Circuit Breakers, Volatility, and the U.S. Equity
Markets: Evidence from NYSE Rule 80A*
Michael A. Goldstein
University of Colorado at Boulder

Joan E. Evans
Federal Reserve Bank of New YorkJames M. MahoneyFederal Reserve Bank of New York

Abstract
After October 1987, the United States equity markets adopted various rules,
collectively known as “circuit breakers,” to reduce stock market volatility. Most
circuit breakers have been triggered only rarely. However, New York Stock
Exchange Rule 80A, which limits destabilizing index arbitrage trades, has been
triggered frequently, sometimes more than once a day. Using both parametric andnon-parametric tests, we compare volatility during periods when Rule 80A is ineffect and when it is not. Controlling for other determinants of volatility, we findthat Rule 80A leads to a small but statistically significant decline in intraday U.S.

*
The authors would like to thank Mark Ventimiglia of the International and Research Divisionof the NYSE for providing the data for this study and Alejandro Latorre of the Capital Markets
Function of the FRBNY for excellent research assistance. We would also like to thank George
Sofianos, James Cochrane and James Shapiro of the NYSE. Much of this analysis was completed
when Michael Goldstein was the Visiting Economist at the New York Stock Exchange. The views
expressed in this paper do not necessarily reflect those of the Federal Reserve Bank of New York,the Federal Reserve System, or the New York Stock Exchange.

386

1

Michael A. Goldstein, Joan E. Evans, and James M. Mahoney

Introduction

Circuit breakers in the U.S. equity markets came under a great deal of scrutiny
over this past year as sharp drops in the Dow Jones Industrial Average (DJIA)
provoked intense debate over their economic usefulness. The 554 point DJIA
drop on October 27, 1997 triggered the New York Stock Exchange (NYSE) tohalt trading twice that day in accordance with NYSE Rule 80B, marking the first
official market-wide halt on the NYSE due to a decrease in stock prices. Despite
the attention focused on the market-wide trading halt, another circuit breaker,
NYSE Rule 80A, was also triggered twice that day, for the 263th and 264th timethat year alone. Since it is difficult to determine the effectiveness of a circuit
breaker on the basis of a single day’s observation, we examine the effectivenessof NYSE Rule 80A in stemming stock market volatility.
First implemented on August 1, 1990, NYSE Rule 80A was established toreduce excess market volatility by adding frictions to the linkage between the
cash and futures markets. Implicitly, it is intended to prevent “the tail from wagging the dog” by preventing index arbitrage traders from further pushing individual stock prices in either rising or declining markets. During the time period of
this study, Rule 80A goes into effect whenever the DJIA moves either up or downby 50 or more points from its previous day’s close. When in effect, Rule 80A
restricts index arbitrage traders from making destabilizing trades.1 This rule
restricts those using an index arbitrage strategy from buying NYSE stocks in arising market or selling NYSE stocks in a falling market. The rule stays in effectfor the remainder of the day unless the DJIA returns to within 25 points of theprevious day’s close, at which point Rule 80A is lifted.2
The effectiveness of circuit breakers has been frequently debated. Most studies have focused on market halts similar to the ones mandated by Rule 80B. However, since Rule 80B has only been triggered on one occasion, examining theeffectiveness of this type of circuit breaker on U.S. equity markets is difficult.
Most studies have relied upon theoretical models to examine circuit breakers,
although their conclusions differ. Greenwald and Stein (1991), for example,
develop a model that indicates that properly designed circuit breakers may help
the market achieve optimal outcomes by mitigating uncertainty via a reduction intransactional risk. In contrast, Subrahmanyam (1994) argues that the existence of
circuit breakers may have the perverse effect of increasing price volatility prior to
the triggering due to the “magnet effect,” whereby, on volatile days, tradersadvance purchases or sales of stock in anticipation of being locked out of themarket by a circuit breaker.
Others have empirically examined the effectiveness of circuit breakers on
1
Specifically, Rule 80A requires that once the DJIA has advanced (declined) by 50 points or
more, all index arbitrage orders to buy (sell) any S&P component stock must be entered as a “buyminus” (“sell plus”) order.
2
On February 11, 1999, the U.S. Securities and Exchange Commission allowed the NYSE tochange Rule 80A so that it is triggered upon a 2% change in the DJIA. Our study pre-dates this change.

