Upload
ohio
View
21
Download
1
Tags:
Embed Size (px)
DESCRIPTION
Shanghai University for Science and Technology. China-EU Summer School on Complexity Sciences. Universal price impact functions of individual trades in an order-driven market. Wei-Xing ZHOU East China University of Science and Technology 14 August 2010. Outlines. 1. Order-driven markets - PowerPoint PPT Presentation
Citation preview
1
China-EU Summer School on Complexity Sciences
Universal price impact functions of Universal price impact functions of individual trades in an order-driven individual trades in an order-driven
marketmarketWei-Xing ZHOUWei-Xing ZHOU
East China University of Science and East China University of Science and TechnologyTechnology
14 August 201014 August 2010
Shanghai University for Science and Shanghai University for Science and TechnologyTechnology
2
OutlinesOutlines
1. Order-driven markets1. Order-driven markets
2. LFM scaling with NYSE data2. LFM scaling with NYSE data
3. LC scaling with ASE data3. LC scaling with ASE data
4. New scaling with Chinese data4. New scaling with Chinese data
5. Summary5. Summary
3
Order-driven marketOrder-driven market
4
Cancelation of all orders at the best ask Cancelation of all orders at the best ask or bidor bid
Submission of an order inside the Submission of an order inside the spreadspread
All partially filled orders (market orders)All partially filled orders (market orders)Some filled orders (market orders)Some filled orders (market orders)
Which events move the price?Which events move the price?
5
Market orders vs. Limit ordersMarket orders vs. Limit orders
Buy orders vs. Sell ordersBuy orders vs. Sell orders
Filled orders vs. Partially filled orderFilled orders vs. Partially filled order
Classification of ordersClassification of orders
6
• Mid-price at time t:Mid-price at time t:
• Immediate price impact is defined as Immediate price impact is defined as the relative change of mid-price right the relative change of mid-price right before and after the transaction:before and after the transaction:
Immediate price impactImmediate price impact
7
• Volume-volatility relation:Volume-volatility relation: vs. vs. • Volume-return relation:Volume-return relation: vs.vs.
Volume-price relationshipVolume-price relationship
Karpoff, J. Fin. Quant. Analysis 22 (1987) 109-126.
8
New York Stock ExchangeNew York Stock Exchange
Lillo, Farmer & Mantegna, Master curve for price-Lillo, Farmer & Mantegna, Master curve for price-impact function, impact function, NatureNature 321 (2003) 129-130. 321 (2003) 129-130.
TAQ of 1000 largest stocks on NYSE (1995-1998)TAQ of 1000 largest stocks on NYSE (1995-1998)
vs.vs.
SourceSource
Date setsDate sets
VariablesVariables
9
20 Portfolios grouped with Cap20 Portfolios grouped with Cap
10
LFM scalingLFM scaling
11
LFM scaling in Chinese data?LFM scaling in Chinese data?
NOT satisfactory!!!
12
Australian Stock ExchangeAustralian Stock Exchange
Lim & Coggins, The immediate price impact of trades Lim & Coggins, The immediate price impact of trades on the Australian Stock Exchange, on the Australian Stock Exchange, Quantitative FinancQuantitative Financee (2005) 365-377. (2005) 365-377.
300 constituent stocks of S&P asx 300 index traded on 300 constituent stocks of S&P asx 300 index traded on the ASE (2001-2004)the ASE (2001-2004)
vs.vs.
Normalized daily-normalized trade sizeNormalized daily-normalized trade size
SourceSource
Date setsDate sets
VariablesVariables
13
10 Portfolios grouped with Cap10 Portfolios grouped with Cap
14
LC scalingLC scaling
15
LC scalingLC scaling
16
LC scalingLC scaling
17
LC scalingLC scaling
18
LC scaling in Chinese data?LC scaling in Chinese data?
NOT satisfactory!!!
19
Shenzhen Stock ExchangeShenzhen Stock Exchange
Zhou, Universal price impact functions of Zhou, Universal price impact functions of individual trades in an order-driven market, individual trades in an order-driven market, Quantitative FinanceQuantitative Finance (2010) to appear. (2010) to appear.
23 constituent stocks of SZSE component index 23 constituent stocks of SZSE component index traded on the SZSE (2003)traded on the SZSE (2003)
vs.vs.
SourceSource
Date setsDate sets
VariablesVariables
20
23 SZSE stocks23 SZSE stocks
21
LFM scaling in Chinese data?LFM scaling in Chinese data?
NOT satisfactory!!!
22
LC scaling in Chinese data?LC scaling in Chinese data?
NOT satisfactory!!!
23
Simple scaling for buy ordersSimple scaling for buy orders
24
Simple scaling for sell ordersSimple scaling for sell orders
25
No buy-sell asymmetryNo buy-sell asymmetry
Slope = 2/3
26
Anomalous hook explainedAnomalous hook explained
27
SummarySummary
Simpler scaling form without additional variableSimpler scaling form without additional variable
Partially filled orders have greater price impactPartially filled orders have greater price impact
No buy-sell asymmetry at the transaction levelNo buy-sell asymmetry at the transaction level
Anomalous volume-return relation explainedAnomalous volume-return relation explained
28
Thank you for your attention!