Discussion: liquidity Creation as Volatility Risk by Itamar - - PowerPoint PPT Presentation

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Discussion: liquidity Creation as Volatility Risk

by Itamar Drechsler, Alan Moreira, and Alexi Savov Yunzhi Hu University of North Carolina FMA, Nov 9, 2018

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Summary

Motivation: liquidity and volatility

• 1. Transaction (participation/inventory) cost: Nagel (2012)
• 2. Asymmetric information: this paper

Main idea: A Kyle (1985) static model with stochastic volatility

◮ All orders flow in at date 1; asset pays off at date 1

pi,1 = constant + σi,1

• sto. vol

vi

• private information

◮ Informed traders’ information is more (less) valuable during periods

• f high (low) realized volatility.

◮ By providing liquidity, market makers have negative exposure to

volatility risk.

◮ Asset-level volatility is highly correlated with aggregate volatility.

Hence volatility risks cannot be diversified away.

◮ Liquidity creation demands a premium.

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Summary

◮ Empirics

• Liquidity providers’ positions ≈ short-term reversal portfolios
• Short-term reversal strategies have negative exposure to volatility

shocks (≈ ∆VIX)

• The five-day large stock reversal return drops by 64 bps if VIX rises

by an average of one-point per day over the holding period.

• The impact persists for (at least) five days.
• Volatility risk exposure is priced and accountable for the average

returns of the reversal strategies.

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Comment 1: robust evidence

• 1. This paper implies that liquidity creation suffers losses during

periods of high realized volatility. Do we see this in the data? Corr

• Rp

t,t+5, RVt,t+5

• ≶ 0
• 2. Days before earnings announcement versus other periods?
• 3. NYSE versus NASDAQ?
• 4. Dealer driven versus algorithm driven?
• Split the sample by 2005
• Menkveld (2016): HFTs avoid carrying a position overnight. Use

(close price - open price) to calculate returns?

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Comment 2: measurement

◮ Which returns?

• Bid-ask bounce? Use quote midpoint changes?
• Hedged returns or raw returns? Market makers hedge common factor

exposures with S&P 500 futures contracts.

◮ ∆VIX or ∆VIXt VIXt−1 ?

• The volatility of volatility

◮ Portfolio formation: why weighted by dollar volume

• In Lehmann (1990) and Nagel (2012), the weights predicted by the

model are derived from previous period’s returns.

• Campbell, Grossman, and Wang (1993) suggests illiquidity should be

measured by return autocovariance conditional on trading volume. Llorente, Michaely, Saar, and Wang (2002) show that conditional γ correlates negatively with measures of asymmetric information.

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Comment 3: effect persistence

◮ Is the persistence driven by persistence in ∆VIX?

∆VIXt = −0.1114

(0.0157) ∆VIXt−1 + εt ◮ When do the effects finally disappear?

• When is private information revealed in price? With multiple insiders,

information revelation slows down. (Foster and Viswanathan (1996), Back, Cao, and Willard (2000))

• Half-life of dealer inventory: 2.5 days in Hansch, Naik, and

Viswanathian (1998); 0.92 days in Hendershott and Menkveld (2014)

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Comment 4: short-term reversal strategies

◮ Can returns to reversal strategies capture more than returns to

liquidity creation?

• Sentiment: fads, overreaction, cognitive errors

◮ Are negative βs driven by buying losers or selling winners?

• Da, Liu, and Schaumburg (2014): buying losers load on illiquid

measures (Amihud and realized volatility of S&P 500 index); selling winners load on lagged investor sentiment (IPOs and equity issuance)

• Stambaugh and Yuan (2017): investor sentiment predicts the short

legs of mispricing factors