1

Volatility Derivatives in Practice: 

Activity and Impact

Scott Mixon∗

Esen Onur∗

November 2014

Abstract:

We use unique regulatory data to examine open positions and activity in both listed and OTC 

volatility derivatives. Gross vega notional outstanding for index variance swaps is over USD 2 

billion, with dealers short vega in order to supply the long vega demand of asset managers. For 

maturities less than one year, VIX futures are far more actively traded and have a higher 

notional amount outstanding than S&P 500 variance swaps. To the extent that dealers take on 

risk when facilitating trades, we estimate that the long volatility bias of asset managers puts

upward pressure on VIX futures prices. Hedge funds have offset this potential impact by

actively taking a net short position in nearby contracts. In our 2011-2014 sample, the net 

impact added less than half a volatility point, on average, to nearby VIX futures contracts but 

added between one and two volatility points for contracts in less liquid, longer-dated parts of 

the curve. We find no evidence that this price impact forces VIX futures outside no-arbitrage 

bounds.

 ∗ Office of the Chief Economist, Commodity Futures Trading Commission, 1155 21st Street, N.W., Washington, D.C. 

20581. Mixon: smixon@cftc.gov, (202) 418-5771. Onur: eonur@cftc.gov, (202) 418-6146. We thank seminar 

participants at the CFTC for helpful comments. 

The research presented in this paper was co-authored by Scott Mixon and Esen Onur in their official capacities 

with CFTC. The Office of the Chief Economist and CFTC economists produce original research on a broad range of 

topics relevant to the CFTC’s mandate to regulate commodity future markets, commodity options markets, and the 

expanded mandate to regulate the swaps markets pursuant to the Dodd-Frank Wall Street Reform and Consumer 

Protection Act. These papers are often presented at conferences and many of these papers are later published by 

peer-review and other scholarly outlets. The analyses and conclusions expressed in this paper are those of the 

authors and do not reflect the views of other members of the Office of Chief Economist, other Commission staff, or 

the Commission itself.
2

Abstract:

We use unique regulatory data to examine open positions and activity in both listed and OTC 

volatility derivatives. Gross vega notional outstanding for index variance swaps is over USD 2 

billion, with dealers short vega in order to supply the long vega demand of asset managers. For 

maturities less than one year, VIX futures are far more actively traded and have a higher 

notional amount outstanding than S&P 500 variance swaps. To the extent that dealers take on 

risk when facilitating trades, we estimate that the long volatility bias of asset managers puts 

upward pressure on VIX futures prices. Hedge funds have offset this potential impact by 

actively taking a net short position in nearby contracts. In our 2011-2014 sample, the net 

impact added less than half a volatility point, on average, to nearby VIX futures contracts but 

added between one and two volatility points for contracts in less liquid, longer-dated parts of 

the curve. We find no evidence that this price impact forces VIX futures outside no-arbitrage 

bounds.
3

Are VIX-linked products distorting the VIX? Some commenters have questioned whether VIXlinked products (Exchange Traded Funds or Exchange Traded Notes) have grown so popular 

that the hedging activities of the issuers dominate and distort the VIX futures market. 

Pessimists ask if liquidity-driven disruptions in VIX ETN markets are being transmitted to the VIX 

futures markets; optimists point to increased (indirect) participation in futures markets 

providing better liquidity and more informative pricing. Analysis is complicated by the fact that 

related volatility derivatives are actively traded in the OTC market, and little is known about the 

trading activity in that space. 

We use unique regulatory data on both OTC variance swaps and exchange-listed VIX futures to 

evaluate the activity and impact of volatility derivatives trading. This study is the first to use the 

market-wide data on variance swap transactions mandated by the Dodd-Frank Wall Street 

Reform and Consumer Protection. Both the swap and futures data include information on the 

participant holding any particular reported position; we are therefore able to link precisely the 

participant type with the positions and need not estimate such relationships. 

We find that gross vega notional outstanding for index variance swaps is over USD 2 billion, 

with USD 1.5 billion in S&P 500 products and nearly USD 500 million across five other major 

indexes across the globe. Dealers are net short vega in order to supply the long variance swap 

demand of unlevered asset managers. For maturities less than one year, VIX futures are far 

more actively traded and have twice as much notional vega outstanding than S&P 500 variance 

swaps. 
4

To the extent that dealers take on risk when facilitating trades, we estimate that the long 

volatility bias of asset managers acts to put upward pressure on VIX futures prices. Hedge funds 

have offset this potential impact because they have actively taken a net short position in nearby 

contracts. In our 2011-2014 sample of VIX futures, the net impact averaged less than half a 

volatility point in nearby contracts, but between one and two volatility points for contracts in 

less liquid, longer-dated parts of the curve. The impact of non-dealer positioning has evolved 

over time as the demands of the various participant types have changed. In 2011 and 2012, for 

example, unlevered asset managers were long and hedge funds were not active, and we 

conclude that this positioning increased front-month VIX futures approximately one volatility 

point. By 2013, hedge funds took sizeable short positions and the estimated impact fell to 

roughly zero. 

We also find significant results when we interpret the data from a term structure perspective: 

the active rolling of contracts by some large asset managers in order to keep a target maturity 

appears to have a small but significant impact on the slope of the term structure. The price of 

the front month contract has been depressed by this persistent selling, on average, while the 

price of the second month contract has been increased. In both cases, this term structure effect 

averages less than half a volatility point. Throughout the entire period analyzed, we find no 

evidence that these price impacts forced VIX futures outside no-arbitrage bounds.

The outline of the paper is as follows. First, we quantify activity in the OTC market and contrast 

the findings with those for the VIX futures markets. We use transaction-level regulatory data to 

gauge typical activity in index variance swaps in various major indexes. Second, we quantify the 

open commitments of variance swaps. In both exercises, we break out the transactions due to 
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interdealer trading and the ones due to dealer/end-user trading. We also evaluate the net 

positioning of various types of market participants across products and across the term 

structure for the S&P 500. Given our finding that the most active part of the volatility derivative 

space is the listed futures market, we focus on VIX futures for the remainder of the analysis. We

model the impact on VIX futures prices of futures positions and end-of-day position changes for 

both the buy-side and sell-side. 

