Algorithmic Traders And Volatility Information Trading

1y ago
64 Views
14 Downloads
229.78 KB
24 Pages
Last View : 6d ago
Last Download : 9m ago
Upload by : Rafael Ruffin
Transcription

Algorithmic Traders and Volatility InformationTradingNSE-NYU Conference on Indian Financial MarketsAnirban Banerjee1 Asst.1Ashok BanerjeeProfessor, IIM Kozhikode2 Professor,IIM CalcuttaDecember 11, 20192

IntroductionI Algorithmic traders presently provide bulk of the tradingvolume in stock exchanges around the globe - both indeveloped and developing markets.I Role of algorithmic traders is far from clear (especially HFTs).I Academic literature on algorithmic trading is primarilyconcentrated on equity markets, that too using data ofdeveloped markets only.I Lack of identifiers for algo tradersI Fragmented market structureI Benefit of using NSE data

MotivationI Do algorithmic traders have information regarding futurevolatility?I Options market is uniquely suited for utilizing privateinformation regarding future realized volatility.I In case of volatility information, the direction of future pricemovement is not known to the trader.However, the trader isbetter informed to predict if the price level is supposed tomove from its current level (in either direction).

LiteratureI Most of the studies suggest that algorithmic traders do not have directionalinformation, but react much faster to publicly available information (Frino,Viljoen, Wang, Westerholm, & Zheng,2015)I There is a large body of existing literature inspecting whether informed tradersuse directional information market in the options market (Stephan & Whaley,1990; Amin & Lee, 1997; Easley, Hara, & Srinivas, 1998; Chan, Chung, & Fong,2002; Chakravarty, Gulen, & Mayhew, 2004; Cao, Chen, & Griffin, 2005; Pan &Poteshman, 2006).I Ni et al. (2008) show that Vega-adjusted net trading volume can be used tomeasure volatility demand for a particular trader group. They also show thatnon-market maker’s demand for volatility is positively related to future realizedvolatility in the spot market.

Algorithmic TradingTable: Proportions of trading volume contributed by different category of algorithmicand non-algorithmic traders in the NSE spot and equity derivatives segment (for theperiod Jan-Dec %9.50%36.90%50.19%49.81%Spot MarketAlgoNon-AlgoStock FuturesAlgoNon-AlgoStock OptionsAlgoNon-Algo

Volatility Information Trading by Algo TradersHypothesisIn an order-driven market, non-algorithmic traders’ demand for volatility in the stockoptions market is positively related to future realized volatility in the spot market.HypothesisInvestors trading on volatility related information in the stock options market behavesimilarly in periods leading up to both scheduled and unscheduled corporateannouncements.

Demand for VolatilityDemand for volatility estimated following Ni.et. al(2008),as the vega weighted nettraded volume in the options market.σ D TGi,tK ,TX X lnCi,tK σi,tTK ,TK ,T(BuyCall TGi,t SellCall TGi,t)K ,TX X lnPi,tKT σi,t(1)K ,TK ,T(BuyPut TGi,t SellPut TGi,t)K ,TK ,TPartial derivative estimated through (1/Ci,t).BlackScholesCallVegai,tandK ,TK ,T(1/Pi,t).BlackScholesPutVegai,t.

