Intraday arbitrage between ETFs and their underlying portfolios

Box, Travis; Davis, Ryan L.; Evans, Richard B.; Lynch, Andrew A.

doi:10.1016/j.jfineco.2021.04.023

Cited by 54 publications

(13 citation statements)

References 27 publications

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“…Third, the results display largely the same patterns when performing the regressions for each minute of the day separately. Fourth, replacing one-minute intervals with five-or ten-minute intervals yields comparable results, but with higher R 2 s. Fifth, the results are robust when using the log transformation suggested by Box et al (2021) to address potential kurtosis issues in returns and order imbalances. Sixth, the conclusions are the same when using trading costs in dollars and a control variable for the share price.…”

Section: Ta B L Ementioning

confidence: 83%

“…(2018), express concern that ETFs' liquidity can attract short-horizon traders, and that liquidity shocks can communicate to the underlying securities through the arbitrage mechanism. Testing this idea with a panel vector autoregressive model and intraday data, Box et al (2021) do not find evidence that shocks to ETFs' returns or order flow propagate to their underlying stocks.…”

Section: Relationship With Returnsmentioning

confidence: 91%

“…Testing this idea with a panel vector autoregressive model and intraday data, Box et al. (2021) do not find evidence that shocks to ETFs’ returns or order flow propagate to their underlying stocks.…”

Section: Relationship With Returnsmentioning

confidence: 99%

“…Using midpoint quotes instead of last-trade prices alleviates most of the staleness issues that are problematic with intraday intrinsic values (IIVs). 2 The paper closest to this one in terms of intraday analysis is that of Box et al (2021), who use monthly data on ETFs to infer daily holdings and compare the bid and ask prices of the ETF and its underlying portfolio. Their focus is different from mine, as they analyze the impact-or, more precisely, the lack thereof-of ETF trading on their underlying securities.…”

Section: Introductionmentioning

confidence: 99%

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ETFs’ two‐sided trading costs and order imbalances

Lachance

2022

The Financial Review

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This paper proposes an integrated trading cost measure for exchange-traded funds (ETFs) that includes premiums and spreads. The measure is computed separately for buyers and sellers as the distance between the ETF's bid/ask prices and a new measure of intraday intrinsic value constructed with the ETF's daily holdings. The two-sided measure shows that order imbalances increase trading costs in the direction of the imbalance, bias midpoint quotes, and result in observed premiums and discounts. It also provides a link between these higher trading costs and the need for authorized participants to pass through the costs associated with primary market activity.

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Section: Ta B L Ementioning

confidence: 83%

Section: Relationship With Returnsmentioning

confidence: 91%

Section: Relationship With Returnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ETFs’ two‐sided trading costs and order imbalances

Lachance

2022

The Financial Review

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“…The GMM methodology is an estimation technique used in many studies that examine the intraday characteristics of liquidity (Box et al 2021;Ben Ammar and Hellara 2021;Ozturk et al 2017). Using the Newey and West (1987) approach, the calculated t-statistics are adjusted for heteroskedasticity and autocorrelation.…”

Section: Hypothesis 2 a Positive Relation Exists Between Volatility A...mentioning

confidence: 99%

Volatility and Depth in Commodity and FX Futures Markets

Aidov

Lobanova

2021

JRFM

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Prior theory suggests a positive relation between volatility and market depth, while past empirical research finds contrasting results. This paper examines the relation between the volatility and the limit order book depth in commodity and foreign exchange futures markets during a turbulent time using the generalized method of moments (GMM). Results indicate a negative relation between volatility and depth and suggest that the depth in the limit order book decreases as volatility increases. Findings help to understand how market participants provide liquidity in response to shifts in prices.

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