The papers closest in spirit to our work are those of Bollen and Whaley (2004), Bollen, O’Neill, 

and Whaley (2013), and Garleanu, Pedersen, and Poteshman (2009).1 These papers provide 

strong empirical evidence that dealer capacity and buying pressure are key determinants of 

derivatives prices. In particular, Bollen, O’Neill, and Whaley (2013) examine the buying pressure 

of VIX ETNs on VIX futures and the VIX; they conclude that the long volatility bias of asset 

managers has strongly impacted VIX futures pricing. A portion of our work examines that 

question, but we use daily regulatory data to examine the impact of asset managers. The 

richness of this data allows us to identify the actual quantity (not an estimate) of futures 

demanded by asset managers as well as the potentially offsetting short demand by hedge 

funds. We separately identify the impact of both asset managers and hedge funds on the VIX 

futures pricing and argue that both impacts should be analyzed together to understand the 

dynamics of this market.

ACTIVITY IN VARIANCE SWAPS AND VIX FUTURES

 1 A variety of papers have examined (synthetic) variance swap prices and VIX futures prices, including Carr and Wu 

(2009), Egloff, Leippold, and Wu (2010), Nossman and Wilhemsson (2009), and Simon and Campasano (2014). 

Other papers have examined the empirical link between index implied volatility and macroeconomic, risk, or 

sentiment variables, e.g., Corradi, Distaso, and Mele (2013), David and Veronesi (2014), Glatzer and Scheicher 

(2005), Han (2008), Mixon (2002), Mixon (2007), and Vähämaa and Äijö (2011). These empirical analyses do not 

directly link observed trading activity and positions to market prices. 
6

The type of OTC data we examine was not available to regulators or market participants prior to 

the current reporting regime and has not been analyzed before. While individual participants 

knew their own transactions and may have had second hand knowledge of other transactions, 

there are no publicly available sources of OTC transaction data for these swaps. Presently, large 

participants in the US swaps market must register with the Commodity Futures Trading 

Commission (CFTC) and report all swap transactions to Swap Data Repositories (SDR). Basic 

details on transaction activity are reported to the public by SDRs, while comprehensive details 

identifying the counterparties and their trade directions are available to regulators.

The repositories consequently offer an unprecedented, transaction-level view of derivatives

markets. The data is not just time and sales type information; it is data in the same spirit as the 

CFTC’s Large Trader Reporting System data, which provides position-level information on 

participants. The aggregation of derivatives transaction data into SDRs provides a powerful tool 

to regulators and market participants. Confidential information from individual participants 

remains confidential, but regulators can present the aggregated SDR data to the public in order 

to promote sound risk management while preserving the liquidity and price discovery role of 

derivatives markets.

The remainder of this section elaborates on our description of activity and positioning in the 

variance space. We find that new swaps are predominately initiated in a handful of indexes, 

with nearly half of the new activity in the S&P 500. In turn, a large fraction of the S&P activity is 

dealer/dealer trading. The majority of activity in the front part of the curve is in VIX futures 

trading, but variance swap activity extends to maturities out a decade or more. Vega 

outstanding aggregates to USD 2 billion, with USD 1.5 billion in S&P 500 swaps. Roughly two 
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thirds of the S&P 500 notional outstanding has dealers on both sides of the trade. Dealers, on 

net, are significantly short variance across the major indexes.

Transaction Activity

We begin with a brief description of variance swap market terminology to facilitate the 

description of our summary statistics. The terminal valuation of a variance swap is given by the 

expression Nvvv (σ r rrrrr

2 − σss s2 ), where Nvvv is the variance notional of the transaction, 

σ r rrrrr

2 is the annualized realized variance of the reference asset over the life of the swap 

(expressed in volatility points), and σss s

2 is the variance strike. The variance strike is thus the 

fixed leg of the swap and the realized variance is the floating leg. The floating leg of the 

variance swap is defined by the equation σ r rrrrr

2 = 252

n ∑ r n 2 =1 .

Industry convention is to set the variance notional of the swap using the equation Nvvv =

Nvvv

2σssssss

2 , where Nvv is the desired vega notional of the swap. This vega notional is approximately 

the value of the swap if, at the valuation date of the contract, the realized volatility and the 

square root of the variance strike (the volatility strike) differ by one vol point. Consistent with 

industry usage of this intuitive notional value, commonly referred to as “dollars per vol point”, 

we also reference the vega notional of the swaps throughout this paper.

We rely on SDR data on open swap positions at the beginning of June 2014 for our crosssectional analysis of variance swaps outstanding. We use the same dataset to identify new 

swaps initiated during the month of May 2014, which is a straightforward measure of 

transaction activity in this market. Futures statistics are computed using publicly available end 

of day reports for May 2014. The raw swap data contains approximately 19,000 records, and 
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we filter it in the following ways. We remove roughly 6,000 transactions between affiliated 

entities within the same corporate family (e.g., a transaction between “Dealer A – New York” 

and “Dealer A – London”). In many instances, these transactions occur virtually simultaneously 

as a transaction by one of the dealer entities with an end-user on the other side of the trade. 

For the purposes of this study, they are taken to be more of an accounting exercise than a 

market-facing transaction, and they are deleted from the dataset. This practice is also followed

by the Bank for International Settlements in its surveys of the global derivatives market. We 

also eliminate obvious transcription errors. The resulting dataset of open swaps contains 

12,314 records and the dataset of new trades contains 1,384 records.

Exhibit 1 displays summary statistics for new variance swaps initiated during the sample period. 

Several observations are immediately apparent from the table. First, trade in S&P 500 contracts 

represents the most activity across products, with approximately half of the USD 160 million 

vega initiated in these instruments. There were 21 regular trading days during the sample 

period, yielding an average daily volume of USD 4 million in S&P 500 variance; transactions 

were observed on each day in the sample. Nearly USD 2 million SPX vega was transacted with 

end-users, on average, per day during the sample. Transactions on the EuroStoxx 50 were 

second most prominent, with transactions taking place with end-users on most, but not all, 

days of the sample. Activity on other products is often far less than on these top two.

A second observation is that transactions between swap dealers are typically larger in size and 

long-dated than transactions between swap dealers and end-users. For example, the median 

trade size (not shown in the table) for S&P 500 swaps was USD 100,000, whether measured 

across all trades or for dealer/dealer trades or dealer/end-user trades. The average size is USD 
9

132,000 for dealer/dealer trades and 112,000 for dealer/end-user trades, reflecting some large 

trades between dealers. Similarly, dealer/dealer trades in the SPX have a tenor about six 

months longer than dealer-end-user trades (1.3 years vs. 0.8 years). These facts are consistent 

with typical dealer activity documented by Naik and Yadav (2003), with dealers facilitating 

many trades by end-users and hedging the entire book in the inter-dealer market.