Empirical ModelσσOneDayRVi,t α β1 .D TGi,t j β2 .D TGi,t j .EADi,t β3 .OneDayRVi,t 1 β4 .OneDayRVi,t 1 .EADi,t β5 .OneDayRVi,t 2 β6 .OneDayRVi,t 2 .EADi,t β7 .OneDayRVi,t 3 β8 .OneDayRVi,t 3 .EADi,t β9 .OneDayRVi,t 4 β10 .OneDayRVi,t 4 .EADi,t β11 .OneDayRVi,t 5 β12 .OneDayRVi,t 5 .EADi,t β13 .EADi,t β14 .IVi,t 1 β15 .IVi,t 1 .EADi,t β16 .abs(D TGi,t j ) β17 .abs(D TGi,t j ).EADi,t β18 .ln(optVolumei,t j ) β19 .ln(optVolumei,t j ).EADi,t β20 .ln(stkVolumei,t j ) β21 .ln(stkVolumei,t j ).EADi,t i,t(2)σσOneDayRVi,t α β1 .D TGi,t j β2 .D TGi,t j .UADi,t β3 .OneDayRVi,t 1 β4 .OneDayRVi,t 1 .UADi,t β5 .OneDayRVi,t 2 β6 .OneDayRVi,t 2 .UADi,t β7 .OneDayRVi,t 3 β8 .OneDayRVi,t 3 .UADi,t β9 .OneDayRVi,t 4 β10 .OneDayRVi,t 4 .UADi,t β11 .OneDayRVi,t 5 β12 .OneDayRVi,t 5 .UADi,t β13 .UADi,t β14 .IVi,t 1 β15 .IVi,t 1 .UADi,t β16 .abs(D TGi,t j ) β17 .abs(D TGi,t j ).UADi,t β18 .ln(optVolumei,t j ) β19 .ln(optVolumei,t j ).UADi,t β20 .ln(stkVolumei,t j ) β21 .ln(stkVolumei,t j ).UADi,t i,t(3)

DataI We use six months (01 Jan 2015 to 30 Jun 2015) of optionsmarket trading data obtained from the NSE for 159 stocks.I Our dataset contains information regarding 37 milliontransactions in the options market during the period of 120trading days.I Implied volatility estimated using an optimization exercisewith options traded price and the Black-Scholes optionspricing model.

Volatility DefinitionI Def. 1 [Daily volatility reported by NSE] :rClose2σi,t,NSE 0.96 σi,t 1,NSE 0.04 (ln Openi,t )2i,tI Def. 2 [Anderson(2001)] : σi,t,Anderson qPI Def. 3 [Alizadeh et. al. (2002)] : σi,t,Range ntk 1(rk,t )2Highi,t Lowi,tClosei,t

Volatility Spikes around Announcements(a) Volatility Estimate (NSEReported)(b)Volatility Estimate (Andersonet. al. 2001)(c)Volatility Estimate (Alizadehet. al. 2002)Figure: The figure plots average realized volatility around scheduled earningsannouncements. The x-axis represents the time line around the pre-scheduled earningsannouncement. 0 represents the earnings announcement date. negative values indicatetrading days prior to announcement and positive values indicate trading days postannouncement.

Volatility Spikes around Announcements(a) Volatility Estimate (NSEReported)(b)Volatility Estimate (Andersonet. al. 2001)(c)Volatility Estimate (Alizadehet. al. 2002)Figure: figure plots average realized volatility around unscheduled corporateannouncements. The x-axis represents the time line around the corporateannouncement. 0 represents the announcement date. negative values indicate tradingdays prior to announcement and positive values indicate trading days postannouncement.

Results - Algo (Earnings Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Algo TraderAnnouncement Type: Pre-scheduled Earnings Announcement(NSE 0.3*(-1.9)(Anderson et. al. 2001)(t-j)(t-j)*EAD(Alizadeh et. al. 7(-0.93)-0.02(0.00)

Results - Algo (Unscheduled Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Algo TraderAnnouncement Type: Unscheduled Announcements(NSE Anderson et. al. 2001)(t-j)(t-j)*EAD(Alizadeh et. al. *(-3.06)0.43(0.13)-8.75***(-2.68)

Results - Non Algo (Earnings Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Non-Algo TraderAnnouncement Type: Pre-scheduled Earnings Announcement(NSE *(2.35)0.41**(2.02)0.35**(2.11)0.3*(1.9)(Anderson et. al. 2001)(t-j)(t-j)*EAD(Alizadeh et. al. 0(-0.34)5.56(1.37)3.07(0.93)0.02(0.00)

Results - Non Algo (Unscheduled Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Non-Algo TraderAnnouncement Type: Unscheduled Announcements(NSE 55)0.29(1.51)-0.26(-1.05)-0.18(-0.81)(Anderson et. al. 2001)(t-j)(t-j)*EAD(Alizadeh et. al. )

Behavior of the Different Algorithmic Trader CategoriesI Prop algo traders primarily engage in HFT, try to exploit any arbitrageopportunity existing in the market. Not known to trade on information.I Agency algorithmic traders provide trade execution service on someone else’sbehalf. Splits orders from possible informed investors to small pieces.Information content of large orders may be lost.HypothesisTrades executed by both propitiatory and agency algorithmic traders in the stockoptions market do not convey private information regarding future realized volatility inthe spot market.

