capital market research forum 4/2555 · 2006. 4. 28. · capital market research forum 4/2555 23...

Capital Market Research Forum 4/2555

23 March 2012

Thaisiri Watewai, Ph.D.Chulalongkorn Business School

Chulalongkorn University

Hedging Effectiveness of SET50 Index Futures:Empirical Studies and Policy Implications

Hedging Effectiveness of SET50 Index Futures:

Empirical Studies and Policy Implications

Thaisiri Watewai, Ph.D.

Chulalongkorn Business School

Chulalongkorn University

1

Motivations

• SET50 Index Futures

o Launched on April 28, 2006

o Adjust portfolio exposure to the index

o Effectiveness of managing the exposure depends on many factors:

• Correlation between the return of the futures and that of the index

• Liquidity cost

• Transaction cost (brokerage commission fees and taxes)

2

Motivations (cont.)

• Alternatives

o ThaiDEX SET50 Exchange Traded Fund (TDEX)

• Require full capital investment

• Short selling can be costly

o SET50 Index Options

• Highly illiquid

3

Motivations (cont.)

• SET50 index futures

o Pros

• Requires only margin deposits

• Cost of shorting the futures is small

• Liquid

o Cons

• Relatively large contract size

• Predetermined expiry date

• High transaction costs (?)

4

Main Findings

• Relatively small liquidity cost

• Relatively large transaction cost

• Significantly improve cost-adjusted Sharpe ratio

• Lower global minimum variance

• Improvement depends on ability to forecast market trend

5

Outline

• Literature Review

• Cost-Adjusted Mean-Variance Model

• Liquidity Cost Estimation

• Factor Model

• Hedging Effectiveness and Cost Contributions

• Extensions

• Policy Implications and Conclusions

6

Literature Review

• Hedging Effectiveness: Objectives

o Based on a given and fixed portfolio

o Determine the optimal hedge ratio

• Minimum Variance (Ederington; Johnson; Myers and Thompson)

• Mean-Variance (Cecchetti, Cumby and Figlewski; Howard and D’Antonio; Hsin, Kuo and Lee)

• Expected Utility Maximization (Benninga, Eldor and Zilcha)

• Mean Extended-Gini Minimization (Cheung, Kwan and Yip)

• Generalized Semivariance Minimization (De Jong, De Roon and Veld)

7

Literature Review (cont.)

• Hedging Effectiveness: Econometrics

o How to accurately estimate the hedge ratio

• OLS (Junkus and Lee)

• GARCH (Baillie and Myers)

• Random coefficient (Grammatikos and Saunders)

• Cointegration (Ghosh)

• Mean Extended-Gini (Kolb and Okunev)

• Generalized Semivariance (Lien and Tse)

8


• Hedging Effectiveness: Trading costs o Always ignore associated trading costs

o Few exceptions • Lence (1995, 1996)

• Brokerage fee and initial margin deposit

• Economic value of complicated estimation techniques for minimum variance hedge ratio is negligible

• Maybe optimal not to hedge at all

• Does not consider liquidity costs – Price impact: Chan and Lakonishok; Keim and Madhavan;

Sharpe et al.

9


• Contributions o Interaction between the use of futures and the

portfolio choice of stocks

o Include liquidity cost in addition to transaction cost • Asymmetric liquidity cost curve

10

Cost-Adjusted Mean-Variance Model

• Objective

o Maximize cost-and-risk adjusted mean return

(Mean - L Cost - T Cost) - Variance

• Universe

o Stocks in SET50 ,TDEX, SET50 index futures

• Budget:

11

Cost-Adjusted Mean-Variance Model (cont.)

• Decision variables

o Weight in stock :

o Risk-free weight :

o Futures weight : =

o Vector of weights :

12


• Mean - Variance

o Mean :

where : vector of mean returns

: risk-free rate

13

Returns of stocks,

futures, cash

Weights of stocks,

futures, cash

Portfolio Return


• Mean – Variance

o Variance :

where : covariance matrix of returns

14

Covariance of stocks,

futures, cash

Weights of

stocks, futures,

cash

Portfolio Variance

Weights of

stocks, futures,

cash


• Liquidity Cost

o Cost Asymmetric Liquidity Cost Curve

15

210 200

205 600

201 500

200

900 199

400 198

800 195

Total Cost

Trading Value


• Liquidity Cost

o Cost Asymmetric Liquidity Cost Curve

where : traded value

: liquidity cost parameter for buy

: liquidity cost parameter for sell 16


• Liquidity Cost

o Re-balance from to

o Percentage of Liquidity Cost : 17

Buy

Sell


• Transaction Costs

o Variable cost + Fixed cost

o Stocks and TDEX :

• = 0.25% + 7% VAT = 0.2675%

o Futures :

• = 250 + 7% VAT = 267.50 baht

18



o Re-balance from to

o Percentage of Transaction Cost :

19

Futures: per contract

Stocks: per traded value



o Pre-determined Expiry Date of Futures

20 31 March 12 February

Buy futures

T-cost

L-cost

Expiry date

T-cost



o Pre-determined Expiry Date of Futures

o Percentage of Transaction Cost :

21


• Formulation

o Objective:

o Constraints:

oNo cash-borrowing

oNo short-selling

o Limit stock concentration at 20%

o Limit position by trading value at 50%

oMaximum number of futures contracts at 20,000 contracts

oMargin deposit at 50,000 baht per futures contract

22

Liquidity Cost Estimation

• Data

o Intraday bid-ask prices of each stocks, TDEX and futures (SET, TFEX, Thomson Reuters)

o Sample 20 points for every 5 minutes from three best bid and ask prices

• Method

o Approximate the piecewise linear liquidity cost curve by two quadratic functions

23

Liquidity Cost Estimation (cont.)