Exhibit 2 breaks out the new trades during the sample period by the tenor of the trades. The 

vertical axis represents the USD vega transacted, and the horizontal axis represents the 

valuation dates for the swaps. The height of the line at a given valuation date therefore displays 

the cumulative activity in swaps maturing before or on that date. For example, approximately 

USD 35 million of vega was transacted in S&P 500 swaps maturing in December 2014 or earlier. 

Another USD 50 million in vega was transacted in swaps maturing between that date and 

December 2017, with a small amount transacted in dates beyond December 2017. The same 

pattern shows up for the other indexes presented, with the levels of transactions and tenors of 

transactions less than those for the S&P. 

For context, the chart also shows May 2014 trading volume for VIX futures. VIX futures activity 

dwarfs that of the variance swap market, but the activity is concentrated in the first few 

contracts. VIX futures trading totals USD 3 billion of vega over the month (an average of 144

million vega per day). We recognize that notional futures trading volume is not perfectly 

comparable with the notional value of new swaps positions (and that VIX futures and variance 

swaps are not perfect substitutes). The goal of the juxtaposition, however, is to illustrate the 

vastly different characters of the two markets. The futures market exhibits many transactions 

for a given interval of time, whereas the swaps market is more about larger, more infrequent 
10

trades. The futures market also focuses on the short end of the curve, with contracts only 

extending to December 2014 and the majority of volume occurring in the first few contracts.

Exhibit 3 provides a summary of the variance positions outstanding at the end of May 2014. As 

expected, the vast majority of positions are in the S&P 500. Of the USD 2.2 billion vega 

outstanding in the SDR data, about 1.5 billion is in the SPX. Of the SPX positions, about 2/3 are 

between dealers, with the remaining 428 million vega representing open dealer/end-user 

positions. The EuroStoxx 50 is the second largest set of positions in the market, with a bit more 

than 100 million of positions between dealers and a bit less than 100 million representing 

positions with end-users. The original tenor of the swaps currently open averaged 3.9 years for 

the SPX, representing a blend of interdealer positions averaging 4.3 years and positions with 

end-users averaging 2.9 years. Tenors for other indexes are shorter but show the same pattern. 

The set of open positions also can be used to quantify the concentration of the positions across 

dealers. In this computation, the concentration is assessed by aggregating positions in different 

subsidiaries of a parent company. (For example, positions held by Dealer A - New York and 

Dealer A – London are aggregated into one set of positions.) As with many dealer markets, the 

top few dealers capture the lion’s share of positions. The concentration is such that 70-95% of 

positions are with the top five dealers in this data. While this is true for swaps on each 

individual index, the concentration for the top five dealers across all of the data is slightly lower 

at 68%, suggesting that it is not the same five dealers who hold the majority of positions in 

every index.

The CFTC’s Commitments of Traders report has provided transparency on positioning across 

various market participant types for decades. Exhibit 4 provides a similar view on net 
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positioning across participant types for VIX futures and the major variance swaps.2 With respect 

to the buy side, the chart suggests that asset managers are net long vega in each of the indexes, 

with the largest net positions in S&P 500 variance and VIX futures (totaling around USD 150 

million in the two of them together). Leveraged money is generally short vega, with the largest 

position, by far, short VIX futures. Dealers are short vega, with a large short position in S&P 500

variance.

Exhibits 5 and 6 focus on S&P 500 exposure across VIX futures and variance swaps of different 

tenors. Exhibit 5 displays the gross notional amount outstanding, while Exhibit 6 displays net 

positioning across market participant types. Both exhibits break out S&P 500 positions across 

tenors, showing the consistency of positioning across the term structure. The displays are for a 

single bucket covering VIX futures (which all expire in less than a year) and four buckets for SPX 

variance covering the zero to one year, one to five year, five to ten year, and ten to twenty year 

tenors. With respect to gross notional, Exhibit 5 illustrates that the VIX futures footprint is more 

than twice as large as that of S&P variance (USD 470 million vs. USD 200 million) in the zero to 

one year bucket. The S&P variance buckets for longer-dated tenors are larger than the VIX 

futures buckets, however. With respect to net notional, Exhibit 6 makes clear that the 

positioning in variance is consistent across tenors for the participant types. Asset managers are 

consistently long volatility but there is a pronounced long position in the five to ten year 

bucket. Dealers are short vega across the term structure and are especially short in the five to 

ten year bucket in a mirror image of the asset manager positioning. Leveraged money is 

 2 The participant types are broadly the same as those used for the Commitments of Traders report. The “Asset 

Manager” category includes investors such as pension funds, endowments, insurance companies, and mutual 

funds. Participants labeled “Leveraged Funds” are typically hedge funds. Swap data parties are identified by Legal 

Entity Identifier (LEI) and are classified manually for this project.
12

significantly short VIX futures, but the positions in variance are relatively small irrespective of 

the tenor bucket. The chart also highlights the dominance of VIX futures positioning in the most 

active part of the curve, the portion that expires in one year or less.

IMPACT OF TRADING ON MARKETS

The evidence thus far confirms what most market participants already believed: the major 

instrument for volatility trading is the S&P 500. For the remainder of this paper, we focus solely 

on the S&P 500 and on the major listed volatility future, the VIX future. Given this narrow focus, 

we can explore very specific, sharp evidence on the impact of positioning and trading in VIX 

futures. The main finding from this section is that the positions of the various market 

participants do, in fact, move the futures price. Net positive end-user demand acts to raise the 

price, while the selling (buying) of contracts acts to lower (raise) the price over short horizons 

such as one day.

Data and Theory

The data examined in this section are from the Commodity Futures Trading Commission’s Large 

Trader Reporting System. The sample covers the three year period from April 2011 to March 

2014. The data are daily market settlement prices for the first six futures contracts and for the 

VIX, as well as daily levels and changes in levels of net positions by market participant type.

