CHWARTZ: Thank you, Laura. What a great way to end thisconference. I have one word to say, ‘thanks.’ Thanks to you, our panelists,our sponsors, and to all of you in the audience for being here. Now let’s gooutside to the drink tables and attack the liquidity pools (laughter).
CHAPTER 8: VOLATILITY IN U.S. ANDEUROPEAN EQUITY MARKETS: ANASSESSMENT OF MARKET QUALITY108Deniz Ozenbas, Assistant Professor of Finance, Montclair State UniversityRobert Schwartz, Speiser Professor of Finance, Zicklin School of Business, BaruchCollege, CUNY
Robert Wood, Distinguished Professor of Finance, The University of Memphis
1.
INTRODUCTION
Volatility is like cholesterol – there is both good volatility and badvolatility. Good volatility characterizes price adjustments that areattributable to news. Bad volatility characterizes price changes that areattributable to transaction costs. Bad volatility is manifest in accentuatedprice swings, the runs and reversals that occur over relatively brief tradingintervals in response to the arrival of buy and sell orders in the market. Wesuggest that an objective of market structure is to control bad volatility.However, it should also be recognized that, to some extent, short-runvolatility must be accentuated so as to appropriately compensate liquidityproviders (i.e., broker-dealer intermediaries and limit order traders).This paper examines the association between the accentuated intraday price swings and market quality.109 To this end, we empirically108
This chapter is reprinted, with permission, from D. Ozenbas, R. Schwartz, and R. Wood,“Volatility in U.S. and European Equity Markets: An Assessment of Market Quality,”International Finance, Blackwell Publishers, Volume 5 Number 3, Winter 2002, pp. 437461.
109
For simplicity this paper has not focused on the relationship between volume andvolatility.
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investigate intra-day volatility over the year 2000 for five marketcenters: the New York Stock Exchange, Nasdaq, the London StockExchange, Euronext Paris, and Deutsche Börse. Consistent withprevious research,110 we find a U-shaped intra-day volatility pattern ineach of these five markets. The result is striking because the fivemarkets represent markedly different market structures – the NYSE isa floor based, primarily order driven market; Nasdaq in 2000 (the testyear for our study) was organized primarily as a dealer market; andEuronext Paris, Deutsche Börse, and the London Stock Exchangeprimarily utilize automated (i.e., not floor-based) order-driven tradingplatforms.We pay particular attention to the sharp volatility spike that occurs in thefirst half-hour of trading in each of the five markets. A second, lesspronounced spike is also observed for the last half-hour of trading. Thesespikes are important, inverse indicators of market quality. As a furtherinverse reflection of market quality, our analysis shows that intra-dayvolatility is accentuated vis-à-vis the volatility of price changes measuredover longer (one- and two-week) intervals.Our analysis is consistent with that of Heiner (1983), who considers anenvironment where market agents have varying levels of skill with respect totheir ability to evaluate information. Heiner envisions a spectrum ofcompetence, across participants, that varies with each news event. 1 1 1 Nosingle agent has sufficient funds to drive the market to a new equilibriumlevel following any news event, and agents with less competence withrespect to a particular news event learn from the more competentparticipants who trade ahead of them. Less competent agents can introduceerrors in the price discovery process by misjudging and overshooting trueequilibrium values.112 Hence price discovery is a dynamic process.110
There is extensive empirical literature documenting intra-day patterns in equity marketvolatility, including Wood, Mclnish.and Ord (1985), Harris (1986), Lockwood and Linn(1990), Werner and Kleidon (1996) and Hong and Wang (2000).
111
The competency ranking of market agents may vary from news event to news eventdepending upon the training and experience of each market agent vis-à-vis each newsevent. This is in contrast to the stylized models of Kyle (1985), Glosten and Milgrom(1985) and others who posit two classes of traders, informed and uninformed (noise)traders.
112
Heiner further enriches the analytical framework by postulating a spectrum of complexityof news events, with the more complex events requiring a greater time to reachequilibrium. This richer analytical framework rationalizes short-term autocorrelation withmarket efficiency without consideration of market frictions. For further discussion, seeAdams, McQueen and Wood (2003).