Circuit breakers, volatility, and the U.S. equity markets

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other markets. For example, Lauterbach and Ben-Zion (1993) examine effects oftrading halts on the Tel Aviv Stock Exchange during the October 1987 crash,finding that the circuit breakers help reduce the next day price declines but havelittle long-term effect. In addition, Bertero and Mayer (1990) examine the effects
of market structure, including circuit breakers, on stock market performance on
23 markets around the world during the October 1987 crash.
Rule 80A itself has been examined empirically, but again, results are inconsistent, perhaps as a result of the small sample sizes of the previous studies. Santoni
and Liu (1993) find mixed results in their study of the effects of Rule 80A on
volatility over the pre-May 1991 period. After adjusting for ARCH effects in the
returns series, the authors find that unconditional variances decline on days when
Rule 80A is triggered. However, in their analysis of minute-to-minute returns, theyfind that the decline in variance does not appear to be associated with the triggeringof Rule 80A, although they had only 28 days in their sample. In a recent paper,
Overdahl and McMillan (1997) study the effect of Rule 80A on trading in the cash
and futures markets. The authors find that index-arbitrage trading volume significantly declines during the 68 observations in their sample when Rule 80A is triggered (consistent with early work on Rule 80A published by the NYSE (1991)), but
prices in the cash and futures markets nonetheless remain linked.3
Our paper differs from previous work in a number of respects. Using minuteby minute data from 1988 to 1997, we examine the effectiveness of Rule 80A in
reducing stock market volatility, using a variety of parametric and non-parametricspecifications to examine if volatility is dampened during times that NYSE Rule80A is in effect. While other papers have mostly focused on the delinking of themarkets, we focus exclusively on the issue of whether Rule 80A dampens volatility, as it is the stated reason as to why the rule is in place. We first examine theintra-day patterns of stock market volatility, and then examine the effects of Rule
80A during both up and down movements. Overall, we find that Rule 80A has asmall but statistically significant effect in reducing stock market volatility. The
effect is asymmetric, however, in that volatility is dampened more in a risingmarket than in a declining market.
The paper proceeds as follows. Section 2 presents a brief history of circuit
breakers on U.S. equity markets. After describing the data in Section 3, we present the results in Section 4. Section 5 concludes the paper.

2

Circuit breakers on U.S. equity markets

Prior to the turbulence in the stock market in October 1987, trading restrictions in
the U.S. equity markets were available for individual stocks, but not for the3
Kuserk, Locke and Sayers (1992) examine Rule 80A with respect to the pricing andmicrostructure of the S&P futures market. They examine the S&P futures market using data fromJanuary to October 1990. During most of this time, Rule 80A was only voluntary. While unable tofind any effects of Rule 80A, Kuserk, Locke and Sayers (1992) frequently note in their paper that
this may be due to the extremely small sample size.

388

Michael A. Goldstein, Joan E. Evans, and James M. Mahoneymarket as a whole. For example, when order flow for an individual stock was“one-sided”—in the sense that there were only sellers and no buyers—individual
specialists on the floor of the NYSE could delay the opening of, or suspend
trading in, individual stocks with the approval of a floor official. However, priorto October 1988, there were no coordinated, market-wide procedures in place inthe event of a massive, market-wide decline in stock prices or one-sided orderflow. The stock market crash of 1987 prompted widespread concern among
regulators, politicians and investors that the existing ad hoc trading restrictions
were not sufficient to ensure the integrity of the U.S. equity markets. Several
studies on the subject of institutional reform, most notably the Brady Report(1988), suggested the adoption of exchange-mandated and exchange-coordinatedtrading restrictions, commonly known as circuit breakers.

2.1

Chronology of circuit breakers on the NYSE

Table 1 provides an outline of the developments surrounding the adoption offormal circuit breakers on the NYSE following the crash of 1987. Rule 80A firstwent into effect in early 1988 on a voluntary basis, whereby NYSE members
were requested to refrain from using the automated trading system known as
SuperDOT on volatile days. Later in the year, Rule 80B, originally calling for a
temporary halt following a 250- or 400-point drop in the DJIA, was implemented.
Rule 80B has changed over the years and currently calls for a trading halt basedon a percentage drop in the DJIA.4
Rule 80A was officially adopted in August 1990 with two separate provisions.
The first provision entails a restriction on destabilizing index arbitrage orders
when the DJIA moves 50 points in either direction from the previous day’s close.
The second provision, more commonly referred to as the “Sidecar Rule,” restricts
program trading orders on the NYSE from being entered into the SuperDOT
system for five minutes when the nearby S&P futures contract declines 12 points
from the previous day’s settlement price. The orders are placed in a separate file(sidecar file) and released simultaneously after the five-minute window expires.
Neither the size of the point change in the DJIA or the S&P futures that triggers
Rule 80A, nor the duration of its effects, has been altered from its inception in
August 1990 to the end of our sample in December 1997.

2.2

Motivation of Rule 80A

The original SEC releases concerning Rule 80A suggested that the rule was
implemented because “program trading may create excess volatility,” and Rule
80A was designed to “minimize excess market volatility and promote stabilizationof the market” by “isolat[ing] one of the potential causes of market volatility,

4
Effective April 15, 1998, the Rule 80B trigger levels are set quarterly and calculated on thebasis of a 10, 20 and 30 percent move in the average DJIA closing level of the previous month.

