KOSDAQ 150 Night Futures

A real-time chart of KOSDAQ 150 futures trades in the night session, from 18:00 to 06:00 KST the next morning, stacked into 1-minute bars. The reference line is the previous regular-session close, and the figures are futures prices, not the index.

Night futures are index futures traded in the extended session that runs from 18:00 to 06:00 KST once the regular session has closed, and their underlyings are KOSPI 200, built from large caps, and KOSDAQ 150, weighted toward growth technology names. Both contracts are listed on a quarterly cycle, trade for the last time on the second Thursday of the expiry month and trading migrates to the following quarter as expiry approaches, so what the screen draws is always the nearest contract. The regular session opens at 09:00 and closes at 15:45, leaving a little over two hours of dead time between the two sessions. When the Korea Exchange brought the night derivatives market onto its own platform the range of eligible products widened and night trading in KOSDAQ 150 began at that point, so the samples the two contracts have accumulated differ greatly in length.

The case for an extended session rests on the fact that price-relevant information does not arrive only during domestic trading hours. Eun and Shim (1989) found the flow of innovations from the United States market to others dominant over the reverse direction. That volatility as well as returns crosses borders was established for New York and Tokyo by Hamao, Masulis, and Ng (1990). Lin, Engle, and Ito (1994) set out the structure in which one market's day meshes with another's night, and Mun and Hong (2003), on spillover from NASDAQ 100 into KOSDAQ 50 index futures, is an early case confirming that the same route runs into Korea. Where trading hours are confined to the day this information waits for the open, and the extended session is the device that shortens the wait. That frictions in the regular session changed once night trading began was measured on KOSPI 200 futures by Seon and Joo (2019).

A futures contract's fair value follows from the cost-of-carry relation (Cornell and French 1983).

Here $S_{t}$ is the level of the underlying index at the previous regular close, $r_{t}$ the risk-free rate, $d_{t}$ the expected dividend yield over the remaining life and $\tau_{t}$ the time to expiry measured in years. How far an observed price stands from that value is the fair-value deviation.

Here $F_{t}$ is the futures price observed in the night session, and the gap $b_{t}$ carries transaction costs, short-sale constraints, uncertainty about dividends and the net demand of hedgers and speculators all at once. At night the cash index is frozen at the regular close and $S_{t}$ does not update, so price movement in these hours is for the most part a revision of expectations about the next open. The finding of Nam et al. (2006) that futures impound information ahead of the cash market was obtained in the regular session, but at night, when the cash market does not trade, the futures price is the only market price attached to the value of the underlying.

The night book is thinner than the regular session's. The combination Barclay and Hendershott (2003) report for after-hours trading, low volume and wide spreads alongside a larger share of the price move carried by each trade, was reported for the United States after-hours market, and in such a market it separates the levels at which a bar can be read. At tick level it is easy to misread a wick produced by a handful of trades as information, whereas the move across a whole session is closer to a summary of the news that arrived that night. That the price impact of a trade varies with the hour of the day is something Webb et al. (2016) had already shown for the regular session, and as the hour changes a trade of the same size leaves a different mark. Lee and Woo (2013) addressed price-discovery efficiency in the night session head on. There is no reason to assume that the night's mix of participants matches the day's, but none of the studies cited here measures that mix contract by contract. Kang (2021) traced how cross-product trading settled in as the KOSDAQ 150 futures market grew.

The night session is taken as the twelve-hour window that opens at 18:00 and closes at 06:00 KST the next day. Each session is identified by its starting date, that is, by the date of the evening, so a session beginning on a Wednesday evening and ending on a Thursday morning carries the Wednesday date. Sessions run on evenings from Monday through Friday other than market holidays, and one opened on a Friday evening closes on the Saturday morning. Anchoring the label to the starting date keeps a single night from being cut at midnight, and it divides time at the same place as the partition of Lin, Engle, and Ito (1994), which pairs one market's day with another's night.

Trades are assigned to buckets obtained by flooring their second-level timestamps to the minute, and every bucket yields four prices and a volume.

Here $p_{i}$ and $v_{i}$ are the price and quantity of an individual trade in that bucket and $k$ is the bucket's index. The open and the close are the prices of the earliest and the latest trade inside the bucket, and the high and the low are the largest and the smallest within it. A minute holding no trade at all produces no bar. Filling an empty minute with the previous value erases the fact that nothing traded, and the observation of Barclay and Hendershott (2003) that volume and per-trade price contribution move in opposite directions in the after-hours market makes that fact data one cannot discard. The stealth-trading argument of Barclay and Warner (1993), in which the share of cumulative price change depends on trade size, adds a further reason not to synthesize trades, since an interpolated bar is not a bar of zero volume but a trade that never happened.