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Our volatility analysis has implications for the price discovery functionof a market.When investors have divergent (non-homogeneous)expectations based on publicly available information, consensus(equilibrium) share values can be found only in the marketplace, as orderscome in and are translated into trades. This implies that news cannot betranslated into new consensus values until all orders based on the newinformation have arrived.However, in a non-frictionless tradingenvironment, order arrival is staggered over time and, hence, the discoveryof a new consensus price does not occur instantaneously with the advent ofnews. As we will discuss further, the process of searching for newequilibrium values after the advent of news accentuates a stock’s pricevolatility.We suggest that the volatility spike at a market’s open exists because ofthe particular difficulty, after the overnight close, of discovering openingprices that are in reasonable harmony with consensus values. Accentuatedvolatility at the close, on the other hand, reflects the difficulty of absorbingprice pressures brought about by traders attempting to complete theexecution of their orders (including the closing out of positions) before thestart of the overnight period. Accentuated volatility over the course of acomplete trading day (i.e., the volatility of open-to-close price changes)exists when momentum trading causes prices to trend away from, andeventually revert back to, equilibrium values.The relationship between short-term volatility and trading costs has beenextensively analyzed in the microstructure literature. Schwartz andWhitcomb (1977) and Lo and MacKinlay (1988) use variance analysis toestablish that short-term volatility is accentuated compared to longer-termvolatility. Hasbrouck and Schwartz (1988), Stoll (2000) and Bessembinderand Rath (2002) are some of the studies that find evidence of a link betweenaccentuated volatility and heightened transaction costs. Werner and Kleidon(1996) have analyzed intra-day volatility patterns for NYSE and Londoncross-listed stocks to assess the extent to which price discovery is integratedacross markets. In their study of the U.S. Treasury market, Fleming andRemolina (1999) have further underscored the difficulty of achievingaccurate price discovery by demonstrating that protracted surges in intra-dayvolatility attend the release of major macroeconomic news announcements.The current paper extends this literature. Our primary innovation is toassess volatility for each individual half-hour period in the trading day foreach of the five markets. That is, for the set of days in our sample period (alltrading days in 2000), we separately consider the vector of first half-hourreturns (e.g., 9:30 – 10:00 for the US markets), second-half hour returns, andso on through the last half-hour returns, and apply this methodology to two
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US and three European markets. While others have looked at some of thesemarkets individually or in pairs (e.g., Werner and Kleidon (1996) and Wood,McInish and Ord (1985)), the five-country analysis underscores theuniversality of our findings.113Our methodology contrasts with two more traditional approaches. One,which uses the series of consecutive short-period returns (e.g., Hasbrouckand Schwartz (1988) use half-hour intervals, and Andersen, Bollerslev andDas (2001) use 5-minute intervals), focuses on the effect of differencinginterval length without considering time of the day effects. The otherapproach, which uses 24-hour periods that start at different times of the day(e.g., Amihud and Mendelson (1987) and Gerety and Mulherin (1994)),captures time of the day effects without considering the effect ofdifferencing interval length. Our approach seeks to achieve both objectivessimultaneously. Regarding the time of the day effects, we pay particularattention to the opening and closing half-hour periods, because these aretimes of particular stress for the markets.We have a further reason for our methodology. When the returnsmeasurements are contiguous and the returns are negatively (positively)autocorrelated, short-period volatility is accentuated (dampened) relative tolonger period volatility.114 But, to the extent that negative and positiveautocorrelation co-exist, the offsetting correlations tend to render each otherinvisible, which undermines the efficacy of variance analysis.115 Treating
113
Werner and Kleidon use 15 minute intervals, but for a different purpose than ours. Theydo not investigate intraday volatility as an inverse measure of quality, and do not focus onthe opening and closing periods of a trading day as moments of particular stress.