Circuit breakers, volatility, and the U.S. equity markets

389

program trading.”5 In response to the 1990 NYSE request for permanent approvalof Rule 80A, the SEC stated that the NYSE thought that the rule had been “helpful in promoting market stability by minimizing excess volatility” and that the“50-point level appears to be high enough that it is not triggered too frequently,
yet low enough to act as a meaningful check on excess market volatility whichmight be associated with index arbitrage activity.”6 Consistent with these statements, an NYSE official recently stated in Congressional testimony, “The purposeof these Rule 80’A provisions is to help decrease market volatility caused by theentry of a large volume of order by professional traders without restricting the
trading of individual investors... Rule 80A’s intent from the beginning has been tominimize excess market volatility and promote stabilization of the market.”7 The
motivation behind this paper is to test whether the effects of Rule 80A on stock
market volatility are consistent with the motivation behind its initiation andcontinued presence. (Emphasis added throughout this paragraph.)

2.3

Tests of circuit breaker effects

The stock market volatility that triggers the Rule 80A circuit breaker and thestock market volatility subsequent to the triggering are observed on days that
Rule 80A is in effect. To understand the true impact of Rule 80A on volatility, wepose the question: Is volatility after the triggering of the circuit breaker different
than it would have been in the absence of the circuit breaker? To answer thatcounter-factual question, we develop models of volatility dynamics that estimate
the expected level of volatility after a movement of the magnitude that triggers
the circuit breaker. Using a single model to determine the effect of circuit breakers on volatility would be insufficient to make strong conclusions about theeffects of the circuit breakers in general, as the result may be due to a misspecified model. Our statistical tests of the effects of circuit breakers are based on avariety of parametric and non-parametric tests of volatility, in order to ensure ourresults are robust to the particular specification of volatility dynamics that isassumed.

3

Data and descriptive statistics

The data in this analysis were obtained from Bridge News Inc., DRI/McGrawHill Inc., and the New York Stock Exchange. The primary data set, obtained from
Bridge News, consists of one observation per minute for three price series: theDow Jones Industrial Average (DJIA), the Standard & Poor’s 500 (S&P 500) cash
5
SEC release No. 34-27812, File No. SR-NYSE-90-11 (March 16, 1990), 55 FR 11284 (March
27, 1990).
6
SEC release No. 34-29308, File No. SR-NYSE-91-21 (June 14, 1991), 56 FR 28428 (June 20,1991).
7
James L. Cochrane, Senior Vice President and Chief Economist of the NYSE, on “Trading
Halts and Program Trading Restrictions,” presented to the Subcommittee on Securities, Committeeon Banking, Housing and Urban Affairs, United States Senate, January 29, 1998.

390

Michael A. Goldstein, Joan E. Evans, and James M. Mahoneyindex and the S&P 500 futures index, which is traded on the Chicago Mercantile
Exchange (CME). The sample period contains all business days from March 1,
1988 through December 31, 1997. Due to the possibility of non-synchronous
price data at the opening of the NYSE, we eliminate the first fifteen observations(corresponding to the first 15 minutes of trading).8 Similarly, we eliminate the lastten minutes of trading, as the NYSE rules restrict destabilizing market-on-closeorders during this time period.9 Finally, we obtain the daily closing prices for the
nearby S&P 500 futures contract, S&P 500 cash index, and the DJIA from
DRI/McGraw-Hill.
We obtain the exact times that Rule 80A was in effect during the sample
period from the NYSE and the exact times that the sidecar rule was in effectduring the sample period from the CME. Overall, during our sample period, Rule
80A was triggered 549 times. This significant increase in observations over an
extended period provides us with a much richer data series than any of the previous studies.
For our empirical analysis, we define three indicator variables to identify eachminute when Rule 80A is in effect. The first variable, 80A_min, is defined to beequal to zero when Rule 80A is not in effect and to one when it is in effect. To
allow for asymmetric effects of Rule 80A in rising versus declining markets, wedefine two additional indicator variables. The variable 80A_up is defined as equal
to one when Rule 80A is in effect on the upside (due to an increase in the DJIA)
and zero, otherwise. Similarly, the variable 80A_down is defined as equal to one
when Rule 80A is in effect on the downside and zero, otherwise.
We derive a forward-looking return series for each of the three price series. A
minute-by-minute return series is calculated as the natural log of the subsequent
price over the current price:
Return(t) = ln ( price(t+1) / price(t)),
where price(t) represents the index or futures price at time t. A minute-by-minutevolatility series is calculated as the absolute value of the return series:10
Volatility(t) = | Return(t) |.
Summary statistics of the data are presented in Table 2. The total number of
8
The NYSE requires their specialists to open each stock within the first fifteen minutes of
trading or otherwise seek special authorization. See NYSE Rule 103A.10(B)(i).
9
The results presented in this paper are robust to alternative specifications of the business day.
In addition to the 9:45am to 3:50pm business day results reported, the tests were also performed forthe entire business day (from 9:30am to 4:00pm) and from 9:35am to 3:55pm.
10
As an alternative measure of volatility, we use, but do not report, the square of the returnseries. The results and conclusions presented below are qualitatively similar using this alternative
measure.