Higher timeframes are not rebuilt on the server but computed on the screen by grouping the 1-minute bars. Group boundaries are cut without overlap from the session's opening time, and consecutive 1-minute bars are combined as follows.

Here $n$ is the number of minutes grouped, and the chart offers 5-minute, 10-minute, 15-minute and 30-minute groupings, while $B$ is the set of 1-minute bars in the group, $k$ the earliest bar in it and $k'$ the latest. Empty minutes inside a group are still not filled, so the volume of a coarse bar is the sum of quantities actually traded, and the coarser the grouping the more the small movements disappear and only the larger sweep remains. Given the result of Webb et al. (2016) that the price impact of a trade is not constant through the day, a comparison across bar sizes holds only when bars from the same hours are set against each other.

Movement inside a session is measured against the close of the previous regular session.

Here $C_{t}$ is the most recent traded price and $P_{\mathrm{prev}}$ the close of the previous regular session, and the value is shown in percent. The reference sits at that close because the cost-of-carry relation makes the cash level at that same moment the starting point of fair value (Cornell and French 1983), and measured that way the night's movement can be set on the same axis as the daily change quoted during the day.

The quantities built this way lead straight into the measures the literature uses. Treating the stretch from the close to the next open as a single return is the partition that separates overnight from intraday returns, and both the result of Bondarenko and Muravyev (2023) that most of the average daily return in S&P 500 futures falls inside a narrow band outside regular trading hours and the drift that Boyarchenko, Larsen, and Whelan (2020) place around the European open are defined on it. Measuring how much of the price a given stretch of a session moved requires accumulating the contribution of each stretch, and the study that treats price-discovery efficiency in the night session that way is Lee and Woo (2013). The questions asked by Ahn et al. (2024), who set retail flows against the gap between overnight and intraday returns, and by Kwak (2024), who links retail trading intensity to returns, presuppose the same partition. What this screen publishes, however, stops at the bars and the change against the reference price, and price contribution, price impact and estimators of the kind Nam et al. (2006) used to measure how far futures lead the cash market are not computed here.

KOSDAQ 150 night futures sit between the trading hours of United States technology stocks and the morning of domestic growth names. Mun and Hong (2003), on how movement in NASDAQ 100 carries into the overnight returns of KOSDAQ 50 index futures, the return from one close to the next open, is an early study confirming this route on Korean data, but its sample covers an earlier generation of index than the present KOSDAQ 150, so what it yields is the existence of the route rather than the realized behavior of the current contract. The frame for cross-border transmission itself was laid down by Eun and Shim (1989) and Hamao, Masulis, and Ng (1990), and the partition of Lin, Engle, and Ito (1994), which pairs one market's day with another's night, made that measurement possible. Because the trading hours of United States technology stocks fall entirely inside the night session, the order in which information arrives is particularly visible in this contract.

Domestic work dealing with KOSDAQ 150 directly is still thin. Kang (2021) set out how this index's futures market grew and how cross-product trading joining the cash market, funds and futures took shape. Yoon (2021), placing the two markets side by side, established that the informational efficiency of KOSPI and KOSDAQ is not the same. Results addressing the night session head on, by contrast, come mostly from the KOSPI 200 contract. Lee and Woo (2013) on price-discovery efficiency in the night session, Yeom, Baek, and Ryu (2013) on strategies that use night futures, Seon and Joo (2019) on the change in frictions around the start of night trading, and Nam et al. (2006) on futures leading the cash market are all evidence obtained on KOSPI 200 and apply to KOSDAQ 150 by analogy only. That night trading in this contract has not been open long, leaving a short sample, is a further reason the reasoning leans on analogy.

Being a growth index makes that analogy something to handle more carefully. KOSDAQ 150 is heavy in growth-stage technology firms and therefore sensitive to the discount rate, and on a night when United States rates have moved sharply the same news can land with a wider swing than on the large-cap index. That direction, though, is a property following from the composition of the index rather than a result measured in the night session. The smaller the companies it holds, the thinner the book in the individual names, and that thinness shows up in the cost of the trades that replicate the index rather than in the index itself. Heavy retail participation cuts the same way. Kwak (2024), on the relation between retail trading intensity and returns, and Ahn et al. (2024), who place retail flows beside the split between overnight and intraday returns, show the participant mix bound up with how returns are spread across the hours, though neither study uses night data from this contract.