Longer dated contracts exist, but they are thinly traded.3

 3 We limit our focus to the more active, shorter maturities in order to avoid inadvertent revelation of confidential 

information. We also limit our sample to a recent period when the various categories of market participant types 

include enough diversity to minimize any concerns regarding confidential information. 
13

Positions are aggregated across traders and accounts into participant types for each contract 

outstanding on a given date. Market participants are categorized in this section as “Dealers”, 

“Asset Managers”, or “Leveraged Funds”. This categorization is generally similar to that used by 

the CFTC’s Commitments of Traders (CoT) report, although we classify some entities in different 

roles due to our slightly different analytical focus. For example, we aggregate some participants 

into the “Leveraged Funds” category that might otherwise be classified as “Financial – Other” in 

a CoT report; this categorization choice has no major influence on our general findings.

The theoretical framework utilized has been carefully articulated by Gârleanu, Pedersen, and 

Poteshman’s (2009) work. They discuss a logically consistent theory in which dealer capacity 

constraints act to drive a wedge between market prices and textbook no-arbitrage prices. In 

their empirical implementation, they find that the level and variation of the typical gap

between implied and realized volatility for S&P 500 index options can be explained by the net 

positive non-dealer demand for optionality. Dealers facilitate the trades but cannot hedge 

perfectly, so they charge a premium price based on the amount of risk they take on. Models 

that explicitly incorporate such financial intermediation appear to be very important in 

explaining option prices.

Empirical Results

Exhibit 7 provides evidence on the dealer capacity issue with respect to the pricing of volatility. 

The first test is whether the price of a VIX future varies according to the level of dealer positions 

in VIX futures. Their capacity to take on short volatility positions is finite, and theory suggests 
14

that they charge higher prices to take on more risk. The test is conducted separately for each 

contract i and is based on the regression:

v − v0 = α + β v0 + γ (DDDDD ) + ε .

In this setup, v is the daily settlement price for contract i, v0 is the VIX spot price, and DDDDD

is the net position (the number of contracts) of dealers in contract i. We follow Gârleanu et al.

and assume that end-user demand is inelastic and exogenous, with dealer prices set such that 

markets clear at each date.4 The maintained hypothesis is that the VIX is exogenously 

determined relative to VIX futures prices. The tested hypothesis is that the coefficient γ is 

significantly negative, signifying that the futures price is negatively related to the level of 

positioning by dealers. A lower than average (i.e., more net short) dealer position is related to a 

higher futures price (equivalently, a higher futures basis). 

Panel A of Exhibit 7 displays the regression results for the first six contracts. For five of the six 

contracts, the coefficient is significantly negative, with Newey West (1987) standard errors 

delivering t-statistics well above conventional significance levels. The results strongly suggest 

that, at the margin, market prices of VIX futures are higher the more dealers are asked to be 

short volatility.

The second test is an elaboration of the first test and uses the fact that dealer positioning is 

equal and opposite in sign to net end-user demand.5 The idea is that end-user demand (i.e., the 

buy-side demand for vega) differs across market participant types. In particular, we regress the 

 4 We relax this exogeneity assumption in robustness tests of our regressions. In the unpublished appendix, we reestimate our regressions using lagged position data and contemporaneous VIX data as instruments and find that 

our general results are not altered.

5 The reported data do not exactly offset in practice because not all positions are reportable, but the discrepancies 

are small and average about 1,000 contracts in this sample.
15

futures price on the level of VIX futures positions held by two separate segments of the buy 

side: asset managers and leveraged funds (i.e., hedge funds). The hypothesis to test is whether 

the futures price is positively related to the net positions of the two types of end-user demand.

For notation, let AA be the net position (in contracts) for Asset Managers in the ith maturity 

contract, and let LL be the net position for Leveraged Funds in the ith maturity contract. As 

before, the maintained hypothesis is that the VIX is exogenous to the VIX futures price. The 

regression is estimated separately for each contract with the specification as follows:

v − v0 = α + β v0 + γ1 (AA ) + γ2 (LL ) + ε . (1)

Panel B of Exhibit 7 displays the results of the regressions. For the six γ1icoefficients, the 

evidence strongly supports a positive relation between the level of Asset Manager positions 

and the level of VIX futures, with all of the coefficients highly significant at conventional levels. 

For the γ2i coefficients, the evidence strongly supports a positive relation between futures 

prices and Leveraged Funds positions, with five of the six coefficients easily significant at 

conventional levels. The basic message from these regressions is that the size of the positions 

for end-users has a significant impact on the level of VIX futures: the data supports the dealer 

capacity theory.

Researchers such as Bollen, O’Neill, and Whaley (2013) have concluded that the significant VIX 

futures positions held by asset managers impacts the price of VIX futures, and the worry is that 

these impacts are distorting prices, potentially due to trading by uninformed noise traders. The 

regression results discussed above are in line with the conclusion that the large, long positions 

of Asset Managers have acted to increase VIX futures prices above what they might be in 
16

absence of those positions. On the other hand, the regressions include the large, short 

positions of Leveraged Funds, which act to decrease the futures price, everything else held 

constant. 

Because futures contracts are in net zero supply, the large net long demand by Asset Managers 

could be offset in equilibrium by a) Leveraged Funds getting net short, b) dealers facilitating the 

demand and taking short positions, or c) a combination of the two. Asset Managers have 

displayed large long positions in this sample, averaging +77,000 contracts, and Leveraged Funds 

have displayed large short positions, averaging -71,000 contracts. Net non-dealer demand has 

averaged -6,100 contracts over the sample, representing a much smaller net position (and the 

opposite sign) than Asset Managers alone. We will further explore the economic impact of the 

positions of various market participant types in a later section.

The next test gauges whether net position changes by the market participants are related to 

changes in futures prices. This question is motivated by the observation that passive Asset 

Managers in VIX futures often target a constant maturity and mechanically buy and sell 

contracts to maintain this target. As the positions have grown in size over the past few years, 

market participants have speculated that the constant rolling of contracts by passive managers 

(selling shorter dated contracts to buy longer dated ones) has put more curvature into the VIX 

futures term structure or acted to steepen it in the region around one month to maturity (the 

target maturity for many funds). 

