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I am trying to measure the effects that Market and Funding liquidity have on a certain portfolio of U.S. stocks. I proxy market liquidity by using the Pastor-Stambaugh (2003) liquidity factor and use the TED spread as a proxy for funding liquidity. The paper that I'm reading that is sort of similar to what I'm trying to do is: Value and Momentum Everywhere by Asness, Moskowitz and Pedersen (2013).

Here they measure the funding and market liquidity risk exposure of value and momentum portfolios.

They say

To measure liquidity risk exposure, we regress value and momentum returns on shocks to liquidity. We consider both funding liquidity shocks (e.g., Brunnermeier and Pedersen (2009)) and market liquidity shocks.

  1. Now I'm wondering about why they are using shocks specifically and not just the level of liquidity in a certain month?

So the level of Pastor and Stambaugh liquidity factor for a certain month and the level of the TED spread in that same month to proxy for market and funding liquidity.

They also note how they define shocks:

The funding series are available for the common period January 1987 to July 2011. We define shocks to these variables as the residuals from an AR(2) model, following Korajczyk and Sadka (2008) and Moskowitz and Pedersen (2012)

So, why do they use the residuals of market liquidity (Pastor and Stambaugh monthly liquidity factor) and funding liquidity (monthly TED Spread) from an AR(2) model instead of just using the TED spread itself for example? What is the reason for doing this?

  1. What would the difference be in the interpretation of:
  • Running the regression of portfolio returns on the level of the TED spread and Pastor-stambaugh liquidity factor

  • Running a regression of those same portfolio returns on the residuals obtained from an AR(2) model of the TED spread and Pastor-stambaugh liquidity factor?

Thanks in advance!

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  • $\begingroup$ I guess there are some theoretical reasons to this, e.g. standard theory says stock returns should primarily react to "news" i.e. unexpected shocks. It might also work better empirically. $\endgroup$ – fesman Nov 21 '20 at 14:51

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