The present paper examines two important issues related to the information content of a trade in option markets: (i) whether trade size is related to information content; and (ii) whether buy and sell transactions carry different information content. The two issues have been investigated extensively in equity markets. However, relatively few studies have been conducted on the information effects of trade size and trade direction in option markets. We examine these two issues using rich market microstructure data on the KOSPI 200 options, the single most actively traded derivative securities in the world. In the year 2006 alone, the number of contracts traded in the KOSPI 200 options was 2.41 billion. In the same year, the aggregate volume of the rest of the top-10 derivatives products in the world totaled 2.40 billion contracts.
In the framework of standard asset pricing theories like the capital asset pricing model, trade size per se is irrelevant in determining security prices. Every market participant is a price taker and has the same access to information. However, if some investors are more informed than others, the uninformed will face the problem of adverse selection in trading (Bagehot, 1971; Glosten & Milgrom, 1985). The price effects of asymmetric information are theoretically modeled in several key papers, including Kyle (1985) and Easley & O’Hara (1987).
One common thread in the models of Kyle (1985) and Easley & O’Hara (1987) is that trade size affects the information content of a trade. In Easley & O’Hara (1987), informed investors prefer to trade in large volumes to maximize their trading profits. Hence, a large order is more likely to be information driven than a small order. In Kyle’s (1985) framework, however, informed investors strategically spread their trades over time to hide their intentions. Although Kyle’s paper itself does not provide a definite prediction about the relationship between order size and information, one can easily infer that an informed investor will strategically break up a suboptimally large order into a few smaller ones and spread them over time.
Empirical evidence on the link between trade size and information is abundant. From earlier papers such as that of Holthausen et al. (1987) to more recent ones such as those of Barclay & Warner (1993), Easley et al. (1997), Dufour & Engle (2000), and Chakravarty (2001), there is plentiful empirical evidence that trade size is significantly related to information content. However, these studies offer two completely different pictures with respect to how trade size relates to information content. According to Holthausen et al. (1987), Easley et al. (1997), and Dufour & Engle (2000), the larger a trade, the more likely the trade is initiated by an informed trader. However, Barclay & Warner (1993) present evidence that trades in the medium-sized category have the most price impact. They explain the result using the so-called “stealth trading hypothesis”, which holds that trading in medium-sized orders is optimal for informed investors for camouflaging their intent and, therefore, minimizing adverse price movements. Chakravarty (2001) reinforces the stealth trading hypothesis by finding evidence that orders from institutions cause the large price contribution of medium-sized trades.
The controversial findings presented above suggest that more research is needed to improve our understanding of the link between trade size and information content. Furthermore, most existing studies that focus on this link look at equity markets only. Little is known about this issue in option markets. The only study so far that examines the direct link between the two variables is Anand & Chakravarty (2007) on equity options markets. No study has investigated how informed investors strategically choose their trade size in index option markets. To our knowledge, this is the first study that examines the link between information and trade size in an index option market.
We study the index option market because of the importance that index products have in derivatives trading. Index derivatives are usually the most actively traded products in any derivatives market. Moreover, in emerging markets, often index derivatives form the only practical conduit of derivative-based arbitrages or hedging. In many of these markets, individual equity options do not exist, or, if they do exist, trading is extremely infrequent.1
In markets that are semi-strong form efficient, the source of informed trading is private, firm-specific information. In this line of logic, one needs to look at individual equity option data to find traces of informed trading in option markets.2 Unlike in equity option markets, however, price changes in index option markets are driven by public information about macroeconomic factors, not by private information. Hence, one might argue that information asymmetry does not play a role in index option trading. Nevertheless, recent studies indicate that informed trading is also active in markets for index derivatives (Schlag & Stoll, 2005; Erenburg et al., 2006; Ahn et al., 2008; Kang & Park, 2008). Possible explanations for the information advantages of certain investors in index derivatives markets include superior skills of processing macroeconomic information, faster access to the market, and/or superior trading abilities.
