Analysis of the Impact of Leveraged ETF Rebalancing Trades on the Underlying Asset Market Using Artificial Market Simulation

185 views

Published on

12th Artificial Economics Conference 2016
20-21 September 2016 in Rome

Analysis of the Impact of Leveraged ETF Rebalancing Trades on the Underlying Asset Market Using Artificial Market Simulation

Isao Yagi Kanagawa Institute of Technology
Takanobu Mizuta SPARX Asset Management Co., Ltd.

Financial markets occasionally become highly volatile, as a result of a financial crisis or other factors. Previously, index futures trading and program trading have been singled out as direct causes of market destabilization, but more recently it has been suggested that leveraged ETFs (funds aimed at amplifying several-fold the movement of a price index such as the Nikkei Stock Average or underlying assets) rebalancing trades may also be a factor. This study uses a financial market simulation (artificial market) constructed virtually on a computer to assess the impact of leveraged ETF rebalancing trades on the underlying assets market. Analysis results showed that a larger amount of the managed assets of leveraged ETFs corresponds to a higher volatility of the underlying securities market. They also demonstrated that leveraged ETF trading can destroy the underlying assets market, if the leveraged ETF trading impact on the market is greater than that of ordinary volatility of the underlying assets.

Published in: Economy & Finance
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
185
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Analysis of the Impact of Leveraged ETF Rebalancing Trades on the Underlying Asset Market Using Artificial Market Simulation