Circuit breakers, volatility, and the U.S. equity markets

391

days per year that Rule 80A was triggered exhibits a U-shaped pattern over the
sample period. In 1990-91, Rule 80A was triggered often (22 and 20 days, respectively), considering Rule 80A was only adopted in August 1990. In 1992-93,
Rule 80A was triggered infrequently (16 and 9 days, respectively). The rate oftriggering increased to 28 days per year in 1994 and 1995, and increased to 101
and 219 in 1996 and 1997, respectively.
This U-shaped pattern can be traced to two effects. First, the U-shape occurs
because Rule 80A is defined by a nominal, and not a percentage, move in the
DJIA. As a result, for a fixed volatility in the DJIA, Rule 80A was more likely to
be triggered in 1997 with the DJIA at 7000 than in 1990 when the DJIA was at
2700. Second, the U-shaped pattern in volatility over the period as noted in Table
2 also contributes to the U-shaped pattern in the frequency of Rule 80A occurrences. During 1990-91, the volatility of the daily returns was between 14 and 16
percent and subsequently fell to 8 percent in 1993 before rising to 16 percent in
1997.

4

Results

In this section, we present the results of our tests of the impact of NYSE Rule
80A on stock market volatility. We first present preliminary regression results
describing the basic relationship between volatility and Rule 80A. We then use
additional conditioning variables in the regression in an attempt to control for
additional cross-sectional and time-series variables which help to forecast volatility. Finally, we propose a non-parametric test comparing stock market volatility
during equally volatile periods, some with Rule 80A in effect and others without
Rule 80A in effect. While the regression-based models are dependent on theapplicability of the assumptions underlying the dynamics of the volatility process,the non-parametric test is designed to be more robust to misspecifications of thesevolatility dynamics.

4.1

Preliminary results

To examine the effects of NYSE Rule 80A on volatility, we first address a welldocumented characteristic of stock market returns known as volatility clustering.
Future volatility tends to be higher than average after periods of high volatility
and lower than average after periods of low volatility. This characterization leads
us to partition the data based on the level of the DJIA, comparing each minute’s
DJIA level to the previous day’s closing DJIA. Forty-two fractiles were constructed, each representing a five-point movement of the DJIA. Fractile 1 represents levels of the DJIA that were between the previous day’s close and five
points above the previous day’s close; fractile 2 represents DJIA levels that were
between five and ten points above the previous day’s close, and so on. Fractile 21,
which represents levels 100 points above the previous day’s close and beyond,ends the series. Similarly, on the down side, fractile 0 represents levels between

392Michael A. Goldstein, Joan E. Evans, and James M. Mahoneythe previous day’s close and five points below; fractile -1 represents levels thatwere between five and ten points below the previous day’s close, and so on.
Fractile -20, which represents drops in the DJIA of greater than 100 points, ends
the series on the down side.
Each minute’s volatility estimate, defined in Section 3, was then assigned to
its respective fractile, based on the level of the DJIA at the beginning of theminute. Figure 1 shows the distribution of the volatility estimates based on theirfractiles. It is apparent from Figure 1 that the distribution of volatility acrossfractiles follows somewhat of a parabolic “smile” pattern. Another area to note is
the slight discontinuity in the slope of the S&P future’s volatility at fractile -10,
which is around when the 80A rule becomes effective for the first time in a day.
To examine the effectiveness of Rule 80A, we regress the minute-by-minutevolatility estimates on their respective fractiles. Due to the non-linearity apparent
in Figure 1, we also include the square of the fractile as a variable. Finally, since
we are interested in the effect of Rule 80A on volatility, we include a dummy
variable indicating when the rule is in effect. Specifically, we estimate:
Volatilityt = b0 + b1 Fractilet + b2 Fractilet2 + b3 80A_mint + e,
where, at time t, Volatilityt is the absolute value of the price variation for either the
DJIA, the S&P 500 index or the S&P 500 futures, Fractilet is the fractile location
at which that price variation took place, Fractilet2 is the square of the fractile
variable, and 80A_mint is the dummy variable that is one when the rule is in effectand zero otherwise.11
We find that all three variables are highly significant at the 0.01% level (Table3). In particular, we find that the coefficient on Fractile is significant and negative,
indicating that downward movements have higher volatilities on average than
upward movements. We also find that Fractile2 is positive and significant, indicating that this variable is capturing the non-linear effects associated with increasing volatility as the DJIA moves further away from its previous day’s close.
However, we also find that the coefficient on 80A_min is negative and significant,indicating that periods when the Rule 80A was in effect had lower volatility thanwhen it was not, controlling for other factors. Therefore, we find some evidence
that NYSE Rule 80A does help reduce volatility.
We next replace the dummy variable 80A_min with separate dummy variables
for Rule 80A on the upside (80A_up) and on the downside (80A_down), running
the regression
Volatilityt = b0 + b1 Fractilet + b2 Fractilet2 + b3 80A_upt + b4 80A_downt + e.

