Doctoral School of Finance and Banking July 2008

1. Doctoral School of Finance and Banking July 2008 Lead – Lag Relationship between the Romanian Cash Market and Futures Market MSc Student: Streche Lucian Supervisor: Moisă Altăr Ph.D.

2. Topics • Motivation • Literature review • Data • Top – down approach • Bottom – up approach • Conclusions and relevance

3. Motivation • Pragmatic approach to econometrics • Value of result & immediate validation • Better understanding of the Romanian markets • Forecast value - both short term and long term • Relevant information in trading: price formation

4. Literature Review Multitude of approaches, different periods, same markets (S&P 500) • Kawaller, Koch and Koch 1987 • Stoll and Whaley 1990 • Chan 1992 • Tse, Bandyopadhyay and Shen 2006 Constant result: Futures market leads Cash market Main difference: Temporal correlation

5. Basic relation between Futures market & Cash market • Perfectly correlated if: • if interest rates and dividend yields were non-stochastic. • Same price if: • trading costs and markets response were identical. • Futures • Hedging • Arbitrage • Trading • Futures Market • Market sentiment • Arbitrage trading • Hedging • Cash Market Usually: interest rate > dividend => futures price > equity price

6. Data processing • Why SIF2 and SIF5? • Largest listed investment funds • Largest trading volume (BVB & BMFMS) • Almost market portfolio (371, 257 invested companies) Available data – all market transactions from August 2007 to March 2008 Aggregation (after schedule matching): • Why this period? • liquidity • correlation/subprime • Why this aggregation method? • hidden/testing orders • futures market-orders

7. Data statistics • Minute data series: • Five minutes data series (different aggregation):

8. Top – Down Approach • Targets: high relevance (long period), comprehensive analysis • Only purpose: to estimate the intraday relation between listed equity prices and futures prices • Inputs: long data series (eight months), high frequency • Model used Chan(1992): • Newey-West Heteroskedasticity & Autocorr. Consistent Covariances • Disadvantages: • subjected to many perturbations and market conditions • doesn’t “explain” very well the dependent variable • infrequent trading and bid/ask spread not treated explicitly

9. High frequency data SIF2/DSIF2 • Observations: • contemporaneous coefficient • linear decrease of coefficient value (log scale) • lag coefficient (correction/rebound; feed-back) • cash leads futures by 3 minutes • futures leads cash by 1 minute

10. High frequency data SIF5/DSIF5 • Problems: • smaller coefficients • small relevance of 6th coeff. • Differences: • price SIF5 > price SIF2 hence more levels are used • DSIF 2 has greater liquidity than DSIF5 • lead extends to 5 minutes

11. Medium frequency data Five minutes data series (different aggregation) • Reason: • correction/rebound effect & aggregation • lead coefficient Chan (1992): for 1985 futures lead cash by 15 min. / for 1987 lead reduced to 5 min. Cause: Romanian futures trader strategy (speculation)

12. Behavior under bad news Data used: 1st and 5th quintiles from five minute series. 85 minutes grouping. • Observations: • large contemporaneous coefficients (data integration speed) • SIF 5 regressions: faster reaction caused by volume

13. Behavior under good news • Conclusions: • for both SIFs the lead remains the same • short – sale constraints have no influence • Chan (1992) finds that there is no difference between bad news and good news • (five minutes for 1987 data). • Results hold also for the Romanian markets.

14. Lead-Lag relation under heavy trading Data series obtained from the five minutes series. Three levels of trading. 85 minutes intervals • Results hold very well for high levels of trading. For SIF2 they hold across the board. • relevant coefficients decrease with the volume • lack of strong information content, at times, makes the coeffs. smaller than under news • at high volume information is integrated very fast into the price. • Chan finds that the lead-lag relation is not affected by different intensities of trading.

15. Lead-Lag relation under medium and low trading

16. Bottom – up approach • Targets: better accuracy (short period), extension to a large period • Only purpose: to estimate the intraday relation between listed equity prices and futures prices • Inputs: short data series (1-2 days), high frequency • Model used Kawaller, Koch & Koch (1987): • Three stage least squares estimation (simultaneous equations). • Disadvantages: • need for many data series to be tested for generalization • futures coefficients changed by the inclusion of cash lags 16

17. High volume trading, bull market Data: 2 days, minute Lead: 3-5 minutes Result in perfect agreement with first model

18. High volume trading, bull market Granger causality

19. High volume trading, bear market Data: 2 days, minute Lead: 2-3 minutes Sif2: feed-back effect and correction/rebound

20. Random days, medium trading volume Three random days, medium volume

21. Conclusions • Conclusions: • the 3 to 5 minutes lead proven using 2 models, 2 stocks, different data frequency, • variable trading volume, variable news, bear and bull market • most information is integrated simultaneously in both markets • Romanian market resembles US markets from the late 80’s

22. Relevance • Relevance: • high level result: data used incorporates al influences & perturbations • traders – gives important information about markets behavior • assessment of local market and investor maturity • market regulators – usage of futures • Future directions of research: • analysis of higher frequency data • study of a large temporal interval using a second model approach • analysis taking into account seasonality, foreign investor level, …

23. Thank You