Implied Volatility Around Earnings Announcements

MS&E 444June 4, 2007

Geoff EvansPablo Jadzinsky

Yu LauJane Wang

Ryan Williams

MS&E 444 • Group 4 • June 4, 20072

Project Overview Are stock returns fat-tailed excluding days

around earnings?• Yes, but less so• Days within a 14 day window of earnings

announcements account for a substantial proportion of kurtosis

• Skewness / kurtosis coefficients change around earnings

MS&E 444 • Group 4 • June 4, 20073

Project Overview How can we characterize implied volatility

of call options around earnings announcements?• Formal characterization framework• N=30 stocks, variety of industries / sectors• Varied time windows (30 to 180 days) • No clear patterns emerge across stocks

MS&E 444 • Group 4 • June 4, 20074

IBM Daily Log-Returns

Kurtosis: 9.922

Skewness: .0146

Normal? p < .001

MS&E 444 • Group 4 • June 4, 20075

Log-Returns Excluding Earnings

Kurtosis: 5.234

Skewness: .1095

Normal? p < .001

MS&E 444 • Group 4 • June 4, 20076

S&P 500 Constituent Stocks

MS&E 444 • Group 4 • June 4, 20077

S&P 500 Constituent Stocks

MS&E 444 • Group 4 • June 4, 20078

Options Trades Near EarningsGoldman Sachs

Option with highest open interest

Volu

me

Impl

ied

Vola

tility

MS&E 444 • Group 4 • June 4, 20079

Identifying Earnings Effects Problem: Too many variables changing

Need to isolate effects of earnings

Solution: Use moneyness to normalize for strike and expiration

Normalize implied volatilities as well for comparison over different stocks

TrTKSm

σ+

=)/ln(

atm

atm

σσσσ −

=~

MS&E 444 • Group 4 • June 4, 200710

Normalized Volatility Surface

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Normalized Volatility Intensity Plot

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No Pattern Emerges

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No Pattern Emerges

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No Pattern Emerges

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Normalized Volume Surface

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Future Work Calculate moneyness based on fat-tailed

distributions

Put options, Small Cap stocks

Predict earnings outcomes based on options trading• Statistical pattern matching• Normalized implied volatility as a feature