11
We arrive at qualitatively similar conclusions if the discrete variables Fractilet and Fractilet2
are replaced by the continuous variables DayReturnt and DayReturnt2, representing the return andsquared return, respectively, from the previous day’s close to minute t.

Circuit breakers, volatility, and the U.S. equity markets

393

We find that Rule 80A has an effect that is negative and statistically significant in both directions. Interestingly, Rule 80A has a much greater impact inlowering volatility in a rising market than in a falling market, even though theunconditional volatility rises more sharply in a falling market than in a risingmarket (from Figure 1).
Finally, we perform a simple nonparametric test of the effects of Rule 80A by
comparing the volatility of returns before and after Rule 80A was officially
implemented (Figure 2 and Table 4). We observe the volatility on days where theDow moved 50 points before and after August 1990, which is the date that the
NYSE made Rule 80A binding. The volatility when the Dow is between -50 and+50 points from the previous close (i.e., when Rule 80A is generally not in effectin either time period) is remarkably similar during these two periods. However,there is a significant divergence between these data series outside of the bounds
noted above (when Rule 80A is in effect in the latter period but not in the former
period). The difference between the volatilities during the pre- and post-August
1990 periods is statistically significant for both Rule 80A and non-Rule 80Atimes. Therefore, to examine whether Rule 80A has an effect on volatility, we
compare whether the decrease in volatility during the post-August 1990 period islarger during times that Rule 80A was activated by examining the differences ofdifferences.12 The difference of differences is positive and significant, implyingthat there is a greater reduction in volatility during the post-1990 period when
Rule 80A was activated than when Rule 80A was not. The results from thisnonparametric test are consistent with the results of the parametric regression:
there is some evidence that stock market volatility when Rule 80A is in effect islower than it would have been if Rule 80A did not exist.

4.2

Additional independent variables

In order to ensure that the results from Section 4.1 are not due to misspecifications, we add additional independent variables to the regression to capture otherindependent effects on volatility that are not related to Rule 80A. First, as shownin Table 1, there was a clear pattern of volatility over the years of our sample, so
we include a series of dummy variables for each of the years, DYEAR=1988 through
DYEAR=1997, equal to one if the observation occurs within the corresponding year
and zero otherwise. Second, there is a clear day-of-the-week pattern in volatilities,
so we include a series of dummy variables, DDAY=Monday through DDAY=Friday, equal to
one if the observation occurs on the corresponding day and zero otherwise. Third,
there is a clear and well-documented pattern of intraday volatilities in equity
markets, with high volatility in the morning, decreased volatility around noon,and subsequent increased volatility as the close approaches. We address this timeof-day pattern with a series of 24 intraday dummies, from DTimeÎ[945,...,1000) through12
Using the differences in differences allows us to adjust for any difference in the base volatility
across the two time periods.

394Michael A. Goldstein, Joan E. Evans, and James M. Mahoney

DTimeÎ[1530,...,1545), equal to one if the time of the observation occurs within thecorresponding range and zero otherwise.
Table 5 shows the results of the regression
Volatilityt = b0 + b1 Fractilet + b2 Fractilet2 + b3 80A_upt + b4 80A_downt +S bj DYEAR + S bj DDAY OF THE WEEK + S bj DTIME OF THE DAY + et.
The results of this regression support the simpler specification from Section 4.1.
With these additional control variables, the regression continues to yield negativeand significant coefficients on the 80A_up and 80A_down dummy variables, suggesting that there is a small but statistically significant decrease in minute-to-minutevolatility when Rule 80A is in effect. The coefficient on Fractile remains negativeand significant and the coefficient on Fractile2 remains positive and significant. In
addition, F-tests (not reported) show that the coefficients on each set of dummy
variables (for the year, the day of the week, and the time of day) were significant in
explaining the volatility of minute-to-minute stock market returns.

4.3

Consideration of various ARCH specifications

A well-documented characterization of equity market volatility is the “ARCH
effects” in the time series of equity returns: volatility is autoregressive and conditionally heteroskedastic. This characterization implies that volatility changes in aparticular pattern, where periods of high volatility tend to be followed by additional periods of high volatility and periods of low volatility tend to followed byperiods of low volatility, and that volatility tends to revert (or autoregress) to along-run mean. One common implementation of this ARCH effect is with a
GARCH (generalized ARCH) model, where the volatility at time t, denoted as st,
develops over time according to the equation

s2t = a + b s2t-1 + c r2t ,where a, b and c are the GARCH parameters. One period’s estimated variance is aconstant plus a weighted sum of last period’s estimated variance and the mostrecent squared return. This model of variance dynamics captures a changing
volatility structure where volatility levels tend to cluster in time but also tend torevert over time to a long-run mean.
Various attempts at estimating this equation (and alternate ARCH-family
specifications) via maximum likelihood were deemed unreliable. When theequation was estimated using intraday data, the estimation procedure often did
not converge (in over 30 percent of the cases) or yielded estimates where (b+c) > 1,
which implies that volatility explodes over time instead of mean reverting (thisoccurred in about 20 percent of the cases). In the cases where the procedure
converged and yielded economically feasible parameter estimates, the variance ofCircuit breakers, volatility, and the U.S. equity markets