The next issue that we examine in the present paper is whether buy/sell trade directions have different information effects in index option trading. Several published studies report the evidence of systematic buy/sell asymmetry in information effects in equity markets (Kraus & Stoll, 1972; Miller, 1977; Figlewski, 1981; Figlewski & Webb, 1993; Chan & Lakonishok, 1993, 1995; Ahn et al., 2005). Little is known about whether similar asymmetry exists in option markets, where the trading mechanism is markedly different from that of equity markets.
In a complete market, as with the case of trade size, there is no reason why a buyer-initiated trade should contain more information than a seller-initiated trade, and vice versa in a systematic way. However, prior studies find that various factors related to incompleteness in equity markets could create a systematic tendency of stock purchases carrying more information than sales. One such factor is short sale restrictions. Short sale restrictions shut some pessimists out of the market, causing stock prices to under-represent negative information (Miller, 1977; Figlewski, 1981; Figlewski & Webb, 1993; Ahn et al., 2005). Another example is the stock selection feature of portfolio managers reported by Chan & Lakonishok (1993, 1995). According to Chan and Lakonishok, institutional purchases are likely to convey more information than institutional sales. Their logic is that a portfolio manager’s choice of a particular issue to sell out of a limited set of securities in a portfolio is not necessarily information motivated. On the contrary, the choice to buy a specific issue out of the numerous alternatives in the market is likely to carry favorable information about the issue.
The abovementioned frictions, of course, are not applicable to index option markets. The asymmetric information effect created by portfolio managers’ purchases and sales would not apply to index option trading because in index option markets investors trade a single asset, an index option. The asymmetry induced by short sale restrictions is not applicable, either, because option traders do not need short selling. Instead, they can simply buy put options.
However, buy/sell information asymmetry could still exist in option markets, albeit for different reasons. In option markets, investors can always choose between call and put options, and this could create asymmetric information effects in the buy/sell order flow. Suppose an investor in an option market has a negative signal on the future value of an asset. The investor can either sell a call naked or buy a put option.3 Of the two choices, the latter will be preferred for the following reasons. When the investor sells a naked call, the maximum future loss is unbounded, while the maximum gain is only the option premium. However, if the investor buys a put, the potential gain could be substantial, with the maximum loss limited just at the option premium. A naked sale of a call also adds the non-trivial burden of maintaining margin requirements, which could often be substantial. A similar reasoning applies to the case of an investor with positive information: buying a call option will be a superior strategy to selling a put option. These considerations suggest a systematic pattern in option markets that buy transactions carry more information than sell transactions.
As an extension of the buy/sell asymmetry-related issue, we examine whether the asymmetry between buy and sell transactions is linked to investor type. It is a street lore that institutions are more informed than individuals. Chakravarty (2001) and Barber et al. (2009) find evidence that supports this claim. There is also a growing but controversial literature on whether domestic investors have information advantages over foreigners or vice versa. Choe et al. (2005), Hau (2001), and Dvorak (2005) report evidence that foreigners underperform domestic investors. On the contrary, Grinblatt & Keloharju (2000), Froot et al. (2001), and Froot & Ramadorai (2008) find that foreign investors are more informed than their domestic counterpart. There is also a group of studies that report foreign investors’ information advantages in Korea equity markets.4 As long as the buy/sell asymmetry in option markets is caused by informed trading and a certain type of investor is more accountable for informed trading than others, we should see a systematic link between the asymmetry and investor type. In our analysis, to answer this question, we differentiate the orders of institutional investors from the orders of individuals and also the orders of foreign institutions from those of domestic institutions.
