1. Economics 201FS: Volatility and Jumps Grace Shuting Wei Spring 2011 20 April 2011 1

2. Investigating volatility during jumps Previously – BNS test – Ait-Sahalia and Jacod (2008) This week – Regression of test statistics from Ait-Sahalia and Jacod – Direction of jumps 2

3. Ait-Sahalia and Jacod (2008) Multipower variation Test statistic Intuition: When power is large (p >2), the contribution of jumps to B(p) overwhelms everything else. This is because high powers (p >2) magnify the large increments at the expense of the small ones. Asymptotic values 3

4. FDX: A-J Jump Test 4 InterceptCoefficientP-val 1P-val 2 RV0.4888-0.01460.00000.4582 BV0.4454-0.00070.00000.9724 MedV0.4825-0.01280.00000.5070 MinV0.4784-0.01150.00000.5500

5. UPS: A-J Jump Test 5 InterceptCoefficientP-val 1P-val 2 RV0.24420.05320.00000.0044 BV0.18950.07370.00040.0001 MedV0.21640.06430.00010.0008 MinV0.21490.06480.00010.0007

6. SPFU: A-J Jump Test 6 InterceptCoefficientP-val 1P-val 2 RV0.15690.15460.0000 BV0.18220.14700.0000 MedV0.19090.14430.0000 MinV0.20070.14100.0000

7. Bollerslev, Todorov, and Zheng (2011) Time-of-Day measures the ratio of the diffusive variation over different parts of the day relative to its average value for the day. Threshold type test 7

8. FDX: BTZ Jump Test 8 RV Positive25.3034 Negative24.8800 Cont24.9167 CV Positive21.0427 Negative20.6837 Cont22.0098

9. UPS: BTZ Jump Test 9 RV Positive26.3374 Negative18.9684 Cont25.2703 CV Positive15.7311 Negative15.6091 Cont16.5947

10. SPFU: BTZ Jump Test 10 RV Positive14.4031 Negative14.2236 Cont14.9952 CV Positive12.3647 Negative12.2311 Cont13.7115