114
This can be shown with the use of the following variance ratio equation (formore detail see Hasbrouck and Schwartz (1988)):
where the numerator on the left-hand side is the short-period variance times m, thedenominator on the left-hand-side is the long-period variance, and m is the ratio of thelong-period differencing interval to the short-period differencing interval. The right-handside is the inverse of unity plus two times the summation of autocorrelation factors. Theformula shows that, if the autocorrelation factors are predominantly positive (negative),short-term volatility is dampened (accentuated) relative to long-term volatility.115
Positive and negative correlation can co-exist for two reasons. First, in some periods(perhaps when there is little news), returns may be dominated by reversal behavior (e.g.,the bid-ask bounce), while in some other periods (perhaps following the advent of newinformation) trending might predominate. Second, first order correlation that is positive
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non-contiguous returns helps to solve this problem. With our methodology,a momentum move in one direction on one day, matched with amomentum move of opposite direction on another day, translates intoaccentuated volatility.116An array of factors may cause the accentuation of intra-day volatility todiffer across markets. These include the architectural structure of a market,patterns of news release (e.g., whether corporate and governmentannouncements tend to be made in the over night halt or during the tradingday), investor characteristics (e.g., whether the market is predominantlyinstitutional or retail), intra-day trading patterns of institutional investors(e.g., whether or not they tend to avoid trading at the open), the cross-listingof stocks in markets with over-lapping time zones (e.g., a British companythat is listed both on the London Stock Exchange and in the US market as anADR), and the amount of after hours and pre-open trading. Our goal in thecurrent paper is not, however, to contrast or to explain volatility differencesacross the five markets we have analyzed, but to establish that accentuatedshort-period volatility is a phenomenon that is common to all. It is also animportant manifestation of market quality that can be compared acrossequity markets.Volatility analysis has implications for market structure. By payingparticular attention to the more challenging times of the trading day (the firstand last half-hour of trading), we are able to detect imperfections in pricediscovery that may not be as apparent in the rest of the day. Our findingssuggest that price discovery at market openings and closings needs to beimproved in each of the five markets.117 This underscores the importance ofcan coexist with higher orders of correlation that are negative (e.g., when a trend isfollowed by one reversal and then a trend in the opposite direction).116
Alternatively stated, with negative first order autocorrelation alone, short-period volatilityis accentuated, and with positive first order autocorrelation alone, short-period volatility isdampened. If prices tend to run in one direction and then reverse, positive first orderautocorrelation coexists along with higher orders of correlation that are negative, and theaccentuation of short-period volatility is more effectively captured with non-contiguousreturns.
117
Price volatility at the open is accentuated by the difficulty of finding appropriate sharevalues after the overnight trading halt, while price volatility is accentuated in the last halfhour of trading because traders come under increasing pressure to close out their positionsas the overnight halt draws near. With respect to both periods, a more efficient marketmechanism would facilitate order interaction and price determination, and thus reducevolatility, at these critical times in the trading day.
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making the trading systems more efficient. Our volatility metrics may alsosharpen our ability to track changes in market quality over time.The paper is structured as follows. We first consider participant attitudestoward accentuated intra-day volatility, and next set forth our analyticframework. In the two sections that follow, we describe our data and testdesign, and report the volatility patterns that we have observed. In our finalsection, we set forth a number of currently unanswered questions.
2.
PARTICIPANT ATTITUDE TOWARDACCENTUATED INTRA-DAY VOLATILITY
To the extent that accentuated intra-day volatility reflects trading costs,one might presume that participants would find elevated volatilityundesirable. This is not necessarily the case. There is, of course, a net lossfor the market in aggregate to the extent that high costs and high volatilitydiscourage trading.118 Aside from this loss, trading is a zero sum game: whatone participant loses, another gains (i.e., one participant’s cost is another’sreturn).119Consequently, participant attitudes toward high intra-dayvolatility depends on how the individuals cope with the volatility, and onwhether or not their revenue is increased or cost increased by it.Generally, the passive side of the market – market makers and publictraders who use limit orders – benefit from accentuated volatility. A tradercan “capture” the accentuated volatility by: (1) first posting a quote orplacing a limit order, (2) next buying at a relatively low price or selling at arelatively high price, and (3) then having price revert back to a previouslevel. In fact, intra-day volatility must be accentuated in order tocompensate dealers and limit order traders for the risks they take when118
On the other hand, inefficient price discovery may generate excessive trading. Fordiscussion and further references see Barber and Odean (2000).
