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Motivational Mathematics (skip) Data Information (skip) Graphing prices

Motivational Mathematics (skip) Data Information (skip) Graphing prices Motivation for my research Correlation in stock prices Correlation in returns Factor Analysis Z-stats RV RV-BV Extensions. - r t,j is log return, M is total # of observations per day. Realized Variance

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Motivational Mathematics (skip) Data Information (skip) Graphing prices

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  1. Motivational Mathematics (skip) • Data Information (skip) • Graphing prices • Motivation for my research • Correlation in stock prices • Correlation in returns • Factor Analysis • Z-stats • RV • RV-BV • Extensions

  2. -rt,j is log return, M is total # of observations per day • Realized Variance • Realized Bi-Power Variation

  3. Sampled at the 5-minute frequency • Sampled from 9/3/2002 to 12/31/07 for 1323 total observed days • Oil futures data at the 5-min frequency, from 1987 • Changing number of observations per day • Different trading days than equity stocks • Ticker Symbols • XOM—Exxon Mobile • CVX—Chevron Oil • COP—Conoco Phillips

  4. XOM:29 CVX:41 COP:38

  5. -Correlation between 5-minute prices -XOM had 29 jumps out of 1343 days observed; 6 of which were shared by either CVX or COP -CVX had 41 jumps out of 1343 days observed; 4 of which were shared by either XOM or COP -COP had 38 jumps out of 1343 days observed; 6 of which were shared by either CVX or XOM

  6. -High degree of correlation between the stock returns, especially between CVX and COP

  7. -Not great correlation between any of the stocks and oil returns -Questionable return for oil given the nature of the data

  8. -For both COP and CVX, Factor1 is loads positively and most variance is explained by common factors (high communality)

  9. -Principle-Component Factors: treating the communalities (1-uniqueness) as 1, thus allowing for no unique factors

  10. Interesting: With RV, we see Factor1 explaining COP and XOM, with a high degree of communality

  11. -When communality is forced to be 1, Factor1 explains COP and XOM while Factor 2 explains CVX and OIL

  12. Pretty terrible results for RV-BV

  13. More familiarity with the practices of the oil industry, especially their trading desk operation to determine how they deal with oil price volatility • Introducing a new jump test that can detect multiple jumps per day and time of jump. Lee-Mykland (2008)? Dobrev et. al (2007) • Auto correlation with small lag times • Can we use the implied volatility of same industry companies and oil futures to forecast volatility using the HAR-RV-CJ model?

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