Results - Prop Algo (Earnings Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Prop-Algorithmic TraderAnnouncement Type: Pre-scheduled Earnings AnnouncementNSE -1.27)Anderson et. al. 2001(t-j)(t-j)*EADAlizadeh et. al. .46)

Results - Prop Algo (Unscheduled Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Prop-Algorithmic TraderAnnouncement Type: Unscheduled AnnouncementNSE nderson et. al. 2001(t-j)(t-j)*EADAlizadeh et. al. (-6.18)16.08***(3.33)-8.51**(-2.01)

Results - Agency Algo (Earnings Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Agency-Algorithmic TraderAnnouncement Type: Pre-scheduled Earnings AnnouncementNSE 03)Anderson et. al. 4)-1.38(-0.79)-5.03***(-4.25)-0.96(-0.92)Alizadeh et. al. )-1.11(-0.14)-5.71(-1.08)-2.04(-0.43)

Results - Agency Algo (Unscheduled Announcement)Table: Coefficients corresponding to the demand for volatility termTrader Group: Agency-Algorithmic TraderAnnouncement Type: Unscheduled AnnouncementNSE on et. al. 2001(t-j)(t-j)*EADAlizadeh et. al. (4.32)-45.66***(-9.05)-29.14***(-3.2)

SummaryI Non-algorithmic traders are informed regarding future volatilitywhile algorithmic traders are not.I The predictive ability of options market volatility demand rarelylasts more than two days into the future.I Neither propitiatory (who trade in their own account) nor agency(who execute trades on someone else’s behalf) algorithmic tradershave volatility related information.I Both scheduled and unscheduled corporate announcements act asexogenous shocks, resulting in volatility spikes. Traders behavesimilarly in periods leading up to both these type of corporateannouncements.

RobustnessEstimation of VegaI We revise our estimate by calculating the Vega using 20-dayrolling realized volatility measure based on the Anderson(2001) measure. Updated results based on this measure aresimilar.Functional form of spot and stock traded volume volumeI We have revised both the volume measures - stock and optiontraded volume to logarithmic scale as per suggestion.

RobustnessUse of alternate range based estimators of realized volatilityTable: Pearson correlation coefficient for the six measures of realized volatility.Pearson Correlation CoefficientsNSE ReportedAlizadehAndersonGarman KlassRogers 8271.000

Algorithmic Trading Table: Proportions of trading volume contributed by di erent category of algorithmic and non-algorithmic traders in the NSE spot and equity derivatives segment (for the period Jan-Dec 2015) Custodian Proprietary NCNP Total Spot Market Algo 21.34% 13.18% 7.76% 42.28% Non-

Related Documents:

1.2.8 Volatility in terms of delta 11 1.2.9 Volatility and delta for a given strike 11 1.2.10 Greeks in terms of deltas 12 1.3 Volatility 15 1.3.1 Historic volatility 15 1.3.2 Historic correlation 18 1.3.3 Volatility smile 19 1.3.4 At-the-money volatility interpolation 25 1.3.5 Volatility

Rich Traders carefully control trading size. 8. For New Traders huge profits are the #1 priority; for Rich Traders managing risk is the #1 priority. 9. New Traders try to prove they are right; Rich Traders admit when they are wrong. 10. New Traders give back profits by not having an exitFile Size: 1MB

Good Volatility, Bad Volatility and Option Pricing . by Bruno Feunou and Cédric Okou . 2 Bank of Canada Staff Working Paper 2017-52 . December 2017 . Good Volatility, Bad Volatility and Option Pricing by Bruno Feunou 1 and Cédric Okou 2 1 Financial Markets Department

ratio portfolios with volatility derivatives. Intuitively, one expects that a portfolio strategy mixing a well-diversified equity benchmark and a suitably designed long exposure to volatility through trading in volatility index futures and/or volatility index options can be enginee