119
By “zero sum game,” we mean that the trading costs (e.g., bid-ask spread and marketimpact costs) paid by one participant (generally the liquidity demander) are anotherparticipant’s returns (generally the liquidity supplier). We do not mean to imply that bothparticipants in a trade cannot mutually benefit. On the contrary, it is well established ineconomics that the gains of trade involve consumer surplus on the buyer side and producersurplus on the seller side. In a security transaction between a natural buyer and a naturalseller, the surplus that each participant receives from a trade is attributable to the portfoliorebalancing that the participant wishes to achieve for longer run investment purposes.
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setting the prices at which others can trade.120 Further, in the opinion of onesell side participant, Henry Paulson Jr., chairman and chief executive ofGoldman Sachs Group, intra-day volatility also generates a need forbroker/dealer services. Paulson put it this way: “Volatility is our friend... Ifit wasn’t for volatility, why should you need Goldman Sachs?”121Technical analysis is widely used by participants on both the sell sideand the buy side. While generally looked down upon in academic circles,technical analysis, in principle at least, has a valid role to play.122 Thoughrarely stated this way, technical analysis can be thought of as an approach toinferring where a stock’s price is relative to an unobserved consensus(equilibrium) value. Any participant who, either through technical analysisor by any other means, can properly time his or her orders in a volatilemarket, will have a positive attitude toward the volatility.Not withstanding, the active side of the market – those who stepforward with market orders and trigger trades – generally pay the costimplied by the accentuated volatility. They pay it in terms of the spread andmarket impact. They pay it in the form of the risk they incur as momentumplayers. And, when a temporary buy-sell imbalance pushes price up toohigh or down too low, traders on the “heavy” side of the market (whoseorders have collectively caused the price move) bear the cost of havingchased liquidity, rather than having supplied it.High intra-day volatility is costly to the market in aggregate. Itdiscourages trades and, for all, makes portfolio returns more uncertain. Aportfolio manager who receives high scores for asset selection can see his orher returns seriously eroded by trading costs. Most PMs view trading costsas such and, with the exception primarily of some hedge funds, few count onenhancing their performance by turning trading costs into returns (except,that is, for some of the more technologically sophisticated funds who havediscovered that they can earn the spread rather than pay it). Accordingly,while they should accept some accentuation of intra-day volatility asinevitable, fund managers, by and large, find it undesirable.
120
For further discussion, see Handa and Schwartz (1996)
121
See Weinberg (2000).
122Academic studies that provide theoretical justification for the use of technical analysisinclude Blume, Easley, and O’Hara (1994), and that provide empirical support include Lo,Mamaysky, and Wang (2000).
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A Trading Desk’s View of Market Quality
ANALYTIC FRAMEWORK
We begin by reviewing the volatility relationships that are necessary forunderstanding our empirical analysis. We first consider the effect that four“market process factors” – bid-ask spreads, market impact, pricediscovery, and momentum trading – have on short-term volatility.Next, we introduce the “news factor.” We then explain why tradingcosts have a pronounced impact on short-term volatility (i.e., the volatilityof price changes measured over short intervals), while the news factor comesto dominate when price changes are measured over sufficiently longintervals.123
3.1
The Market Process Factors
Accentuated short-period volatility is attributed to four well-knownmarket process factors. We identify each in this sub-section.The Bid-Ask Spread: Even if the quotes are constant over a period oftime, transaction prices will bounce between the bid and the offer, with thestaggered arrival of market sell orders that execute at the bid, and the arrivalof market buy orders that execute at the offer. This effect is likely to beminimal with the current small tick sizes.Market Impact: A large buy order pushing the transaction price abovethe posted offer, or a large sell order pushing the transaction price below theposted bid, means that the effective bid-ask spread is wider for larger orders,particularly when impatient traders who others think could be informedundertake the buy/sell programs. Market impact accentuates intra-dayvolatility in much the same way as does the spread.Price Discovery: As noted in the introduction, the process of pricediscovery is the search for a stock’s consensus price – the value that reflectsthe broad market’s willingness to hold shares, given the new information andthe divergent expectations of investors based on the news. During thesearch process, a stock’s price will sometimes run up too far, while at othertimes it may fall too low. En route to finding its consensus value, lows and123
Note that, if news is public knowledge, prices may change without trading. For this reasonit would be preferable to base the analysis on returns for quote midpoint rather thantransactions prices. Unfortunately quote data were not available for Deutsche Börse.Thus for comparability we used transactions prices to calculate volatility for all marketsstudied.