We use two structural models that extend the spread decomposition model proposed by Madhavan et al. (1997) (the MRR model). In the MRR model, information asymmetry is captured by estimated adverse selection costs, which we use as a proxy for the information content of a trade. Our first model is the size-dependent model (SDM). We use this model in our analysis of the information effects of trade size. An innovation of the SDM is that the model conveys the idea that trade size can affect adverse selection costs. More specifically, the model estimates the extra price impact of the volume of a trade separately from the impact of the direction of the trade. For our analysis of buy/sell asymmetry, we introduce the dummy variable model (DVM). The DVM separately estimates the information contents of buyer- and seller-initiated trades using a dummy variable. The model is also used when we examine the effects of investor type on buy/sell asymmetry.
The index option data set we use in the present study is extremely rich in terms of its coverage of details. The data set contains an information set that is usually unavailable in the data from other markets: for example, it contains information on investor type (in total, 17 types). The trader type information is essential in our analysis that examines the effects of institutional trading.
The rest of the paper is organized as follows. The next section describes the KOSPI 200 options market and explains the data set. Section 3 proposes the two structural models and the motivation for using these models. Section 4 discusses the main empirical findings. Section 5 concludes the paper.
2.1. KOSPI 200 Options Market
The KOSPI 200 options are based on the KOSPI 200 index, which is composed of the 200 most representative stocks of the Korea Exchange (KRX). The options are by far the most actively traded derivatives in the world. Table 1 presents the trading volume in millions of contracts for the top-10 derivatives in the world between 2001 and 2006. The table clearly shows that the KOSPI 200 options have maintained a dominant number one spot during the entire 6-year span. In 2006 alone, its trading volume was more than 2.41 billion contracts, which is greater than the cumulative volume from the rest of the top-10 derivatives of the world (2.40 billion contracts).5
Table 1. The world’s top-10 derivative contracts
The world’s 10 most active derivative contracts, measured in millions of contracts from the year 2001 to the year 2006, are shown. The rank is determined based on the trading volume of the year 2005 because the Tasa de Interés Interbancaria de Equilibrio (TIIE) 28-Day Interbank Rate Futures, which is ranked fifth in 2006, is ranked below 20th before 2002.
| 1||KOSPI 200 options, KRX||823.3||1889.8||2837.7||2521.6||2535.2||2414.4|
| 2||Eurodollar Futures, CME||184.0||202.1||208.8||297.6||410.4||502.1|
| 3||Euro-Bund Futures, Eurex||178.0||191.3||244.4||239.8||299.3||319.9|
| 4||10-year T-Note Futures, CBOT||57.6||95.8||146.5||196.1||215.1||255.6|
| 5||E-mini S&P500 Index Futures, CBOT||39.4||115.7||161.2||167.2||207.1||257.9|
| 6||Eurodollar Options, CME||88.2||105.6||100.8||130.6||188.0||269.0|
| 7||Euribor Futures, Euronext.liffe||91.1||105.8||137.7||157.8||166.7||202.1|
| 8||Euro-Bobl Futures, Eurex||99.6||114.7||150.1||159.2||158.3||167.3|
| 9||Euro-Schatz Futures, Eurex||92.6||108.8||117.4||122.9||141.2||165.3|
|10||DJ Euro Stoxx 50 Futures, Eurex||37.8||86.4||116.0||121.7||140.0||213.5|
In addition to the extreme abundance of liquidity, the KOSPI 200 options market has another unique characteristic: a high participation rate of individual investors. In most of the derivative markets in developed countries, institutional investors form the majority of the market participants. In the KOSPI 200 options market, however, individuals are the major participants. For example, in our sample year, 2002, individual investors account for 66% of the total trading volume, which almost doubles the combined trading volume by all institutions.
2.2. Microstructure of the KOSPI 200 Options Market
KOSPI 200 options are European options. The options have a contract size of KRW100 000 times the level of the KOSPI 200 index price. Four different option series with varying maturities are traded in the options market. The maturity months are three consecutive near-term months and one nearest month from the quarterly cycle of March, June, September, or December. Nine different exercise prices are set for the three consecutive near-term contracts and five different exercise prices for the next quarterly month contract. The options use two different tick sizes. If the quotation price is three points or higher, the tick size is 0.05 points (i.e. KRW5000); otherwise, the tick size is 0.01 points (KRW1000).
