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Final Presentation: Jump statistics and volume

Final Presentation: Jump statistics and volume. Econ 201 FS April 22, 2009 Pat Amatyakul. Last time. Regressed jump statistics on daily volume for the BNS test, Jiang-Oomen test, and Ait-Sahalia Jacod test.

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Final Presentation: Jump statistics and volume

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  1. Final Presentation: Jump statistics and volume Econ 201 FS April 22, 2009 Pat Amatyakul

  2. Last time • Regressed jump statistics on daily volume for the BNS test, Jiang-Oomen test, and Ait-Sahalia Jacod test. • Note that for the stocks where the value is statistically significant, BNS and Ait-Sahalia test yields a positive relationship while Jiang Oomen yiled a negative relationship

  3. This time • Plotted out the Jiang-Oomen test statistic to see why the relationship is different • Revise coding • Regress volume on the jump statistics, as well as the lag of volume

  4. Volume vs. Day of the week revisited JNJ KO PG T

  5. Jiang Oomen swap variance ratio jump test • The assumption here is that the swap variance should equal the realized variance if no jumps are detected • Swap variance is defined as: • The test statistic is

  6. One sample plot of the test statistic

  7. Jump detection • This is a two-sided jump test. These are the percentage of jumps detected at the 95% confidence level

  8. Redoing the simple jiang regression • Regressing the absolute value of the jiang statistics on volume

  9. Rethinking the regression • Volume clustering tend to occur, that is, volume today tend to affect volume tomorrow so I included a few lag volume terms into the regressors • Volume on Monday seemed to be lower than every other day of the week, so I included that into my regressors • Made some minor adjustment from last time to make sure that the signs of the coefficient means the same thing in all of the three jump statistics

  10. The regression • The regression is as follows • Where the stat is either the BNS z-stat, the absolute value of the Jiang-Oomen z-stat, and -ASJ variable for the Ait-Sahalia Jacod test • Monday is a 0 or 1 dummy variable

  11. Summary of results • The correlation between volume and its lag term seems quite high and significant • BNS test does not yield any conclusive results, only 2/10 are significant and it is a split between a positive correlation and negative correlation • For the JO test, 5/10 are significant and 4 showed a negative relationship and 1 showed a positive relationship. • For the Ait-Sahalia Jacod test, 9/10 are significant and all showed a negative relationship between volume and jump statistics

  12. Interpretation • According to Tauchen and Pitts (1983), changes in prices and volume are related • Need to investigate how this is related to each test statistics, since the change in prices provide the basis for calculating all the test statistics

  13. Applications • In general, at least for JO and ASJ tests, lower volume corresponds with higher chance of jump days • Since volume is an easy indicator to observe in the market, one could flag an especially low volume day to possibly correspond with a jump. This would work only for the ASJ test, because it seems like the coefficient in the JO test regression are rather small. • Might be able to somehow incorporate this into asset pricing

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