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highs emerge that are commonly associated with support and resistancelevels.124With divergent expectations, a support level may be associated with thebearish end of the expectations spectrum, and a resistance level may beassociated with the bullish end. Bouncing between a support level and aresistance level has the same effect (although it is likely stronger) asbouncing between a bid and an offer quote. Both the bid-ask bounce and thehigh-low bounce translate into accentuated intra-day price volatility.125Momentum Trading: Protracted buying or selling pressures canoccur when large, institutional participants and/or informed traders, in anattempt to control market impact costs, break up their trading in a stealthyfashion for partial execution over a series of trades. Price discovery in anon-frictionless trading environment where participants have differentbeliefs may also result in periods of protracted buying or selling pressure.The protracted pressures lead naturally to momentum trading.When participants buy simply because the sequence of recent pricechanges has been predominantly positive, a stock’s price is apt to be pushedup too far. Similarly, when participants sell simply because the sequence ofrecent price changes has been predominantly negative, a stock’s price is aptto be pushed down too far. A stock’s price running up too high, and/ordown too low, translates into accentuated intra-day volatility.We have suggested that intra-day volatility is high because ofspreads, market impact, errors in price discovery, and momentumtrading. Bid-ask spreads and market impact are trading costs. Pricediscovery errors occur because, due to trading costs, accurate pricediscovery is not instantaneously achieved. Momentum trading occursbecause momentum players believe that current prices do not reflectnew consensus values, and they recognize that trades may signal newinformation. Thus, each of the four market factors either is a tradingcost, or exists because of trading costs. Recognizing this, we canexplicitly link trading costs and accentuated short-run volatility.124
“Support and resistance levels” is part of the terminology of technical analysis. A support(resistance) level denotes a lower (higher) reflecting barrier that price is not apt topenetrate in the absence of informational change.
125
Most important for our analysis, the level of volatility resulting from price discovery willvary across markets (and stocks) depending upon the design and regulation of a market, aswell as upon the skill of its traders.
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Because the magnitude of trading costs depends in part on marketdesign, we also have a link between intra-day volatility and marketstructure.
3.2
The News Factor
New information (news), whether stock specific, industry specific, ormarket wide, also generates price changes over the course of a trading day.126We can separate the “market process factors” we have just considered –spreads, market impact, dynamic price discovery, and momentum trading –from the “news factor” by relating volatility over relatively lengthymeasurement intervals (e.g., the volatility of weekly or bi-weekly pricechanges) to that computed for very short intervals (e.g., the volatility of halfhour price changes). As we discuss below, the market process factors, beingshort run in nature, wash out of the longer run measurements. In contrast,the news factor generates a “permanent” price shift, and thus will bereflected in volatility no matter what the length of the measurement intervalused.
3.3
The Washout
Assume an eight-hour trading day and a half-hour measurement interval.The price change over any day is the sum of sixteen price changes: one foreach of the sixteen half-hour periods that comprise the day. Alternativelystated, the price change over a day is sixteen times the average half-hourprice change. Is the variance of the one-day price change also sixteen timesthe average half-hour variance? It depends.127What would the relationship be between half-hour price volatility andthe one-day price volatility if the news factor alone causes prices to change?It is well known that share prices that reflect all information, and thattherefore change only with the arrival of news, change randomly. This isbecause any news release, until it occurs, is by definition unknowable(whether the news is bullish or bearish is a coin flip). When share prices126
By “news” we are referring to fundamental information about share value, not totrading information (e.g., quotes, transaction prices, and volume).
127
For a random walk, variance increases linearly with time and, hence, volatility (standarddeviation) for a random walk increases linearly with the square root of time. When returnsare serially correlated (as is the case with mean reversion), the linear relationship isperturbed. For further discussion and a mathematical derivation, see Hasbrouck andSchwartz (1988).