  1. 1. 1111 Analysis of the Impact of Leveraged ETF Rebalancing Trades on the Underlying Asset Market Using Artificial Market Simulation Kanagawa Institute of Technology SPARX Asset Management Co., Ltd. Isao Yagi Takanobu Mizuta 12th Artificial Economics Conference 2016 20-21 September 2016 in Rome It should be noted that the opinions contained herein are solely those of the authors and do not necessarily refrect those of SPARX Asset Management Co., Ltd. You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf Mail: mizutata[at]gmail.com HP: http://www.geocities.jp/mizuta_ta/ http://ae2016.it/public/ae2016/files/ssc2016_Mizuta.pdf(proceedings)
  2. 2. 2222 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf
  3. 3. 3333 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf
  4. 4. 4444 Leveraged ETF Actually, Leveraged ETF has to trade the index on daily to maintain its leverage (Rebalance). A Leveraged ETF is designed to deliver several times the return of its benchmark index (future), e.g. S&P 500, Euro Stoxx 50, FTSE 100, Nikkei 225. 4 60 70 80 90 100 110 120 130 140 0 1 2 3 4 5 6 7 8 9 101112131415 Value days Index (Future) Leveraged ETF (x2) ETF = Exchange Traded Funds is an investment fund traded on stock exchanges, much like stocks.
  5. 5. 5555 Rebalance to maintain its leverage buy the index when its price goes up sell the index when its price goes down For example, when the index returns are +10% on day 1 and -10% on day 2 5 This Rebalance makes Financial Market Unstable? Momentum Trading Index Leveraged ETF (x2) is Designed as Leveraged ETF (x2) Holding Index (future) Value Days (a) Return (b) Return = 2x(a) (c) Value Will be by (b) (d) Should have = 2x(c) (e) Will be by (a) (f) Rebalance = (d)-(e) 0 $100 $200 $200 1 +10% +20% $120 $240 $220 +20 2 -10% -20% $96 $192 $216 -24 +10% -10% +20% -20% So, leveraged ETF This leads The Leverage is twice (x2) x 2 x 2 x 2 This momentum trading Rebalance concern some people,
  6. 6. 66 Very Different Results about an Impact on the Financial Market For examples, Cheng and Madhavan(2009): “In USA market, the index moves 1% in one day, the volume of rebalancing trading of a leveraged ETF may account for 16.8% of total trading in the index at the close of trading on that day.” Deshpande et. al.(2009): “Leveraged ETFs accounted for a mere 0.0079% of trading in the S&P 500.” Trainor Jr. (2010): “Examined the impact of leveraged ETF rebalancing on the volatility of the S&P 500 but was unable to draw any firm conclusions.” Many Empirical Analysis Previous Studies for Leveraged ETF So many factors cause price formation. An empirical study cannot isolate the direct effect of leveraged ETFs. These shows In actual markets, there are
  7. 7. 77 We built an Artificial Market Model (Agent Based Model for Financial Market) to investigate the market impact of leveraged ETF rebalancing on the index price formation. • There are no previous study investigating the market impact of leveraged ETF using an Artificial Market Simulation. • Our model is based on the model of Yagi et. al. (2010). Strong Advantages: Artificial Market Simulations can Therefore, In this Study, isolate the direct effect of leveraged ETFs. analyze micro process and give us new knowledge. treat situations have never occurred. (e.g. There are more Leveraged ETFs than current)
  8. 8. 8888 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf
  9. 9. 99 Ground Design Index (future) Market e.g. S&P500, Euro Stoxx 50, FTSE 100, Nikkei225 Leveraged ETF Market Trading Normal Agents Rebalance Trading The Leveraged ETF Agent Trading This area is only modeled (treated) Not modeled (Not treated) 10,000 Only one
  10. 10. 1010 Fundamentalists We modeled Normal Agents as 3 kinds of agents, fundamentalists, Chartists and Noise traders. Normal Agents Exactly same as Yagi et. al.(2010) Chartists Noise Traders orders to buy, sell, and wait with equal probabilities. expects a positive return when the market price is lower than the Fundamental Price (externally given and constant), and vice versa. Market followers mode: an agent expects a positive return when historical market return (moving average) is positive, and vice versa. Contrarians mode: an agent expects a positive return when historical market return is negative, and vice versa. Learning Process Fundamentalists and Chartists learn some parameters of high-performance agents and switch mode. 4,500 4,500 1,000
  11. 11. Fundamental Price Market Price Chartists (Market followers mode) Fundamentalist * Fundamentalists Fundamental Price > Market Price  expect Positive return Fundamental Price < Market Price  expect Negative return * Chartists (Market followers mode) Historical return > 0  expect Positive return Historical return < 0  expect Negative return 11 Fundamentalists and Chartists
  12. 12. 1212 Price Determination Model Call Market (Batch Auction) 0 20 40 60 80 100 0 50 100 Price Cumulative number of Shares BUY (Bid) SELL (Ask) Market Price (Trade Price) Trading Volume Cumulative number of Shares that sellers want to sell over this price Cumulative number of Shares that buyers want to buy under this price In a call market, buy and sell orders are grouped together and then executed at specific times (rather than executed one by one continuously). We determine Market Price and Trading Volume at the crossing point of supply and demand curves. Supply Curve Demand Curve
