The deep liquidity at equity markets’ open and close auctions typically provides price discovery and facilitation of block order executions. Trading at these centralized, large-scale liquidity events permits institutional investors to establish sizeable positions without undue complexity [1]. To participate effectively in auctions, we need to understand how an order can potentially affect the auction price, namely the market impact. While market participants have a relatively good understanding of the market impact in continuous trading sessions, there is much less knowledge about auction market impact in the public domain.

The conventional method to calibrate a market impact model is to run regression analysis on the price change before and after the order against the order size using proprietary order data accumulated over many years. However, applying this method to estimate auction market impact has two major drawbacks:

There is only a single open and a single close auction each day, so a large data set takes a very long time to accumulate. Moreover, the auction price used in such regressions reflects only the final equilibrium price and not the price dynamics to reach the equilibrium, which is often used in the calibration of a continuous market impact model.

The regression using a given firm’s trading data is often noisy because the firm’s orders only represent a small percentage of the total number of orders entering the auction.



Full text: tradersmagazine

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