The market is purely order driven. All limit orders are consolidated into the central electronic limit order book, where they are crossed with the incoming market orders.6 The market adopts two trading systems: call and continuous auctions. Before the market opens, a pre-opening call auction, which sets the initial price of the day at 09:00 hours, takes place. A regular continuous double auction follows it. The continuous auction lasts until 15:05 hours, at which point once again the market switches to a call auction before it closes at 15:15 hours.
2.3. Data and the Sample Period
We use the historical trade and quote data of the KOSPI 200 options provided by the KRX. The data set is unique in that it provides a rich set of information about each order and trade. This includes not only the usual information, such as the transaction price, trade size, and time of an each order, but also detailed information about the type of investor who submits the order. The investor type flag in the data set is given in 11 domestic and six foreign investor groups. We can also make exact identifications of the trade directions (whether a trade is initiated by a buyer or a seller), by keeping track of the fine time stamp provided by the data, which records every trade and quote at 100th of a second.
The sample period of this study is from 1 January to 31 December 2002. We choose this specific period because it does not contain any persistent upward or downward trends. Chiyachantana et al. (2004) demonstrate that market-wide bearishness or bullishness is an important factor to control for in research that examines buy/sell asymmetry in transaction costs. As one of the two main issues we examine in the present study is asymmetric information effects between buy and sell trades, we need to pick a market period that is neutral enough, belonging neither to a persistent bull market nor to a prolonged bear market. The market during the years prior to 2001 was severely bearish, whereas the period subsequent to 2002 is characterized by sturdy bullishness that continued until 2007. The markets in the years of 2001 and 2002 just fit the aforementioned specification. We choose 2002 because it is more recent.7 Another advantage of the 2002 data is that no trading halt occurs during the year. Trading halts are common in the other years.
We include only the nearest and second-nearest maturity options in our analysis because the other maturity options are barely traded. We use all trades and quotes recorded during the continuous trading session of every trading day, from 09:00 to 15:05 hours. This procedure results in 15.6 million transactions of call options and 12.8 million transactions of put options. In terms of trading volume, call options account for approximately one billion contracts and put options account for nearly 800 million contracts.
2.4. Option Delta
In our analysis, we separately examine call and put options. We further classify options by their delta values. This is somewhat in contrast to many existing studies on options, which use option moneyness for classification. Traditionally, moneyness is defined as the underlying asset’s price over the strike price. Option delta is a more comprehensive measure than this traditional metric in that it incorporates such information as the time to the maturity of the option and the volatility of the underlying asset value.8 This point is well demonstrated by Bollen & Whaley (2004).9
The use of option delta is justified in the following illustration. In the KOSPI 200 options market, the longest time to maturity of a nearest month option is usually fewer than 30 calendar days. This short maturity makes the traditional definition of option moneyness, such as the underlying price over the strike price (S/K), less meaningful for investors. For example, suppose there are two call option series with the same strike price at 100. Furthermore, suppose that one has 30 calendar days left until maturity, while the other has three calendar days left. Now assume that the level of the current underlying index is 95. Whereas both options have the same moneyness at 0.95, the 3-day option will trade out of the money and, conditional on the movement of the underlying market, the 30-day option could trade in the money because of its speculative value. Hence, using option moneyness for a grouping purpose is problematic in this case, leading to a potential error of treating two distinctively different options as identical. Of course, using option delta will alleviate such a problem.
The option delta (Δ) is calculated as follows:
N(·) is the standard normal cumulative density function. St denotes the value of the KOSPI 200 index at time t. D stands for the present value of the dividends paid from time t until the expiration date, T. K is the option’s strike price. The term r is the continuously compounded risk-free interest rate, which is measured by the rates from 91-day Certificate of Deposit (CD). τ1 (τ2) is the ratio of the trading time in the number of minutes (in the number of calendar days) remaining until the option maturity to the total length of the trading time in the number of minutes (the number of calendar days) in a year. σ is the implied volatility of the underlying asset, as estimated from the midpoint of the bid and ask prices of the at-the-money option at time t.