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follow a random walk, a straightforward relationship exists between halfhour volatility and one-day volatility: the variance of the log of pricechanges for a one-day interval is sixteen times the average half-hourvariances (just as the one-day price change is sixteen times the average halfhour price change).128 In other words, as Schwartz and Whitcomb (1977)have shown when prices follow a random walk, variance increasesproportionately with increases in the length of the interval over which theprice changes are measured. Equivalently, the volatility (standard deviation)increases proportionately with the square root of the measurement interval.What is the half-hour to one-day volatility relationship when the fourmarket factors we have considered are also operative? To answer this, firstrecognize a common denominator in the impacts that each of the four marketfactors have on short-run price changes: each will cause the price of a stockto bounce between a lower value at one moment, and a higher value atanother moment. That is, price either bounces between a bid and an offer, orbetween a positive market impact and a negative market impact, or betweena support level and a resistance level, or between a momentum run up and amomentum run down, or between any combination of the above.The crucial point is that, after going up, price comes down and, aftergoing down, it reverts back up. It is reversal behavior, not random walk,which characterizes short-period price movements. At times, prices alsotrend in one direction or the other, either because of the path followed whenadjusting to new information and/or because of momentum trading. Whenthere is momentum trading, the trends commonly over-shoot their mark and,when they do, the trends also end with reversals.Reversals are associated with an accentuation of the volatility of pricechanges measured over short trading intervals. Importantly, their effect isincreasingly muted as the measurement interval is lengthened. It commonlyhappens that a daily high-low range is large for a stock, but that the open-toclose price change is not.129 However, at times, over a trading day, trendscan also build that accentuate volatility, and their impact may dampen outonly when measurement intervals of longer than a day are used.And so, while change introduced by news remains, with reversals, theimpacts of the four market process factors tend to wash out over the longer128
See Schwartz and Whitcomb (1977) and Hasbrouck and Schwartz (1988). For ourempirical measure of volatility, we use the standard deviation of the log of price changes, sothe one-day volatility measure would be the square root of 16 times the half-hour volatilitymeasure.129
For further discussion, see Schwartz and Shapiro (1992).
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period. Thus, if, e.g., weekly or bi-weekly closing prices are analyzed, onemight conclude that prices are relatively stable. On the other hand, if onefocuses on the intra-day prices, one might alternatively conclude that theyare relatively volatile. The reason is that when prices do not follow a randomwalk, volatility is not cumulative the way price changes are.When intra-day reversals predominate, sixteen times the average halfhour volatility is larger than the volatility of the one-day, open-to-close pricechange. If trending predominates over the course of a day, the volatility ofopen-to-close price changes is also elevated. It is for this reason that werefer to intra-day volatility as being “accentuated.” We can capture theaccentuated intra-day volatility because the effect of the market factors thatthe volatility accentuation is attributed to tend to “wash out” when longermeasurement intervals are used. We consider this further in the next subsection.
3.4
Capturing Accentuated Volatility
Our discussion suggests two ways of capturing accentuated volatility,and we use them both in this paper.First Approach: The first approach is to assess the intra-day pattern ofvolatility using a very short measurement interval. Specifically, we examinethe volatility of half-hour price changes for each of the half-hour intervalsthat comprise the trading day, for each of the five markets that we havestudied. A sharp spike in volatility for the first half-hour interval in thetrading day would be attributed to the market process factors, not to the newsfactor.If the news factor alone causes the price changes, the intra-day volatilitypattern should reflect the intra-day pattern of news releases. If news releasesare more prevalent for some half-hour intervals than for others, the half-hourvolatility should be highest for those intervals that experience the most newsreleases.Alternatively, if news releases have no systematic intra-daypattern, then the intra-day volatility pattern, including the first half-hourinterval, should be flat.Nevertheless, would not over-night news events have relatively largeprice impacts at the open? They do, but the point is, this will not affect thefirst half-hour returns if the opening transaction prices are accuratelyestablished.130 The reason is that the first recorded price change that we130
By a price being accurately established, we mean that the price is indeed a consensusvalue, given the expectations of all participants that are based on the publicly availableinformation. However, if an overnight news event results in some participants having privateinformation, we note that a consensus value based on public information can continue to
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analyze does not include the over-night price change. Rather, it is thedifference between the last price in the interval, and the price at which themarket opened. Further, controllable information releases (e.g., corporateearnings announcements as distinct from floods and earthquakes) typicallyoc