395

the estimates over time was so great, even on consecutive days, as to make any
inference from their use untenable.
The lack of usefulness in GARCH estimation with intraday data is consistent
with existing literature, which finds only limited applicability of GARCH estimation using intraday data. One potential source of noise that makes intraday
GARCH untenable is the bid-ask bounce, which is present but small in daily data
but is dominating in intraday data. Another problem with intraday data is theissue of how to handle the overnight change. With daily data, the time between
observations is assumed to be constant, even if some time changes include aweekend while other do not; this simplification appears to be innocuous. However,for intraday data, the change from one day’s close to the next day’s open is ordersof magnitude greater than minute-to-minute changes, and at the same time, it
seems to be too important a piece of information not to include in estimating
future volatility. However, there is no obvious way to incorporate this overnightchange into an intraday GARCH-type framework.

4.4

Non-parametric test

The final test of the effects of Rule 80A on stock market volatility takes advantage of the fact that Rule 80A has been defined as an absolute point move, and not apercentage move, from its inception in 1990 to the end of the sample in 1997. For
example, as shown in Table 2, a 50-point move in the DJIA in 1990 corresponded
to an approximate 2% move, but the same 50-point move in 1997 corresponded toan approximate 0.7% move. The idea behind this non-parametric test is to selectmatched pairs of days with equivalent percentage moves from the previous day’s
close, but where one day’s move triggered Rule 80A but the other day did not.
For example, a 1% change in the DJIA on a day in 1997 would have triggered
Rule 80A, but a 1% change in the DJIA on a day in 1990 would not have. We
attempt to control for the time-of-day and day-of-the-week effects that werealluded to earlier, by matching days in later years with days in earlier year on
three dimensions: percent change from the previous day’s close, the time of day
and the day of the week.
Table 6 shows the results of this final non-parametric test. We match 37
observations from 1997 with 37 observations from the early 1990s, where the
DJIA moved by a similar percentage (approximately one percent) at a similar
time (fifteen minute interval) on the same day of the week. In 1997, a one percentage point move triggered Rule 80A, while in the early 1990s, a one percentage point move did not. The average volatility on the nearby S&P 500 futurescontract over the next 30 minutes was then calculated for each pair of observations, and these averages were then compared. Over the 37 paired observations in
our sample, the volatility over the subsequent half-hour was significantly less in
the 1997 sample, when Rule 80A was triggered, than in the earlier sample, when
Rule 80A was not triggered. It is impossible to know whether the matching
criteria that were used to match these pairs of observations are capturing all of the396

Michael A. Goldstein, Joan E. Evans, and James M. Mahoneyimportant determinants of volatility. However, this nonparametric result does
provide additional evidence to complement the parametric results presented
earlier that Rule 80A appears to have a small but measurable impact on the volatility of stock market prices.

5

Conclusions

This paper examines the effects of NYSE Rule 80A, which went into effect inAugust 1990. Using minute-by-minute data on the DJIA cash index and the S&P
cash index and futures price, we find that although overall minute-by-minutevolatility is higher as the DJIA moves further from its previous day’s close,
NYSE Rule 80A does help reduce volatility. The decrease in volatility is found ineach of the three series examined: the S&P 500 cash index, the S&P 500 futuresindex, and the level of the Dow Jones Industrial Average.
While these results are statistically significant, we also note that they are
relatively small in magnitude. Over time, Rule 80A has been triggered more andmore frequently. Unlike Rule 80B, the price limits for Rule 80A have not been readjusted over time, although the DJIA has increased three-fold since its inception.
As it gets triggered more often, it may lose its effectiveness and significance.
Therefore, it is questionable whether Rule 80A should continue to exist in its
current form. Under the theory that there is evidence that Rule 80A reduces
volatility, one option might be to make it effective over the entire day. Alternatively, another option would entail the removal of the rule entirely, under thetheory that the reduction in volatility is insufficient to warrant the delinking of themarkets. Still another option would be to reset the 80A price limits periodically
based on the level of the DJIA, basing Rule 80A triggering levels on a percentage
DJIA move instead of a nominal DJIA move. Ultimately, it is a policy decision as
to whether Rule 80A reduces volatility sufficiently to warrant the costs associatedwith its implementation.