2.5. Data Processing
We process our sample data in the following steps. Throughout the entire process, we handle put and call options separately. First, we partition each trading session into 15-min intraday intervals. Then, for each option series, we estimate the option delta for each 15-min interval. Then we sort the intervals by the respective delta estimates. Next, delta ranges are set using a grid of 0.05 (for put options, the absolute values of the delta estimates are used). The lowest range has delta values from 0 to 0.05. The highest range has delta values of 0.95 or greater. We then merge all 15-min interval data if their delta estimates belong to the same range. We discard the first two groups whose delta values are below 0.1 because of their extremely low prices, which are only a few times larger than the tick size. We also exclude the last two groups (delta values greater than 0.9) because trading in these ranges is extremely infrequent.
Table 2 presents the descriptive statistics of the sample by different delta ranges. We report the results on call (Panel A) and put options (Panel B) separately. The table shows that trading activity decreases monotonically with (absolute) delta values regardless of whether it is measured by the number of transactions, volume in number of contracts, or volume in monetary value. The pattern is also clearly present when we look at buys and sells separately. Liquidity is most plentiful with low delta options. It gradually decreases as the delta value increases. The contract-weighted average transaction prices increase with delta values because the greater the delta of an option, the deeper in the money the option goes.
Table 2. Descriptive statistics
The descriptive statistics of the trading activity for the KOSPI 200 options throughout 16 groups classified by the option delta for 2002 are shown. Panel A shows the results for call options and Panel B shows the results for put options. The “Total” column shows the results for total value in each delta interval. The “Buy (Sell)” column shows results for the buyer (seller)-initiated trades. Presented are the number of transactions (in thousands), trading volume in thousand contracts, trading value in billion Korean Won (KRW), contract-weighted average price in points (one point is equivalent to 100 000 KRW), average trade size in contract, and average trade value in points. The last row (“All”) shows the statistics for the whole sample data.
|Panel A: Descriptive statistics for call options|
|0.10–0.15||2003||899||1104||176 893||86 278||90 615||7758||3828||3931||0.439||0.444||0.434||88.3||96.0||82.1||38.74||42.60||35.60|
|0.15–0.20||2004||923||1081||155 876||76 065||79 811||9825||4840||4984||0.63||0.636||0.625||77.8||82.4||73.8||49.01||52.43||46.10|
|0.20–0.25||1824||838||986||116 728||56 571||60 157||9309||4549||4760||0.798||0.804||0.791||64.0||67.5||61.0||51.05||54.29||48.29|
|0.25–0.30||1516||707||809||74 954||36 681||38 274||7144||3522||3622||0.953||0.96||0.946||49.4||51.9||47.3||47.12||49.81||44.77|
|0.30–0.35||1316||619||697||53 621||26 182||27 439||6259||3088||3171||1.167||1.179||1.156||40.7||42.3||39.4||47.57||49.88||45.51|
|0.35–0.40||979||462||517||35 726||17 399||18 328||4463||2187||2276||1.249||1.257||1.242||36.5||37.7||35.4||45.59||47.36||44.02|
|0.40–0.45||906||426||479||28 766||14 044||14 722||4142||2041||2101||1.44||1.453||1.427||31.8||33.0||30.7||45.74||47.89||43.83|
|All||12 831||5943||6888||702 754||342 907||359 848||61 638||30 450||31 188||0.877||0.888||0.867||54.8||57.7||52.2||48.04||51.23||45.28|
|Panel B: Descriptive statistics for put options|
|0.10–0.15||1750||797||953||14 0524||68 622||71 901||6537||3236||3302||0.465||0.471||0.459||80.3||86.1||75.5||37.36||40.59||34.65|
|0.15–0.20||1715||798||917||118 466||57 904||60 562||7738||3815||3923||0.653||0.659||0.648||69.1||72.6||66.1||45.13||47.81||42.80|
|0.20–0.25||1423||661||762||78 541||38 158||40 384||6457||3159||3298||0.822||0.828||0.817||55.2||57.7||53.0||45.36||47.78||43.27|
|0.25–0.30||1216||565||651||53 647||26 019||27 627||5265||2568||2698||0.981||0.987||0.976||44.1||46.0||42.4||43.29||45.44||41.41|