Even so, the observation that returns concentrate outside regular trading hours recurs regardless of an index's character. Bondarenko and Muravyev (2023) showed that most of the average daily return in S&P 500 futures sits inside a narrow band, and Boyarchenko, Larsen, and Whelan (2020) recorded the concentration around the European open. Translated into Korean time, that moment falls close to the dead stretch between the regular close and the night open, so a claim that information density is higher late in the session does not follow from this data. Nor has any published measurement confirmed whether the same shape appears in KOSDAQ 150 night futures.

A fund tracking KOSDAQ 150, or a portfolio tilted toward growth names, carries the gap lying between the 15:30 stock-market close and the 09:00 open the following day. The exchange market open in between is night futures, and estimating a hedge ratio takes the form of regressing the next morning's opening gap on the night return of the same night.

Here $\Delta S^{\mathrm{open}}_{t}$ is the underlying index return from close to next open, $\Delta F^{\mathrm{night}}_{t}$ the night-futures return of that night and $h^{*}$ the ratio that makes the residual variance smallest. The thinner the orders gathering in the opening call and the wider the swings of the individual names, the larger that residual variance, so a value estimated on the large-cap contract cannot simply be carried over. Yeom, Baek, and Ryu (2013), who examined the performance of strategies using night futures, also worked on a KOSPI 200 sample, so their result stands here as a reference case rather than direct evidence.

The terms of trading that deviation are set by replication cost. Fair value follows from the cost-of-carry relation (Cornell and French 1983), but buying and selling a basket of KOSDAQ growth stocks costs more than a large-cap basket and the individual books are thinner, which widens the no-arbitrage band around fair value. That does not mean the linkage is closed. Kang (2021) set out how cross-product trading joining the cash market, funds and futures took shape as the KOSDAQ 150 futures market grew. Creation and redemption of exchange-traded funds, market-maker hedging and cash-futures arbitrage aim at the same fair value from different positions. At night the cash side of that route is closed, so relations that hold during the day are not maintained unchanged.

A trade leaves a larger mark in a thin book than it does during the day. The explanation divides in two, the low after-hours volume set against a large per-trade price contribution reported by Barclay and Hendershott (2003), and the disproportionate pull that mid-sized trades exert on cumulative price change in Barclay and Warner (1993). Webb et al. (2016) measured the intraday seasonality of price impact on KOSPI 200 futures, and there is no reason for that asymmetry to fade in a contract with thinner liquidity. Night volume is informative accordingly in how it stands against what is usual at the same hour, not at its face level.

Marking overnight exposure in real time is a use the two contracts share. What differs is that the domestic evidence to lean on is thin on the KOSDAQ side. Nam et al. (2006) on futures leading the cash market, Lee and Woo (2013) on price-discovery efficiency in the night session and Seon and Joo (2019) on the change in frictions after night trading began are all results from KOSPI 200, and the object Mun and Hong (2003) studied in treating spillover from NASDAQ 100 into KOSDAQ 50 index futures is a different index from the present one. Alongside the comparison of Yoon (2021), in which the two markets differ in efficiency, and the work of Kwak (2024) on retail trading intensity, there is no ground for reading a figure obtained on the large-cap contract as a value for this contract. The overseas observation that returns concentrate in a narrow band outside regular trading hours (Bondarenko and Muravyev 2023) has likewise never been confirmed to reproduce here.

This screen is not a sample pinned to a fixed date but a live series that passes a session along while it is still running. The platform's testing framework scores only snapshots taken at a fixed point in time and leaves one harness row per series stamped with the sample's end date, and the night-session bars have no such row. No battery of unit-root, stationarity or structural-break tests is therefore reported and no verdict is claimed. Not having run a test is not the same thing as having passed one.

The standing of the numbers on the screen follows from this. The open, the high, the low, the close, the volume and the change against the reference price are constructed quantities made from trades by a fixed rule, and they are not values a model estimated. A constructed quantity carries neither a standard error nor a null hypothesis, so the word significance has nothing to attach to, and a value that moves during a session moves because more trades have arrived rather than because an estimate was updated. The bar count and the presence of minutes without trades are likewise properties of the data itself and not products of missing-value handling.

The condition under which a battery becomes possible is plain. Once completed sessions are archived as a panel stacked by session date and that panel's end date is frozen, a list of tests suited to the character of the series can be fixed in advance, run once and recorded together with the direction of each verdict. Until that sample exists, this series has no test results.