1. Information in the term structure of variance swaps and CFO predictions of volatility Whit Graham, Josh Kaehler, Matt Seitz

2. 2 Agenda Variance Swaps –What is a variance swap? –Term structure –Initial analysis/ findings –Key questions/ ongoing analysis CFO Survey- volatility predictions –CFO survey data –Initial findings –Questions for further study

3. 3 Introduction to Variance Swaps Variance swaps are similar to an interest rate swap, except the cash flows are based on the volatility of an underlying asset. (in our case, the S&P 500) Variance swap has a set term (ranging from 1-24 months, a volatility “strike”, and a notional amount). The party “long” the variance swap agrees to pay the fixed strike price at maturity, and receives the realized variance (counterparty does the opposite). The strike price is typically higher than “fair”/ realized volatility, because variance swaps are convex to volatility. Final Equity Payment = Variance Notional x (Realized Vol 2 – Strike Price 2 )

4. 4 Variance Swaps- Term Structure Typical term structure- mildly upward sloping in “normal” times; downward sloping in times of crisis. Can change significantly over time!

5. 5 Variance swaps- initial analysis and findings How well do variance swaps predict future realized volatility? –Reasonably well in the short term (1-3 months), far less accurate in the longer term. Does the “slope” of the curve have an impact? –Adding a “spread” or slope coefficient is always significant, but does not necessarily add a lot of explanatory power Ability of S&P variance swaps to predict volatility in other markets? –Using the S&P 500 v/s rate to predict volatility in the FTSE, DAX follows a similar pattern.

6. 6 Variance swaps- key questions & further analysis Can we predict equity returns based on past volatility + current v/s rates & term structure? If so, what would be the optimal trading strategy to capitalize? –How to determine how long to hold? Inclusion of “stop loss” limits? Implications/ extension to international markets including FTSE, DAX

7. 7 Duke/CFO Magazine Global Business Outlook Survey Data Quarterly Survey of Financial Executives –Analysis uses data from Q2 2000 to present –Average of 350 respondents Gauges respondent’s opinion on important economic data –Asks for both short-term (1 year) and long-term (10 year) market forecasts Study still relatively new with potential for untapped value

8. 8 Initial Areas for Study Does CFO volatility expectation have a relationship with what is realized in the market? –Responses Analyzed: Average of respondent standard deviations Disagreement between respondent market risk premiums Does outlook CFO’s have for their own companies or the broader economy serve as an indicator of future volatility? –Responses Analyzed: Own firm optimism diffusion Economy optimism diffusion

9. 9 Initial Takeaways Short-term CFO forecasts are better predictors of volatility Best relationships found with shorter horizon for realized volatility –i.e. “Disagreement” has better explanatory power for 1 month vol than 2 month vol Optimism of respondent’s own firm best indicator of short term volatility

10. 10 Ongoing Steps Testing best performers of first round of research on changes in implied volatility –Would allow for a quarterly VIX trading strategy Identify potential variance swap trading strategy based on current realized volatility research Address logistics of trading strategy –“Take profit” scenario, other signals