Volatility Strategies How to profit from interest rate volatility . Source: Ardea Investment Management, Bloomberg. 5 These dynamics of abnormally low market pricing of interest rate volatility and compressed volatility risk premia used to be rare but are now becoming more common. Just as risk premia have shrunk in other

pricing certain kinds of exotic and structured products. keywords: volatility of volatility, variance derivatives, exotic options, structured products. 0.1 Introduction It is intuitively clear that for exotic products that are strongly dependent on the dynamics of the volatility surface pro

One of the fastest, most efficient trading platforms in the world, offering complex trading and price improvement auctions with industry-leading risk control features. Powered by Volatility, reimagined. 30-day volatility The SPIKES Volatility Index (index symbol: SPIKE) is a measure of the expected 30-day volatility in the SPDR S&P 500 ETF .

“algorithmic” from “software” has been also reflected in media studies. [2] “Drawing on contemporary media art practices and on studies of visual and algorithmic cultures, I would like to develop the idea of algorithmic superstructuring as a reading of aest

aforementioned model. Thus, the Triangulation Algorithmic Model is provided and defined to aid in understanding the process of measurement instrument construction and research design. The Triangulation Algorithmic Model for the Tri-Squared Test The Algorithmic Model of Triangulation is of the form (Figure 2). Where,

In these notes we focus on algorithmic game theory, a relatively new field that lies in the intersection of game theory and computer science. The main objective of algorithmic game theory is to design effective algorithms in strategic environments. In the first part of these notes, we focus on algorithmic theory's earliest research goals—

Jan 07, 2016 · to resume trading after an event —there may be scope for firms to wind down their operations instead. Market making strategy MiFID II imposes obligations on algorithmic traders when they pursue a market making strategy. A person engaged in algorithmic trading will be consid

and demanded inspired master traders to reach out to all of us newbie traders via websites and books. To say wisdom, training, and advice from these master traders was necessary would be an understatement. Afte

Aaron here—host of the Chat With Traders podcast. Thanks for grabbing a copy of this guide. In 2015 I asked 17 traders to complete the statement above and presented it to you as a guide, it was a hit and it has since been read by thousands. I’ve now asked another 23 traders to do the same. Their insight can be found on the following pages.

4 A System Built for Traders by Traders 4.1 The Day Trading Academy Master Traders 4.2 How Hundreds of Traders Have Found Profitability Through 5 Welcome To The Program 5.1 Our Trading Program 5.2 The Day Trading Academy Style of Trading 5.3 Members Area Preview 5.4 Annual ro

Mindset Of The Millionaire Traders The world’s top traders may all have different methods for making money but they all tend to share the same personality traits that make them great traders. To be a successful trader you must approach the markets with the right attitude. Here, I have isolated ten key traits that the world’s

The Effect of Algorithmic Trading on Liquidity in the Options Market Abstract Algorithmic trading consistently reduces the bid-ask spread in options markets, regardless of firm size, option strike price, call or put option, or volatility in the markets. Howeve

The impact of exchange rate and oil prices fluctuation on the stock market has been a subject of hot debate among researchers. This study examined the impact of both the exchange rate volatility and oil price volatility on stock market volatility in Nigeria, so as to guide policy formulation based on the fact that the nation’s economy

Short Volatility Trading with Volatility Derivatives. Russell Rhoads, CFA. 2. Options involve risk and are not suitable for all investors. Prior to buying or selling an option, a person . The multiplier for VIX Options is 100 and trading is available during both European and US market hours VIX Options

Andreas Wagner { Integrated Electricity Spot and Forward Model 16/25. MotivationFrameworkModel and ResultsConclusions Volatility of supply-functional This observation motivates the following volatility structure (as in Boerger et al. [2009]) Volatility structure ( ;t) e (t ) 1; 2(t) ; where 1 is the (additional) short-term volatility, is a positive constant controlling the in .

The American Revolution DID inspire other revolutions to follow. French Revolution (1789-1799) –partly because France was broke after helping us (and we broke our alliance partly thanks to George Washington’s advice against “entangling alliances”) Haitian Revolution (1791-1804) Mexican War of Independence (1810-1821)