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Circuit breakers, volatility, and the U.S. equity markets

Figure 1: Mean volatility of returns for the S&P 500 cash index,
S&P 500 futures and the Dow Jones
Volatility (Mean Absolute Returns)

0.00090.00080.00070.00060.00050.00040.00030.00020.00010-20 -18 -16 -14 -12 -10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

20

Fractile

Mean Volatility S&P 500 Futures
Mean Volatility S&P 500 CashMean Volatility Dow Jones

Figure 1. The average minute-by-minute intraday volatility for the Dow Jones Industrial Average, the S&P 500 index and the S&P 500 futures contract from January 1990 to June 1997. Each
minute was assigned to a fractile representing a five point movement in the Dow Jones Industrial
Average. The zero fractile represents movements in the Dow Jones Industrial Average from theprevious day’s close to five points below the previous day’s close.

398

Michael A. Goldstein, Joan E. Evans, and James M. MahoneyFigure 2: Mean volatility of returns for the S&P 500 cash index
pre- and post-August 1990
Volatility (Mean Absolute Returns)

0.00080.00070.00060.00050.00040.00030.00020.00010-20 -18-16-14 -12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

20

Fractile

Mean Volatility S&P 500 Cash Pre-August 1990
Mean Volatility S&P 500 Cash Post-August 1990

Figure 2. The average minute-by-minute intraday volatility for the S&P 500 cash index in two
sub-periods: from January 1988 to August 1990 and from August 1990 to December 1997. Each
minute was assigned to a fractile representing a five point movement in the Dow Jones Industrial
Average. The zero fractile represents movements in the Dow Jones Industrial Average from theprevious day’s close to five points below the previous day’s close.

Circuit breakers, volatility, and the U.S. equity markets

399

Table 1: Chronology of circuit breaker adoptions and amendments on the
NYSE
Date

NYSE Limits

10/22/87

No official limits in period immediately following the crash of
1987. Individual stocks can have delayed openings or halts due toorder imbalances.

1/14/88

NYSE implements a 75-point collar on a voluntary basis by asking
firms not to use SuperDOT system in the event of a 75-point move.

2/4/88NYSE changes voluntary collar to 50 points.
80A.

10/20/88

NYSE institutes Rule 80B that calls for a 1 hour trading halt if the
DJIA drops 250 points or a 2 hour halt if it drops 400 points. Rule
80A modified to include a 5-minute sidecar for program trades
after 12 point S&P 500 drop. Sidecar does not apply during last
35 minutes of trading.

8/1/90NYSE officially implements Rule 80A restricting index arbitrage
orders from buying on an uptick (buy minus orders only) andselling on a downtick (sell plus orders) after 50 point move.
Unrestricted arbitrage is restored if the DJIA reverses to within 25
points of previous day’s close.

7/22/96

NYSE modifies the Rule 80B trading halt by cutting the time of thehalt in half, to one half hour for a 250 point DJIA drop, and to one
hour for a 400 point drop.

2/3/97

NYSE modifies Rule 80B again by raising the trading halt trigger
points to 350 points for a half-hour halt and to 550 for a one-hourhalt.

Submits it as Rule

400

Michael A. Goldstein, Joan E. Evans, and James M. Mahoney

Table 2: Rule 80A summary statistics
This table provides yearly statistics on Rule 80A triggerings for the period August
1990 to year-end 1997. The data are broken down by year, by the direction of the
move, and the frequency with which it was triggered more than once in a day. It
also provides yearly statistics on the level and volatility of the DJIA.

1990 1991 1992 1993 1994 1995 1996 1997
*

Total

On a down move

15

8

8

5

19

14

51

113

233

On an up move

7

12

8

4

9

14

53

125

232

Twice in a day

1

0

0

0

2

1

16

55

75

3 or more in a day

0

0

0

0

0

0

1

13

14

Total triggers/year

23

20

16

9

30

29

119

303

549

Total days

22

20

16

9

28

28

101

219

443

Standard deviation of
DJIA closing prices

16.3 14.6 10.4

8.2

11.0

8.8

11.7 16.1

12.1

Ave. DJIA closing price2679 2929 3284 3525 3794 4494 5740 7002

4049

80A move as % of
average DJIA

1.9

Rule 80A Triggered:

1.7

1.5

*Rule 80A went into effect August 1, 1990.
Source: NYSE

1.4

1.3

1.1

0.9

0.7

Circuit breakers, volatility, and the U.S. equity markets

401

Table 3: Volatility regression results
This table presents OLS regression results on intraday volatility estimates for the
Dow Jones Industrial Average (DJIA), the S&P 500 index and S&P 500 futures
based on minute by minute data for these three series from January 1990 to June
1997. The absolute value of the price variation per minute was determined foreach series. Forty-two fractiles were created representing 5 point movements inthe level of the DJIA; twenty-one above the previous day’s close, twenty below
the previous day’s close, and one representing a zero to five point decrease in the
DJIA. Each minute’s variation was then assigned to the fractile during which it
occurred. The following regression was then run:
Volatility = b0+ b1 Fractile + b2 Fractile2 + b3 80A + e
where Fractile is the appropriate fractile, Fractile2 is the square of the fractile (tocontrol for non-linearities), and 80A is a dummy variable which is one when
NYSE Rule 80A is in effect and zero otherwise. (Each coefficient and standard
error estimate has been multiplied by 106 for legibility.)*

Intercept

Fractile

Fractile2

80A

Adjusted R2

DJIA

148*
(0.21)-2.46 *
(0.04)

0.37 *(0.01)-6.36*(1.21)

0.93%

S&P500index

111*(0.17)-2.20 *
(0.04)

0.34 *(0.01)-11.53*(1.01)

0.99%

S&P500Futures240*(0.33)-6.93 *
(0.07)

0.89 *(0.01)-10.65*(1.93)

2.36%

indicates significance at the 0.01% level.