The specific regression tested is

∆(v − v0) = α + β ∆v0 + δ1 (∆AA ) + δ2 (∆LL ) + ε . (2)
17

This specification is simply a first difference version of the levels regression used above. The 

hypothesis to test is whether the change in position sizes is positively related to the change in 

the futures basis. Examination of Exhibit 8 suggests that there is a systematic relation between 

the change in the futures basis and the change in the net positions for Asset Managers. The 

evidence is pervasive and shows up for the first, third, fifth, and sixth contracts, and the tstatistics are well into the significant range. There is less evidence for an impact for the trading 

by Leveraged Funds, although the coefficient for the third contract is significant. The results are 

consistent with the idea that Asset Managers, some of whom mechanically roll contracts 

without direct regard to absolute prices, market liquidity, or subjective views, have trades that 

can move the futures basis in the direction that they are trading. Leveraged Fund managers, 

who have different objective functions, appear to have less impact, perhaps because they can 

time their transactions more opportunistically or because they do not roll the contracts as often 

as some Asset Managers do. Based on the results of these regressions, we are motivated to 

include both level and difference effects of end-user positions in our final model.

Exhibit 9 displays the results of our final model, which has the specification

v − v0 = α + β v0 + γ1 (AA ) + γ2 (LL ) + δ1 (∆AA ) + δ2 (∆LL ) + ε . (3)

The fit of the models compares favorably with the levels regressions from Exhibit 7, with 

adjusted R2 values approximately the same or slightly higher. The significance of the levels 

variables (coefficients shown under the header “Positions”) generally obtains for both Asset 

Managers and Leveraged Funds. The significance of the first differenced variables (coefficients 

shown under the header “Position Changes”) shows up only for the first two contracts, which is 

where conventional wisdom puts the impact. The regressions strongly support a link between 
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contract rolls and the steepness of the VIX futures term structure. In the next section, we 

explore the economic relevance of this model by examining counterfactuals implied by it.

ECONOMIC RELEVANCE

On average, the VIX futures term structure sloped steeply upward during our sample period. 

The settlement price of the front month contract averaged 19.12, with each more distant 

contract displaying a higher average value out to the sixth contract, which averaged 22.98 (VIX 

futures prices can be interpreted as annualized volatility levels). The worry by some is that this 

steep upward slope is abnormal and is driven by, or at least exacerbated by, the large long 

positions of Asset Managers. Based on the estimated models, we ask “How much of this steep 

upward slope is explained by positioning?”

Counterfactual Analysis

We compute the counterfactual futures prices by assuming zero positions and zero position 

changes by end-users at each date in the sample (i.e., the counterfactual is computed as 

v − v0 = α + β v0). Exhibit 10 displays the average counterfactual values implied by the 

model, the average effects due to the positioning variables, and the resulting average VIX term 

structure over the sample. 

The observed average value for each contract is displayed in the final column of Exhibit 10. The 

counterfactual price (displayed in the second column) also slopes upward, but it displays a 

kinked shape for the averages. The counterfactual front month contract is below the observed 

value (shown in the last column of the table), the peak of the counterfactual term structure is 
19

at contract 3, which is 1.6 vol points higher than the observed average value, and the longest 

dated counterfactuals are below the observed average values.

The middle two columns of the table (“Level Effect” and “Roll Effect”) break out the estimated 

impact of the actual positions for each contract. The “Level Effect” value is the average, 

combined effect of the Asset Managers and the Leveraged Funds positions at each date. The 

“Roll Effect” is the average, combined effect of the position changes of the two participant 

types. For convenience, the sum of these two values is shown in the next to last column (“Net 

Effect”). For each date, this predicted net effect is the sum of the products of the estimated 

coefficients multiplied by the observed variables. 

We find that the Level Effect is generally larger than the Roll Effect, with the Roll Effect less 

than 0.1 vol points for the third through the sixth contract. While the Level Effect appears to be 

statistically significant at a very high confidence level, the Roll Effect is not significant for the 

contracts furthest out. 

This breakout highlights the relative impact of the Level Effect versus the Roll Effect: even when 

the rolling of positions impacts the futures price in the front two contracts, the impact is small 

compared to the Level Effect of aggregate position sizes. For the front month contract, the 

Level Effect acts to increase the futures price by 0.51 vol points, but the Roll Effect partially 

offsets this by 0.17 vol points, with the Net Effect just 0.35 vol points. A larger impact is actually 

observed in the third, fifth, and sixth contracts, with the model suggesting impacts between 

one and two vol points. Interestingly, the predicted impact on the third contract is to decrease

the price by 1.6 vol points, contrary to the general idea that long positions of Asset Managers 

have pushed up prices. This is readily explained by the fact that Asset Managers do not hold 
20

large positions in this contract, but Leveraged Funds hold, on average, large short positions 

which are offset by long dealer positions.

We use the estimated model to attribute the differences between the observed data and the 

counterfactual data in a second breakout. Whereas the previous exhibit attributed the 

differences to a Level Effect and a Roll Effect, Exhibit 11 displays the average estimated net 

impact attributed to non-dealer participant types. It also shows, in addition to these two 

impacts, the net impact of these two. 

The results tell a simple story. The average long positions of Asset Managers act to increase the 

futures price, at the margin, across every contract except the third one, but the average short 

positions of Leveraged Funds act to decrease the futures price for every contract. The net 

impact varies according to the strength of the two effects, which reflects the net size of the two 

positions. In the front month, Asset Managers dominate slightly, leading to the net impact of 

+0.35 vol points noted previously. In the third contract, the short positions of Leveraged Funds 

dominate, leading to the predicted -1.6 vol point impact. In longer dated contracts, where 

liquidity is undoubtedly lower, the modest Asset Manager net long positions dominate, leading 

to a predicted impact between 1.5 and 2.0 vol points.

We compare our results with those of Bollen et al. (2013). Our model suggests an estimated 

upward pressure of impact of on the front month contract, due to Asset Managers, of just 

under half a vol point. This is partially offset by short positons of Leveraged Funds, with a net 

impact of +0.35 vol points. Bollen et al. find that the distribution of VIX futures prices relative to 

VIX peaks at 2.5% since VIX ETNs were introduced. Given an average VIX level of 22.9 for their 

sample, their analysis suggests that long demand for VIX futures by ETP hedgers added 0.6 vol 
21

points (22.9 x 2.5%) to the price of VIX futures. By that comparison, our results are quite similar 

to those of Bollen et al. We find, and they find, a statistically significant impact of non-dealer

long futures demand on prices that is approximately half a vol point on average in recent years. 

We also find, however, an offsetting impact by non-dealer short futures demand, which they do 

not analyze. The net short demand by non-dealers becomes most influential for the third 

contract, where Asset Manager’s net demand is the smallest. Further, we find even larger net 

impacts on the longer-dated contracts expiring five to six months out. 