• Example:

24


• Results :

25


• Results : By security

Ticker Estimate (x 10-8) Rank

PTT 0.42 0.28 1 1

PTTEP 0.52 0.42 2 2

BANPU 0.81 0.72 3 3

TOP 1.92 1.60 10 11

Futures 4.12 4.47 16 17

LH 6.65 6.14 20 21

TDEX 11.75 10.72 29 30

TRUE 21.96 19.12 37 40

MAKRO 49.54 37.28 51 52 26


• Forecasting

o Average of last 10 trading days as forecast of next day

o Forecasting performance:

27

Factor Model

• Multifactor model (Chincarini and Kim)

• Factor choices

o Market : 1

o Value : PE ratio

o Size : log(Market Capt)

o Momentum : past 12-month performance

o Recommendation : recommendation score 28

Factor Model (cont.)

• Data

o Thomson Reuters Datastream :

• Daily PEs, market capitalizations, total index returns, and recommendation scores (IBES) of stocks in the SET50 index from the database.

o Bloomberg :

• Daily total index return of TDEX and futures

o Thai BMA :

• Daily yield of one-month treasury bill

29


• Descriptive Statistics

o The market factor : most volatile

• standard deviation and excess kurtosis.

30


• Forecasting

o Mean :

where : factor mean

o One-year period with exponential weights

31

Factor Beta Expected Factor Return

Expected Stock Return


• Forecasting

o Variance : where : factor covariance

: residual covariance

o One-year period with exponential weights

32

Factor Beta

Factor Covariance

Stock Variance

Factor Beta

Hedging Effectiveness and Cost Contributions

• Setup

o Time period: January 2008 – December 2009

o Frequency: daily trading

o Initial budget : 1,000 million baht

• Analysis

o Ex-ante : expected returns before re-balancing

o Ex-post : realized returns

o Both are cost-and-risk adjusted 33

Hedging Effectiveness and Cost Contributions (cont.)

• Scenarios

o Futures not allowed :

• MV

o Futures allowed:

• Liquidity + Transaction costs : MV

• Liquidity cost : MV

• Transaction cost : MV

• No cost : MV

o Always include liquidity and transaction costs of stocks and TDEX

34


• Performance Analysis

o Cost-adjusted mean-variance frontier

where : portfolio’s Sharpe ratio

o Liquidity cost

o Transaction cost

35

Return Risk


• Results: Ex-ante MV frontier

36


• Results: Ex-post MV frontier

37


• Results: Ex-post liquidity costs

38


• Results: Ex-post transaction costs

39


• Results: Ex-post cumulative returns and weights

40

Extensions

• Two extensions

o Naïve forecasting model

• One-year equally weighted sample means for

• Five-year equally weighted sample covariances for

o Minimum stock holdings (LTF) • Minimum of 65% in stocks

41

Extensions (cont.)

• Naïve forecasting model : Ex-ante MV frontier

42

Extensions (cont.)

• Naïve forecasting model : Ex-post MV frontier

43

Extensions (cont.)

• Minimum stock holdings

o MV frontier

where : expected value at min global variance

: std deviation at min global variance

: min global variance Sharpe ratio

o LTF : 65% minimum stock holdings 44

Extensions (cont.)

• Minimum stock holdings : Ex-ante MV frontier

45

Extensions (cont.)

• Minimum stock holdings : Ex-post MV frontier

46

Extensions (cont.)

• Minimum stock holdings : Ex-post liquidity costs

47

Extensions (cont.)

• Minimum stock holdings : Ex-post transaction costs

48

Extensions (cont.)

• Minimum stock holdings : Ex-post weights

49

Policy Implications and Conclusions

• Significant improvements on both ex-ante and ex-post Sharpe ratio (given ability to forecast market trend)

* Minimum global variance Sharpe ratio

MV MVFLT Increase

Factor model

Ex-ante 0.241 1.940 1.699

Ex-post 0.070 1.069 0.999

Naïve model

Ex-ante 0.470 0.842 0.372

Ex-post 0.012 -0.299 -0.311

Min stock holdings*

Ex-ante 0.4275 1.9659 1.5384

Ex-post 0.2380 1.3014 1.0634

50

Policy Implications and Conclusions (cont.)

• Lower global minimum variance on both ex-ante and ex-post bases

• Relatively small liquidity cost

• Relatively large transaction cost

MV MVFLT Decrease

Min stock holdings

Ex-ante 0.1917 0.0899 0.1018

Ex-post 0.2010 0.0564 0.1446

51


• Current market structure and liquidity for the SET50 index futures well facilitate investors with large portfolio values (1,000 million baht)

• Realized benefits depend also on the ability to forecast the market trends, and constraints faced by fund managers

52


• Fund managers must understand the role of the futures in improving the risk-adjusted performance

• Do not be misled by the fact that using a short position of the futures to hedge the market risk may reduce the realized return during the market upturn

53

capital market research forum 4/2555 · 2006. 4. 28. · capital market research forum 4/2555 23...

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