402

Michael A. Goldstein, Joan E. Evans, and James M. Mahoney

Table 4: Stock price volatility, by fractile, before and after the adoptionof Rule 80A
This table presents the volatility of stock prices (as measured by the absolutevalue of price returns) conditioned on two states. Across the columns are the preAugust 1990 (when Rule 80A was not in effect) and post-August 1980. Acrossthe rows are the periods when Rule 80A had been triggered (or would have been
triggered, for the pre-August 1990 period) and periods when Rule 80A had notbeen triggered. The differences across years are presented in the last column andthe difference of differences is presented in the last row. (t-statistics under the null
hypothesis of no differences in means are provided in parentheses.)

Pre-August 1990
Post-August 1990
Sample size
Average Sample size
Average Difference
volatility
volatility in volatility
Rule 80A on

2734.00021056

45484.00014412.00006644(8.4413)Rule 80A off

5318.00016215

69395.00014093.00002122(18.1942)

Difference ofdifferences:.00004522(42.968)

Circuit breakers, volatility, and the U.S. equity markets

403

Table 5: Volatility regression results for augmented regression
This table presents OLS regression results on intraday volatility estimates for the
Dow Jones Industrial Average (DJIA), the S&P 500 index and S&P 500 futures
based on minute by minute data for these three series from January 1990 to June
1997. The absolute value of the price variation per minute was determined foreach series. Forty-two fractiles were created representing 5 point movements inthe level of the DJIA; twenty-one above the previous day’s close, twenty below
the previous day’s close, and one representing a zero to five point decrease in the
DJIA. Each minute’s variation was then assigned to the fractile during which it
occurred. Additional dummy variables were added for the day of the week, thetime of day and the year that the observation occurred. The following regression
was then run:
Volatilityt = b0 + b1 Fractilet + b2 Fractilet2 + b3 80A_upt + b4 80A_downt +S bj DYEAR=y + S bj DDAY OF THE WEEK + S bj DTIME OF THE DAY + e,
where Fractile is the appropriate fractile, Fractile2 is the square of the fractile (tocontrol for non-linearities), 80A is a dummy variable which is one when NYSE
Rule 80A is in effect and zero otherwise, D YEAR is equal to one if the observation
occurs in year y and zero otherwise, DDAY OF THE WEEK=d is equal to one if the day of
the week equals d and zero otherwise, and DTIME OF THE DAY=f is equal to one if thetime falls into fractile f (described in the text) and zero otherwise. Coefficients forthe day of the week, the time of day and year are not reported, for space considerations, but F-tests for each set of coefficients indicate that each set is statistically significant in explaining the volatility of stock market returns.
(Each coefficient and standard error estimate has been multiplied by 106 forlegibility.)
Fractile-2.20*(0.05)

Fractile2
0.36*(0.01)

80A_down 80A_up-6.51*-23.22*(1.46)(1.48)

Adjusted R2
46.4%

S&P500 108*(0.50)
index-2.00*(0.04)

0.31*(0.01)-12.93*(1.25)-26.00*(1.28)

39.9%

S&P500 272*
Futures (0.91)-6.15*(0.09)

0.85*(0.01)-6.15*(2.47)-22.03*(1.93)

47.7%

DJIA

*

Intercept
121*(0.56)

indicates significance at the 0.01% level.

404

Michael A. Goldstein, Joan E. Evans, and James M. Mahoney

Table 6: Nonparametric test for volatility effects of Rule 80A
This table show the results of a match pair comparison of stock market volatilities
for the nearby S&P 500 futures contract. Days in the early 1990s were paired withdays in 1997, with similar percent volatilities from the previous day’s close (1%)
by 12:00 noon, and matched on other determinants of volatilities (such as the dayof the week). A one percent move in the early 1990s did not result in the triggering of Rule 80A, while a one percent move in 1997 did result in such a triggering.
We report the number of observations, the average minute-to-minute volatility inthe thirty minutes after the one-percentage point move, the difference, and the tstatistic associated with the difference.
(The average minute-to-minute volatility is multiplied by 106 for enhanced readability.)
No. ofobservations

Volatility after
1% move:
Early 1990s,
Rule 80A not
triggered

Volatility after
1% move:
1997,
Rule 80A
triggeredDifference

t-statistic

37

4.18

3.73

-0.45

-2.27

Circuit breakers, volatility, and the U.S. equity markets

405

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