission up to twice as large as 30-minute estimates. Finally, transmission differs across commodity types. For gold and silver, it is unidirectional from iNAV to ETF, consistent with passive arbitrage. For energy ETFs, which are more liquid and hold futures as underlying assets, transmission is bidirectional and asymmetric.


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\noindent\textbf{Keywords:} ETF, volatility, transmission, commodity markets, futures, intraday, realized variance, jump, HAR, arbitrage, microstructure.\\
\textbf{JEL Classification:} G12, G13, G14, C32

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