Innovation in Treasury Futures: Examining Volatility Patterns Before and After the Shift From Open-Outcry to Electronic Trading

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1 Sacred Heart University WCOB Faculty Publications Jack Welch College of Business Innovation in Treasury Futures: Examining Volatility Patterns Before and After the Shift From Open-Outcry to Electronic Trading Lucjan T. Orlowski Sacred Heart University, Bluford Putnam Sacred Heart University Follow this and additional works at: Part of the Finance and Financial Management Commons Recommended Citation Orlowski, Lucjan T. and Bluford Putnam. "Innovation in Treasury Futures: Examining Volatility Patterns Before and After the Shift From Open-Outcry to Electronic Trading." Market Risk and the Impact of Financial Volatility Luxembourg May This Presentation is brought to you for free and open access by the Jack Welch College of Business at It has been accepted for inclusion in WCOB Faculty Publications by an authorized administrator of For more information, please contact

2 " I n n o v a t i o n i n T r e a s u r y F u t u r e s : E x a m i n i n g V o l a t i l i t y P a t t e r n s b e f o r e a n d a f t e r t h e s h i f t f r o m O p e n - O u t c r y t o E l e c t r o n i c T r a d i n g " Lucjan T. O r l o w s k i a n d Bluford P u t n a m Presentation at L u x e m b o u r g Conference L u x e m b o u r g, M a y 18, 2012

3 K e y O b j e c t i v e s I d e n t i f y m a j o r s t a g e s o f t r a n s f o r m a t i o n o f U. S. 1 0 Y T r e a s u r y futures t r a d i n g f r o m o p e n - o u t c r y to G l o b e x e l e c t r o n i c E x a m i n e m a j o r f e a t u r e s o f institutional c h a n g e s at e a c h s t a g e o f this t r a n s f o r m a t i o n

4 F o u r D i s t i n c t P h a s e s o f T r a n s i t i o n f r o m P i t t o G l o b e x ( s e e F i g u r e 1) 1. Pit Only Phase. The period of exclusive CBOT floor (open-outcry auction) trading runs from the inception of the US 10-Year Treasury Note futures trading on May 3, 1982 to August 28, 2000 when the CBOT US 10-Year Treasury Note futures was added to the Globex trading platform. 2. Hybrid or Transitional Phase. A hybrid period, with active trading in both the pit session and on the electronic trading system existed from August 28, 2000 (4% share trading on Globex) to September 11, 2003 (Globex's share reaching 90%). 3. Globex Dominant Phase. The phase of a dominant, well-established electronic Globex trading system ran from September 12, 2003 to November 26, The end-point coincides with a major dive in the Globex share from 99 to 90 percent as the effects of the US subprime mortgage debacle spread through the financial system. 4. Globex Leadership Phase, involving a renewed role for pit trading. The commencement of the limited revival of pit trading corresponded with the elevated systemic risk crisis period from November 26, 2007 and has continued through the end of 2011 (the end of our data range). This phase is characterized by the dominance of Globex but with some, discernible role for the floor-based trading.

5 Figure 1: Share of U.S. Treasury 10-Year Note futures trading volume on Globex as a percent of total trading volume: 1.0 8/28/ Phases: I i i i i i i i i i i i i i i i i i i i i i i i i i T j i r Data source: CME Group.

6 Institutional Difference I : Price Discovery Pit vs. Globex Diagnostic Statistics of H i g h - L o w Price Ratio Figure 2; The ratio of" logs of* high-to-low price Fig. 2a: JPit sessions. Sample period: Pvlay 3, 1982 January 18,2012 1,600 1, _ 1,ooo - eoo - ooo m o.ooooo ^ ^^ I , S S O.OOSOO Series: l_og(hprr)/l_og(l_rrr>-1 Sample 5/03/1982 1/18/2012 Observations 7463 Mean Median Maximum Minimum Std. Dev, Skewness Kurtosis Jarque-Bera Probability O.OOI o.oooooo O.OOOOOO Strong leptokurtosis, right skewed. Figure 2t»: GLOBEX sessions. Sample period August 29, 2000 January Series: LOG(HPRE)/LOG(LPRE)*1 ^ Sample 5/03/1982 1/18/2012 Observations 28S _. Mean O.OOI 3SO pr i ** r Median O.OOI * 3" Maximum Minimum f H < " ^ * # f7 Std. Dev J «., * S kewness ,_ K- s 1 K^ * Kurtosis ',*s* *s Jarque-Bera r # it- I h Rrobability O.OOOOOO O.COST'S Higher mean, even more leptokurtic, and more right skewed than the distribution for the same ratio for pit trading.

7 Institutional Difference II Responses of trading volume to the difference between high and low prices on pit vs. Globex Figure 3: Impulse responses of total 10Y Treasuries trading volume to a one-standard-deviation shock in the change in high-low price spread on pit vs Globex sessions Fig. 3a: Pit sessions Response to Cholesky One S..D. Innovations ± 2 S.E. Response of DLOG(TRV1 OY) to D(HPRR-LPRR) n ^.2- A 1 I I I -" I i 6

8 Fig. 3b: Globex sessions. Response to Cholesky One S.D. Innovations ± 2 S.E. Response of DLOG(TRVIOY) to D(HPRE-LPRE) Source: Authors' estimation based on CME Group data.