  13. 13. 1313 Immediately Before leveraged ETF ordering The leveraged ETF ONLY does Rebalance Trading. The Leveraged ETF Agent Our Original Model The leveraged ETF orders after all Normal Agents ordered. It determines rebalance trading using the temporary market price, 50 (Left). If it need buying 20 shares, it makes buy 20 shares order at very high price. The order changes Supply and Demand Curve (Right). The market price is changed to 60 (from 50). The price difference, 10 is the market impact of the rebalance. 0 20 40 60 80 100 0 50 100 Price Cumulative number of Shares BUY (Bid) SELL (Ask) 50 0 20 40 60 80 100 0 50 100 Price Cumulative number of Shares BUY (Bid) SELL (Ask) 70 After it ordered 50 60
  14. 14. 14141414 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf
  15. 15. 151515 Search Parameter and the size is changed to 0.1%, 1%, 10%, 15%, 16%, 17% This is Search Parameter. Total Value of the leveraged ETF / Total Value of all Normal agents We investigated how the leverage ETF impacts market prices, relationship between size (total value) of the leverage ETF and the magnitude of the market impact. We executed simulations for several size of the leveraged ETF. We defined the size of leveraged ETF as Note that in our proceeding we tried on greater number of parameters, however here we only show important results for simplicity.
  16. 16. 161616 There is clearly the threshold for stable or unstable market between 15%-16% Volatility, stylized facts Size (Total Value of the leveraged ETF / Total Value of all Normal agents) NO Leveraged ETF 0.1% 1% 10% 15% 16% 17% Volatility(%) 1.13 1.18 1.4 1.47 1.39 6.42 29.5 Kurtosis 4.64 4.34 2.56 6.29 19.2 15.8 3.41 Lag 1 0.52 0.49 0.38 0.39 0.56 0.48 -0.49 Autocorrelation 2 0.71 0.70 0.72 0.64 0.45 0.48 0.79 of the squared 3 0.52 0.50 0.42 0.35 0.31 0.27 -0.55 rate of return 4 0.65 0.64 0.66 0.52 0.21 0.28 0.73 5 0.53 0.51 0.44 0.31 0.13 0.11 -0.60 Stable (small volatility) Unstable (large volatility) Volatility = Standard Deviation of Returns Kurtosis = Kurtosis for Return distribution
  17. 17. 17 Market is Destabilized when Market Impact > Volatility MIVR is very important Key Parameter MIVR < 1: Stable, MIVR > 1: Unstable Market Impact Volatility Ratio Size (Total Value of the leveraged ETF / Total Value of all Normal agents) NO Leveraged ETF 0.1% 1% 10% 15% 16% 17% No. of Orders 0 8 72 88 90 6,34157,617 (a) Market Impact (%) 0 0.00493 0.0396 0.0539 0.0785 2.18 12.5 Volatility (%) 1.13 (*1) 1.18 1.4 1.47 1.39 6.42 29.5 [MIVR] Market Impact Volatility Ratio = (a) / (*1) 0 0.00434 0.0349 0.0476 0.0692 1.92 11 Stable Very Small Impact Unstable Large Impact We defined the Market Impact Volatility Ratio (MIVR) = market impact (averaged absolute market impact / market price) / Volatility (in the case of no leveraged ETF)
  18. 18. 18 Possible Mechanism Prices remain within the Normal Volatility Range Stable Unstable: Destabilize Financial Market Market Prices Without Impact Market Prices Impacted Market Impact Amplified Volatility Fundamental Price Recovering to Fundamental Price > normal Volatility range Market Impact < Volatility Market Impact > Volatility Volatility the market impact do NOT amplify volatility market impact amplify volatility range within the normal volatility range Market Impact pushed out prices outer the normal volatility range Volatility Range wider
  19. 19. 19191919 (1) Introduction (2) Artificial Market Model (3) Simulation Results (4) Summary & Future Works You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf
  20. 20. 20202020 Summary * We built an artificial market model (a kind of agent based model) to investigate the impact of leveraged ETF rebalancing on the index price formation. * We found that Market Impact (MI) per Volatility (V) is very important Key Parameter, MI < V: Stable, MI > V: Unstable. * We showed the possible Mechanism of Destabilizing Market. Future Works * search the threshold more precisely, 15%-16% is true? * discuses Mechanism of Destabilizing Market more deeply * do Empirical Study focusing the relationship between Market Impact and Volatility
  21. 21. 2121212121 That’s all, thank you!! You can download this presentation material from http://www.slideshare.net/mizutata/20160921Slide Share .pdf http://www.geocities.jp/mizuta_ta/20160921.pdf References -- Proceedings of this presentation -- http://ae2016.it/public/ae2016/files/ssc2016_Mizuta.pdf -- Empirical Studies -- * M. Cheng and A. Madhavan, “The Dynamics of Leveraged and Inverse ExchangeTraded Funds,” Journal of Investment Management, vol. 7, no. 4, 2009. * M. Deshpande, D. Mallick, and R. Bhatia, “Understanding Ultrashort ETFs,” Barclays Captital Special Report, Jan. 2009. * W. J. Trainor Jr., “Do Leveraged ETFs Increase Volatility,” Technology and Investment, vol. 1, 2010. -- Artificial Market Model: Base Model -- * I. Yagi, T. Mizuta, and K. Izumi, “A study on the effectiveness of short-selling regulation in view of regulation period using artificial markets,” Evolutionary and Institutional Economics Review, vol. 7, no. 1, pp. 113–132, 2010. http://link.springer.com/article/10.14441%2Feier.7.113# -- Artificial Market Model: Brief Review focusing our studies -- * T. Mizuta, “A Brief Review of Recent Artificial Market Simulation (Multi-Agent Simulation) Studies for Financial Market Regulations and/or Rules,” SSRN Working Paper Series, 2016. http://ssrn.com/abstract=2710495

×