1 / 15

Miscellany

This study investigates the effects of market microstructure noise on stock price volatility using one-minute price data for Alcoa and DuPont. We explore the separation of actual volatility from noise and examine the predictive relationship between negative overnight returns and subsequent price reversals. By analyzing a dataset spanning from 1997 to 2010, we present methods for estimating finite sample considerations and the impact of microstructure on observed price movements. Key findings provide insights into stock performance after negative overnight returns.

paulos
Télécharger la présentation

Miscellany

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. Miscellany XiaoyangZhuang Economics 201FS Duke University March 16, 2010

  2. Price Series One-minute price data Alcoa. The world’s leading producer of aluminum. DuPont . A diversified scientific company. April 9, 1997 – December 30, 2010 3404 data points Aligned

  3. Returns Series * ^ Five-minute returns data (overnight returns excluded) Alcoa. (*) October 10, 2008, 9:40 - 9:50: Standard & Poor’s revised the firm’s outlook from “stable” to “negative” after markets closed on October 9. [$11.76 11.82 12.43 12.54 13.09 $13.00 12.53 12.40 12.29 12.25 $12.04] DuPont. (^) May 6, 2010, 14:40 – 14:50: Flash crash. [$36.60 $34.10 $35.44]

  4. Contents 1. Separating Market Microstructure Noise from the Price Process 2. Using Overnight Returns to Predict (Next-Day) Price Reversal

  5. Contents 1. Separating Market Microstructure Noise from the Price Process 2. Using Overnight Returns to Predict (Next-Day) Price Reversal

  6. Volatility Series Annualized, daily, one- and five-minute realized volatility data where t the day index, M is the number of intervals per day, and ∆ is the sampling interval size.

  7. Market Microstructure Noise • Motivating Question • Given one-minute stock price data, is it possible to disentangle actual volatility from market microstructure noise? • The Idea • (*) The difference between realized volatilities calculated using x- and y-minute (x < y) returns data should be due to finite sample considerations and microstructure noise: • where RVolt,x-minute is the realized volatility on day t calculated using x-minute returns data; A is the difference due to finite sample considerations; and εmicrostructure is the difference due to microstructure noise. • The Percent of RVolt,xmin Not Accounted for by RVolt,(x+6)min seems to stabilize when x≥ ~6 (as we will see). • Could the stabilized value serve as an estimator of finite sample considerations as the sampling frequency changes?

  8. (RVolt,1min - RVolt,6min)/ (RVolt,1min) Percent of RVolt,1min Not Accounted for by RVolt,6min Sample statistics: AA mean(Percent) =6.30 median(Percent) = 6.95 std(Percent) = 13.12 range(Percent) = [-57.40 50.55] Sample statistics: DD mean(Percent) = 8.00 median(Percent) = 8.24 std(Percent) = 13.73 range(Percent) = [-50.40 78.56]

  9. (RVolt,6min - RVolt,11min)/ (RVolt,6min) Percent of RVolt,6min Not Accounted for by RVolt,11min Sample statistics: AA mean(Percent) =2.59 median(Percent) = 2.68 std(Percent) = 12.55 range(Percent) = [-62.97 52.65] Sample statistics: DD mean(Percent) = 3.62 median(Percent) = 3.55 std(Percent) = 12.56 range(Percent) = [-51.31 45.26]

  10. (RVolt,11min - RVolt,16min)/ (RVolt,11min) Percent of RVolt,11min Not Accounted for by RVolt,16min Sample statistics: AA mean(Percent) =-0.79 median(Percent) = -0.45 std(Percent) = 14.76 range(Percent) = [-56.30 53.01] Sample statistics: DD mean(Percent) = -0.28 median(Percent) = 0.22 std(Percent) = 14.82 range(Percent) = [-85.91 59.17]

  11. (RVolt,16min - RVolt,21min)/ (RVolt,16min) Percent of RVolt,16min Not Accounted for by RVolt,21min Sample statistics: AA mean(Percent) =0.82 median(Percent) = 1.16 std(Percent) = 16.03 range(Percent) = [-71.70 57.39] Sample statistics: DD mean(Percent) = 1.16 median(Percent) = 1.79 std(Percent) = 16.15 range(Percent) = [-78.41 49.66]

  12. Can the mean of stable Percent values (i.e. x ≥ ~6) serve as an estimator of finite sample considerations?

  13. Contents 1. Separating Market Microstructure Noise from the Price Process 2. Using Overnight Returns to Predict (Next-Day) Price Reversal

  14. Overnight Returns and (Next-Day) Price Reversal Motivating Question Given that a stock loses value in afterhours trading, how will the stock perform in the first 15 minutes after the market opens the following morning? Procedure Define t as the day index. For each stock, consider every case in which the overnight return is negative: i.e. [pricet,15:59 pricet+1,9:35] = [x y], where x > y Compute a = max([pricet+1,9:36 pricet+1,9:37 . . . pricet+1,9:50]). Plot each (negative) overnight return against its corresponding Price Reversal, where Price Reversal = log(a) – log(pricet+1,9:35)

  15. Overnight Returns and (Next-Day) Price Reversal Sample statistics: AA mean(Reversal) = 0.4281% median(Reversal) = 0.2552% % of Reversal data points exceeding 0: 74.8 Sample statistics: DD mean(Reversal) = 0.3441% median(Reversal) = 0.2309% % of Reversal data points exceeding 0: 77.0

More Related