Relation to No-Arbitrage Bounds

We have not evaluated the economic significance of the estimated impacts described so far. Is 

half a vol point of price pressure economically large? One measure of the significance is to 

compare it to the bid-ask spread for VIX futures. Over the sample period, the median end-ofday bid-ask spread has been 0.05 vol points for all six contracts, which might make one 

conclude that the impact is quite large.

On the other hand, we argue that the more relevant action is to compare the estimated impact 

to the no-arbitrage bounds for the futures price. Carr and Wu (2006) show that the VIX futures 

should be bounded below by the at-the-money-forward implied volatility and above by the 

volatility strike of the forward variance swap. Both of these variables are observable. 

Exhibit 12 displays the width of this range and the predicted impact from the models for the 

front month contract. The no-arbitrage range is computed using the one- and two-month 

constant maturity S&P 500 implied volatilities and the one- and two-month indicative variance 
22

swap rates, both from Bloomberg. For clarity, the chart displays 20-day moving averages of the 

impact and the bounds.

Our first conclusion from examining the chart is that the estimated impact due to non-dealer 

positions is usually well within the no-arbitrage range. The range is far wider than the 0.05 vol 

point bid-ask spread, and it varies over time (averaging around two vol points wide during this 

period). When measured by this standard, the impact due to net non-dealer futures demand is 

not large. 

Our second conclusion from examining the chart is that the impact itself varies over time as the 

relative positions of the two participant types changes. The conclusion is facilitated by 

examination of Exhibit 13, which displays the net positions of the three participant categories. 

The price impact declined sharply in August 2011 as Asset Managers rapidly reduced their 

positions and dealers increased their positions as they absorbed the other side. It fluctuated

around one vol point during the September 2011 – September 2012 period, when Asset 

Manager longs were larger in magnitude than Leveraged Funds shorts (with dealers averaging -

19,000 contracts short as a consequence). It was approximately zero over the September 2012-

March 2014 period when Leveraged Funds increased their short positions and dealers moved

net long as a consequence.

CONCLUSION

VIX futures represented nearly 20% of CBOE’s annual transaction revenue in 2013, according to 

publicly available data, and contract volumes have grown at a dramatic pace each of the last 

several years. Variance swaps, a related product, are traded OTC and have historically featured 
23

the opaqueness common to smaller OTC markets. Volatility derivatives used to be science 

fiction, but today they are clearly a major part of derivatives activity. With this prominence 

comes concern that the activity is somehow distorting the market signals that the product 

should provide. We use unique regulatory data in this paper to examine open positions and 

activity in both the listed and OTC volatility space. We then go on to evaluate the impact of 

trading and positions in the most active volatility derivative of the S&P 500: VIX futures 

contracts.

We find that the stock of variance swaps is large for long-dated contracts, with S&P 500 

instruments exhibiting USD 1.5 billion vega outstanding (a third of which is between dealers 

and end-users) and transactions occurring daily. Dealers are short vega in order to supply the 

long vega demands of asset managers. The market for variance swaps on other underlying 

indexes is somewhat smaller, with activity concentrated among the next five or so indexes 

before trailing off to less active markets. Total notional vega outstanding is over USD 2 billion, 

and we find over USD 150 million initiated in new swaps in a recent sample month. VIX futures 

are far more actively traded, with USD 3 billion of vega over the month, and all of the activity is 

in the front end of the curve. 

To the extent that dealers take on risk when facilitating trades, we estimate that the long 

volatility bias of asset managers acts to put upward pressure on VIX futures prices. Hedge funds 

offset this theoretical impact because they have actively taken a net short aggregate futures 

position in our sample. For the front-month contract, the net upward impact of long non-dealer 

activity on prices, on average, is less than half a volatility point. At the longer end of the futures 

curve (e.g., contracts expiring in 5-6 months), non-dealer positioning has a long bias that results 
24

in upward pressure of 1.5 vol points. For futures expiring in approximately three months, the 

net short hedge fund positioning dominates, driving futures prices down. Nonetheless, we find 

no evidence that this price impact forces VIX futures outside of their no-arbitrage bounds.

We conclude that end-user demand for volatility products exerts a measurable influence on VIX 

futures prices. The impact at a given point in time is dependent on non-dealer supply and 

demand for VIX futures, which each vary over time. In our 2011-2014 sample period, the net 

impact averaged less than half a volatility point in nearby contracts, but between one and two

volatility points for contracts in less liquid, longer-dated parts of the curve. 
25

Exhibit 1.

Summary Statistics for Variance Swap Activity (May 2014).

S&P 

500

EuroStoxx 

50

Russell 

2000

FTSE 

100

Nikkei 

225

Kospi 

200

Other Total

Total New Swaps (USD 

Vega Millions)

85.2 23.4 4.8 11.2 4.7 8.7 21.5 159.4

SD/SD 47.9 11.1 2.8 0.5 1.8 1.6 3.3 68.9

SD/non-SD 37.3 12.3 2.0 10.7 2.9 7.1 18.2 90.5

New Swaps Total

(Count)

694 193 57 125 44 96 175 1,384

SD/SD 362 69 28 5 12 12 50 538

SD/non-SD 332 124 29 120 32 84 125 846

Original Tenor (Years) 1.1 0.6 1.2 0.3 0.8 0.9 0.8 0.9

SD/SD 1.3 0.8 1.4 0.4 1.2 1.3 1.2 1.2

SD/non-SD 0.8 0.4 1.1 0.3 0.7 0.9 0.6 0.7

Average Swap Notional 

(USD Vega Thousands)

123 121 84 90 106 90 123 115

SD/SD 132 161 99 99 149 130 65 128

SD/non-SD 112 99 70 89 90 85 146 107
26

Exhibit 2.

Variance Swap Activity (May 2014), cumulative by tenor.
27

Exhibit 3.

Summary Statistics for Variance Swaps Outstanding (May 2014).

