1 / 13

Monte-carlo and Bootstrapping

Fin250f: Lecture 9 Spring 2010 Brooks, chapter 12.1-12.4. Monte-carlo and Bootstrapping. Outline. Computer simulation Monte-carlo Bootstrapping. Why Simulations?. Small samples Complex expressions (no analytics) Ease of use (hard to get analytics). Examples from Finance.

gwendolyn-colon
Download Presentation

Monte-carlo and Bootstrapping

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fin250f: Lecture 9 Spring 2010 Brooks, chapter 12.1-12.4 Monte-carlo and Bootstrapping

  2. Outline • Computer simulation • Monte-carlo • Bootstrapping

  3. Why Simulations? • Small samples • Complex expressions (no analytics) • Ease of use (hard to get analytics)

  4. Examples from Finance • Risk management • Value-at-Risk/Stress testing • Option pricing • Trading rules and data snooping • Stationarity testing

  5. Monte-carlo procedure • Postulate "data generating process" DGP • Simulate with random number generator y = b*x + e • Estimate b • Store distribution • Examples: simplemc.m, snooprule.m

  6. Random Number Generators • Fake random numbers • Random seed to start • Matlab and seeds • newSeed.m

  7. Bootstrapping • Use actual data • Draw new set of data independently • Stock returns • Write on paper • Throw in urn • Draw returns at random with replacement • Sample.m • genRandomWalk.m

  8. Bootstrapping a Regression • Two methods • Method 1 • Resample (y,x) pairs • Method 2 • Resample e = y-bx residuals • Then regenerate y from scrambled e

  9. Power of the Bootstrap • No distributional assumptions • Data speaks for itself

  10. Problems with Bootstrap • Outliers in the data • Too little data • Nonstationary data • Nonindependent data • Max's and mins

  11. Antithetic Sampling • If distribution is symmetric about zero • Simulate with bootstrap • Randomly flip sign • Like having twice as much data

  12. Bootstrap Examples • bootcapm.m • bootquantile.m

  13. Monte-carlo Examples • mclinearsnoop.m • In sample/out sample • snooprule.m • Trading rule snooping • Best rules out of a group • mcmse.m

More Related