|0.30–0.35||954||442||512||36 509||17 539||18 970||4247||2049||2198||1.163||1.168||1.159||38.3||39.7||37.1||44.53||46.33||42.97|
|0.35–0.40||788||364||425||25 446||12 318||13 128||3526||1703||1823||1.386||1.382||1.389||32.3||33.9||30.9||44.72||46.82||42.92|
|All||10 168||4705||5464||517 334||252 046||265 288||46 684||22 969||23 714||0.902||0.911||0.894||50.9||53.6||48.6||45.91||48.82||43.40|
3. Structural Models
3.1. Size-Dependent Model
We propose a structural model that accounts for trade size information when inferring the information content of a trade. There are two distinctively different opinions on how trade size relates to information content. Easley & O’Hara (1987) demonstrate that informed investors prefer to trade in large volume at any given price. The implication is that, in general, large trades convey more information than small trades. Lin et al. (1995) and Easley et al. (1997) find empirical evidence consistent with the prediction. On the contrary, Barclay & Warner (1993) propose that informed traders strategically split their large orders into smaller ones to camouflage their identity. According to this “stealth trading hypothesis,” it is medium-sized trades, not large trades, that have the largest price impact. Chakravarty (2001) also reports findings that confirm the hypothesis.
The original MRR model assumes a fixed order size. To accommodate the information effect of trade size, we modify the MRR approach by incorporating trade size as an independent variable in the estimation model. This model is the SDM. There are some advantages of adopting the structural model. First, we can examine the effect of trade size on the information content of a trade free from the arbitrariness of the discrete trade-size classification rules used by most existing studies. These rules usually use a grid in classifying trade size. For example, Huang & Stoll (1997) use a classification rule in their spread component estimation, which categorizes a trade as a small trade if its volume is fewer than 1000 shares, as a medium-sized trade if the size is between 1000 and 10 000 shares, and as a large trade if the size is greater than 10 000 shares. In our model, the volume of each trade itself is used as a direct input in the system. Second, the SDM also allows us to infer the effect of trade size on the temporary spread component or order-processing costs, another important transaction cost-related consideration for investors.
Now we describe the model. As in the MRR model, the post-trade value of an asset, μt, is defined as:
In the above specification, xt and xt−1 are buy/sell indicators or trade initiation variables, taking the value of +1 for a buyer-initiated trade and −1 for a seller-initiated trade. The parameter α0 reflects the part of asymmetric information costs that are attributable to the trade direction itself. is the square root of the volume of the trade at time t, measured in the number of contracts. The square root of volume instead of raw volume is used to accommodate the concave nature of the volume effect. α1 is the parameter in which we are interested and captures the trade size-related component of asymmetric information costs. The combined term represents the permanent price effect of a trade made at time t. is a surprise in the order flow. can be interpreted as a change in investors’ belief about the asset value, reflecting the trade size in the order flow. ε is an independently and identically distributed (i.i.d.) error term with a zero mean that captures the innovation in public beliefs. The conditional expectation of xt given xt−1 is defined similar to the original MRR model:
where ρ is the first-order autocorrelation in the order flow.
Meanwhile, the observed transaction price at time t, Pt, is defined as
where β0 measures the part of order-processing costs attributable to trade direction, while β1 captures order-processing costs that are related to trade volume. represents the temporary price effect of a trade made at time t. ξ is a rounding error due to price discreteness.