S&P 

500

EuroStoxx 

50

Russell 

2000

FTSE 

100

Nikkei 

225

Kospi 

200

Other Total

Total Outstanding (USD 

Vega Millions) 1,512 205 81 66 51 81 166 2,164

SD/SD 1,084 112 57 40 15 37 65 1,411

SD/non-SD 428 93 24 26 37 44 102 753

Open Swaps Total 

(Count) 7,729 1,146 583 568 514 537 1,237 12,314

SD/SD 5,293 532 450 326 168 165 553 7,487

SD/non-SD 2,463 614 133 242 346 372 684 4,827

Original Tenor (Years) 3.9 2.0 4.0 2.3 1.3 1.8 1.9 3.2

SD/SD 4.3 2.3 4.3 3.0 1.5 2.9 2.7 3.9

SD/non-SD 2.9 1.7 2.8 1.4 1.2 1.3 1.2 2.2

Top 5 Dealer Share by 

Vega (SD/non-SD 

trades)

70.4% 80.6% 83.6% 86.1% 78.8% 92.7% 79.7% 68.0%
28

Exhibit 4.

Net Vega Notional Outstanding by Participant Type and Underlying Index
29

Exhibit 5.

Vega Notional Outstanding, May 2014 (VIX Futures and S&P 500 Variance Swaps)
30

Exhibit 6.

Net Vega Notional Outstanding, VIX futures and S&P 500 Variance Swaps, by Participant Type 

and Time to Maturity
31

Exhibit 7.

Results from regressing dealer and non-dealer VIX futures positions on VIX spreads.

Panel A Panel B

Contract 

Expiry

VIX Dealer Adj. R2

(%)

VIX AM LF Adj. R2

(%)

1 -0.116 -0.022 22.9 -0.082 0.020 0.005 28.1

(-4.42) (-4.29) (-3.54) (2.21) (7.36)

2 -0.254 -0.051 42.9 -0.244 0.016 0.015 39.7

(-6.92) (-5.31) (-5.91) (2.76) (2.27)

3 -0.324 -0.103 53.6 -0.437 0.064 0.092 67.1

(-9.00) (-5.74) (-12.56) (2.16) (11.33)

4 -0.431 -0.130 56.6 -0.401 0.232 0.051 58.6

(-10.42) (-5.49) (-10.66) (5.93) (5.49)

5 -0.403 0.024 50.0 -0.410 0.359 0.019 58.9

(-11.06) (0.59) (-12.57) (6.30) (1.50)

6 -0.432 -0.073 52.2 -0.424 0.468 0.030 62.6

(-11.82) (-2.44) (-12.42) (9.76) (2.24)

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Columns marked “AM” reflect coefficients on 

Asset Manager variables. Columns marked “LF” reflect coefficients on Leveraged Fund 

variables.
32

Exhibit 8.

Results from regressing non-dealer VIX futures position changes on VIX spread changes.

Contract 

Expiry

ΔVIX (t-stat) ΔAM (t-stat) ΔLF (t-stat) Adj. R2

(%)

1 -0.355 (-7.91) 0.029 (3.34) 0.007 (1.59) 59.4

2 -0.534 (-12.30) 0.008 (1.26) 0.001 (0.22) 80.4

3 -0.646 (-23.20) 0.058 (4.49) 0.027 (4.42) 90.4

4 -0.702 (-25.58) 0.029 (1.37) 0.003 (0.43) 92.7

5 -0.751 (-26.97) 0.077 (2.20) 0.022 (1.87) 94.0

6 -0.771 (-32.36) 0.061 (2.12) 0.008 (0.68) 94.7

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Columns marked “AM” reflect coefficients on 

Asset Manager variables. Columns marked “LF” reflect coefficients on Leveraged Fund 

variables.
33

Exhibit 9.

Results from regressing non-dealer VIX futures positions and position changes on VIX spreads.

Positions Position Changes

Contract 

Expiry

AM (t-stat) LF (t-stat) ΔAM (t-stat) ΔLF (t-stat) Adj. 

R2

(%)

1 0.020 (7.34) 0.003 (1.36) 0.052 (2.83) -0.005 (-0.63) 28.0

2 0.014 (2.50) 0.017 (2.57) 0.089 (3.55) -0.053 (-3.09) 43.5

3 0.058 (1.83) 0.093 (10.96) 0.071 (0.88) -0.049 (-1.52) 67.4

4 0.234 (5.85) 0.052 (5.40) -0.267 (-1.60) 0.106 (1.79) 58.8

5 0.356 (6.02) 0.016 (1.19) 0.073 (0.29) -0.062 (-0.72) 58.8

6 0.464 (9.44) 0.029 (2.11) -0.198 (-1.34) -0.094 (-1.00) 62.4

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Columns marked “AM” reflect coefficients on 

Asset Manager variables. Columns marked “LF” reflect coefficients on Leveraged Fund 

variables.
34

Exhibit 10.

Estimated non-dealer net demand impact on average VIX futures prices.

Contract Counterfactual 

Average Price

Level Effect

(t-statistic)

Roll Effect 

(t-statistic)

Net Effect 

(t-statistic)

Observed 

Average Price

1 18.77 0.51 

(7.34)

-0.17 

(-3.57)

0.35

(5.25)

19.12

2 20.05 -0.28 

(-1.55)

0.29 

(4.50)

0.02

(0.09)

20.10

3 22.70 -1.70 

(-10.50)

0.05 

(1.89)

-1.64

(-10.14)

21.04

4 21.94 -0.33 

(-2.35)

0.08 

(2.21)

-0.25

(-1.65)

21.70

5 21.20 1.16 

(4.20)

0.00 

(0.07)

1.16

(4.23)

22.38

6 21.53 1.41 

(5.29)

0.03 

(0.96)

1.43

(5.50)

22.98

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Average effects and their significance are 

estimated by computing the relevant regression coefficients multiplied by the sample average 

position and change in position data, when the averages are assumed observed without error.
35

Exhibit 11.

Estimated average impact, by contract expiry, of VIX futures positions and daily position 

changes by Asset Managers, Leveraged Funds, and the net impact of the two.
36

Exhibit 12.

Estimated impact of non-dealer net demand on VIX futures front month contract prices and 

estimated arbitrage bounds (22 trading day moving averages).
37

Exhibit 13.

Net positioning in VIX futures by Asset Managers, Leveraged Funds, and Dealers.
38

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Working paper Vanderbilt University.

Bollen, N., and R. E. Whaley. “Does Net Buying Pressure Affect the Shape of Implied Volatility 

Functions?” Journal of Finance, Vol. 59 (2004), pp. 711-753. 

Carr, Peter, and Liuren Wu. “A Tale of Two Indices.” Journal of Derivatives, Vol. 13, No. 3 (2006), 

pp. 13-29.