9 F i g u r e 3 R e s u l t s Pit: N o c h a n g e in total 1 0 Y T r e a s u r i e s trading v o l u m e in r e s p o n s e to the h i g h - l o w price s p r e a d o n pit s e s s i o n s G l o b e x : a sharp i m m e d i a t e i n c r e a s e in trading v o l u m e in r e s p o n s e to a w i d e r h i g h - l o w price spread, f o l l o w e d b y a correction T h i s r e a c t i o n u n d e r s c o r e s the stronger role o f d a y traders in electronic m a r k e t s It reflects s u d d e n s h o c k s i n d u c e d b y a flight to quality at t i m e s o f financial distress that are n o r m a l l y f o l l o w e d b y the s e c o n d d a y c o r r e c t i o n A r g u a b l y, t r a d e r s s e e m to r e a c t m u c h f a s t e r to the n e w i n f o r m a t i o n in e l e c t r o n i c trading, w h i l e in p i t s e s s i o n s the n e w s s p r e a d s o v e r t i m e

10 A n a l y s i s o f R e t u r n s a n d V o l a t i l i t y D y n a m i c s G A R C H - M - G E D M o d e l T h e c o n d i t i o n a l m e a n equation: (\ r>h\ t+t = y Q + ya(vol) t + y 2 au + M, ( 3 ) T h e c o n d i t i o n a l v a r i a n c e equation: &? = h 0 + \tf-x + + h p Ml P + v.^1, v q a]_ q (4)

11 Table 6: GARCH(p,q)-M-GED Tests (Eqs. 3 and 4). Dependent variable: Change in the logs of high/low price ratio for the day of 10 Y Treasury 10-Year Note Futures. Regressors: Change in the 10Y Treasury 10-Year note futures trading volume (VOL); log ofgarch term (loggarch). Variables: Conditional mean equation: Phase I Phase II Phase III Phase IV Constant term VOL GARCH var. Conditional -variance equation: 0.001*** (8.54) 0.039*** (43.73) *** (-10.84) 0.001*** (4.07) 0.007*** (21.15) *** (-4.66) 0.001*** (4.40) 0.002*** (36.17) *** (-5.86) 0.001*** (3.43) 0.002*** (23.97) *** (-4.80) Const. ARCH(l) ARCH(2) ARCH(3) ARCH(4) ARCH(5) ARCH(6) ARCH(7) GARCH(l) 0.001** 0.423*** *** ** ** NA 0.976*** *** *** ** ** NA 0.897*** *** *** *** NA NA NA NA NA 0.981*** GED parameter Diagnostic statistics: 1.242*** 1.387*** 1.163*** 1.177*** Adj usted R 2 AIC SIC Log Likelihood Durbin-Watson Notes: Z-statistics are in parentheses; AIC=Akaike information criterion; SIC= Schwartz Information criterion; *** indicates significance at \ A>, ** at 5%, * at 10%. Source: Authors estimation based on CME data.

12 T a b l e 6 R e s u l t s Phase I: increase in volume in pit sessions associated with a higher high-low price ratio; negative G A R C H in mean = higher risk engenders lower returns (underscoring risk hedging function of Treasuries); highly persistent conditional variance; unruly impact of high-order A R C H news on volatility; pronounced tail risk (high leptokurtosis implied by GED parameter lower than 2) Phase II: increase in volume in pit sessions associated with a higher high-low price ratio; negative G A R C H in mean; less persistent G A R C H variance; complex A R C H terms; significant tail risks Phase III: less pronounced direct relationship between higher volume and high-low price ratio; much lower G A R C H persistency, but stronger role of ARCH-type shocks implying high susceptibility to random shocks; strongest leptokurtosis, i.e. most extreme tail risks; most robust result among all four phases Phase IV: increase in volume in pit sessions associated with a higher high-low price ratio; negative G A R C H in mean; return of high G A R C H persistency in volatility; weaker tail risks than in phase III; weaker role of ARCH-type surprises to volatility

13 C o n d i t i o n a l V o l a t i l i t y C h a r a c t e r i s t i c s o f t h e F o u r T r a n s i t i o n a l P h a s e s T r a n s i t i o n from pit to G l o b e x h a s b e e n q u i t e d r a m a t i c, b u t t h e r e is n o w s o m e r o o m for p e r s o n to p e r s o n t r a d e s T r a d i n g v o l u m e s t r o n g l y r e a c t s to s h o c k s i n h i g h / l o w p r i c e ratio in e l e c t r o n i c trading, b u t n o t in o p e n - o u t c r y T h e p r i c e d i s c o v e r y ( h i g h / l o w p r i c e ratio) is s t r o n g l y a f f e c t e d b y t h e t r a d i n g v o l u m e, r e g a r d l e s s o f t h e institutional t r a n s i t i o n P h a s e III e x h i b i t s q u i t e different c o n d i t i o n a l volatility characteristics t h a n t h e r e m a i n i n g t h r e e p h a s e s ; volatility d u r i n g t h e G l o b e x - d o m i n a n t p e r i o d w a s d r i v e n p r e d o m i n a n t l y b y A R C H ( l ) - t y p e s h o c k s a n d h a d l o w p e r s i s t e n c y D u r i n g P h a s e III, c h a n g e s in h i g h / l o w p r i c e ratio w e r e s u b j e c t t o t h e m o s t e x t r e m e tail r i s k s

14 A S u m m a r y : H i g h l i g h t s o f O u r F i n d i n g s A rapid transition (a leap) from open-outcry to electronic trading Substantial increases in trading activity and open interest within the transition process Trade matching at the lightening speed > lower transaction costs > high frequency trading In Globex dominant trading, external (ARCH-type) shocks or surprises hit the Treasury futures market > a greater propensity for price jumps (discontinuities) and > higher trading volume; the day after resumption of normal volumes gets shorter Higher correlations between price volatility and intra-day high-low price spreads in Phases III and IV» market surprises (shocks) cause problems for option traders relying on straight-forward implied volatility models (i.e. Black-Scholes) Phase III shows discernible volatility characteristics, i.e. predominance of A R C H - t y p e shocks, significant tail risks; nevertheless, the higher volatility might be attributable to the risk-taking associated with the sub-prime debacle