From Equations (2)–(4), we can express the price change at time t as
We set up generalized method of moments (GMM) moment conditions and estimate the five parameters, α0, α1, β0, β1, and ρ:10
We perform separate estimations for each of the 12 months in the sample year, as well as for the 16 different delta ranges. We use Hansen’s J-test to determine model fitness. All optimized function values are negligibly small and P-values are close to one. The coefficient that we are most interested in is α1, the sensitivity of adverse selection costs to trade size. If trade size is an important factor for informed trading, α1 should be positive and significant. In the meantime, a positive β1 implies that order-processing costs increase with trade size. A negative β1, by contrast, is consistent with economies of scale for order-processing costs.
The SDM is similar to the model proposed by Angelidis & Benos (2009) in their analysis on the market microstructure of the Athens stock market. They also extend the MRR model by including trade size as an independent variable. However, our model is slightly different from theirs in the following manner. Angelidis and Benos (2009) assume that the permanent spread component is proportional to the square root of trade size, while the temporary spread component has two parts: one that is dependent upon and the other that is independent of trade size. We allow both permanent and temporary components to be dependent upon as well as independent of trade size.
3.2. Dummy Variable Model
We describe the model used to analyze the asymmetric information effects between buy and sell trades. The model is also an extension of the MRR model. We call the structural model the dummy variable model (DVM) because it captures both permanent and temporary components of the spread, separately for buys and sells, using a dummy variable. Specifically, the DVM is the same as the original MRR model except that it contains a dummy variable on two OLS normal equations to separate the price effects of buyer- and seller-initiated trades. The moment conditions for the GMM estimation are set as
In the above moment conditions, Dt denotes a dummy variable that indicates the initiating trade type. For each trade type (buy or sell), we separately estimate the three parameters α, β, and ρ using the GMM technique.11 For example, when we estimate adverse selection costs incurred by buys, if a specific trade at time t is buyer initiated, Dt takes the value of 1 and 0 otherwise. Similarly, for adverse selection costs incurred by sells, Dt becomes 1 if the trade at time t is seller initiated and 0 otherwise.
We apply a similar dummy variable technique to estimate the model parameters for our analysis of the information effects of buys and sells for different types of investors. In our data set, investors are classified into 17 types. We regroup these 17 types into three broad groups: domestic individuals, domestic institutions, and foreign institutions.
We examine two important issues related to the information content of a trade in option markets: (i) whether trade size is related to the information content of a trade; and (ii) whether buy and sell transactions carry different information content. Our analysis is based on a rich set of market microstructure data on the KOSPI 200 options, the single most actively traded derivative securities in the world.
We use two structural models that extend the MRR model (1997): the SDM and the DVM. The SDM captures the idea that trade size can affect adverse selection costs. The DVM separately estimates the information contents for buyer- and seller-initiated trades using a dummy variable.
From the analysis using the SDM, we find that trade size is significantly related to the information content of a trade. The estimated parameter for the impact of trade size on the permanent price impact is positive and significant. The pattern is persistent across different parts of the year and across different delta ranges. This finding reinforces the earlier empirical evidence on the relation between trade size and information that is reported in the literature (Easley & O’Hara, 1987; Lin et al., 1995; Easley et al., 1997; Dufour & Engle, 2000).
The DVM analysis on buy/sell information asymmetry shows that there is a clear pattern of asymmetry in the information asymmetry costs between buyer- and seller-initiated transactions. Further analysis reveals that the trades initiated by institutions lead to greater information effects than the trades by individuals, and that the buy/sell information asymmetry is driven mostly by institutional trading. The investor group that is associated with the largest adverse selection costs and contributes most to the buy/sell asymmetry is foreign institutions.
The empirical results from the DVM model generally agree with the finding that individuals are not as sophisticated as institutions (Barber & Odean, 2000; Grinblatt & Keloharju, 2000; Barber et al., 2009). Furthermore, the results are also consistent with evidence showing that foreigners have informational advantages (e.g. Grinblatt & Keloharju, 2000; Froot & Ramadorai, 2008).