Carr, Peter, and Liuren Wu. “Variance Risk Premiums.” Review of Financial Studies Vol. 22, No. 3 

(2009), pp. 1311-1341.

Corradi, Valentina, Walter Distaso, and Antonio Mele. “Macroeconomic Determinants of Stock 

Volatility and Volatility Premiums.” Journal of Monetary Economics, Vol. 60, No. 2 (2013), pp. 

203-220.

David, Alexander, and Pietro Veronesi. “Investors’ and Central Bank’s Uncertainty Embedded in 

Index Options.” Review of Financial Studies Vol. 27, No. 6 (2014) pp. 1661-1716.

Egloff, Daniel, Markus Leippold, and Liuren Wu. “The Term Structure of Variance Swap Rates 

and Optimal Variance Swap Investments.” Journal of Financial and Quantitative Analysis Vol. 

45, No. 5 (2010), pp. 1279-1310.

Garleanu, Nicolae, Lasse Heje Pedersen, and Allen M. Poteshman. “Demand-Based Option 

Pricing.” Review of Financial Studies, Vol. 22, No. 10 (2009), pp. 4259-4299. 

Glatzer, Ernst, and Martin Scheicher. “What Moves the Tail? The Determinants of the OptionImplied Probability Density Function of the DAX Index.” Journal of Futures Markets, Vol. 52, No. 

6 (2005), pp. 515-536.

Han, Bing. “Investor Sentiment and Option Prices.” Review of Financial Studies, Vol. 21, No. 1 

(2008), pp. 387-414.

Mixon, Scott “Factors Explaining Movements in the Implied Volatility Surface.” Journal of 

Futures Markets, Vol. 22, No. 10 (2002), pp. 915-937.

Mixon, Scott. “The Implied Volatility Term Structure of Stock Index Options.” Journal of 

Empirical Finance, Vol. 14, No. 3 (2007), pp. 333-354.

Naik, N.K., and Pradeep Yadav. “Risk Management with Derivatives by Dealers and Market 

Quality in Government Bond Markets.” Journal of Finance, Vol. 58, No. 5 (2003), pp. 1873-1904.
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Newey, W.K., and K.D. West. “A Simple, Positive Semi-Definite, Heteroskedasticity and 

Autocorrelation Consistent Covariance Matrix.” Econometrica Vol. 55 (1987), pp. 703-708.

Nossman, M., and Wilhemsson, A. “Is the VIX Futures Market Able to Predict the VIX Index? A 

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40

APPENDIX

(NOT FOR PUBLICATION)
41

Exhibit A1. ROBUSTNESS TEST USING INSTRUMENTAL VARIABLES (LEVELS)

Results from regressing dealer and non-dealer VIX futures positions on VIX spreads.

Instruments are contemporaneous values of the VIX and lagged values of position variables.

Panel A Panel B

Contract 

Expiry

VIX Dealer VIX AM LF

1 -0.119 -0.024 -0.086 0.020 0.006

(-4.41) (-4.29) (-3.55) (2.29) (6.33)

2 -0.259 -0.055 -0.248 0.022 0.018

(-6.92) (-5.15) (-5.79) (3.10) (2.43)

3 -0.328 -0.113 -0.449 0.066 0.098

(-8.99) (-5.86) (-12.79) (1.96) (11.17)

4 -0.440 -0.147 -0.408 0.246 0.057

(-10.10) (-5.78) (-10.56) (5.73) (5.61)

5 -0.404 0.038 -0.412 0.374 0.019

(-11.34) (0.85) (-12.68) (6.08) (1.40)

6 -0.436 -0.081 -0.427 0.483 0.031

(-11.83) (-2.43) (-12.38) (9.53) (2.18)

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Columns marked “AM” reflect coefficients on 

Asset Manager variables. Columns marked “LF” reflect coefficients on Leveraged Fund 

variables.
42

Exhibit A2. ROBUSTNESS TEST USING INSTRUMENTAL VARIABLES (CHANGES)

Results from regressing non-dealer VIX futures position changes on VIX spread changes.

Instruments are contemporaneous values of 1/VIX and the change in VIX, plus lagged values of 

the change in VIX variables and the slope of the term structure (the difference between the first 

and third futures contract prices).

Contract 

Expiry

ΔVIX (t-stat) ΔAM (t-stat) ΔLF (t-stat)

1 -0.317 (-6.69) 0.234 (2.21) 0.118 (1.87)

2 -0.488 (-7.38) -0.079 (-1.57) 0.017 (0.47)

3 -0.653 (-21.25) -0.216 (-1.20) 0.117 (2.75)

4 -0.725 (-12.75) 1.55 (1.19) -0.267 (-1.07)

5 -0.724 (-11.09) 2.19 (0.46) 1.48 (0.62)

6 -0.759 (-25.51) 0.681 (1.59) 0.419 (2.23)

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Columns marked “AM” reflect coefficients on 

Asset Manager variables. Columns marked “LF” reflect coefficients on Leveraged Fund 

variables.
43

Exhibit A3. ROBUSTNESS TEST USING INSTRUMENTAL VARIABLES (FINAL MODEL)

Results from regressing non-dealer VIX futures positions and position changes on VIX spreads.

Instruments are contemporaneous values of the VIX and lagged values of position variables and 

position change variables.

Positions Position Changes

Contract 

Expiry

AM (t-stat) LF (t-stat) ΔAM (t-stat) ΔLF (t-stat)

1 0.029 (3.83) -0.001 (-0.19) 0.387 (2.18) 0.064 (0.88)

2 -0.006 (-0.29) 0.017 (0.94) -1.43 (-0.78) -1.31 (-1.11)

3 0.108 (0.70) 0.095 (6.32) 2.58 (0.34) -0.396 (-0.52)

4 0.217 (4.87) 0.053 (4.44) -1.57 (-0.94) 0.121 (0.28)

5 0.333 (4.15) 0.017 (0.57) 0.972 (0.13) -2.246 (-0.83)

6 0.520 (3.96) 0.024 (1.09) -3.956 (0.51) 0.209 (0.16)

Notes: Shaded boxes represent statistical significance at 5% level. T-statistics are based on 

Newey-West (1987) standard errors with 3 lags. Columns marked “AM” reflect coefficients on 

Asset Manager variables. Columns marked “LF” reflect coefficients on Leveraged Fund 

variables.