Technical Data Analysis

AUTO-CORRELATION

www.econ.berkeley.edu/~anderson/Spurious.pdf

If an individual stock does not trade on a given day, its daily return is reported as zero;14 if it

does not trade for several days, it is in effect accumulating several days of unreported gain or loss, which

is captured in the data on the first subsequent day on which trade occurs. Think of the “true” price of the

stock being driven by a positive (negative) drift component, the equilibrium mean return, plus a daily

mean-zero volatility term, with the reported price being updated only on those days on which trade occurs.

On days on which no trade occurs, the reported return will be zero, which is below (above) trend; on days

on which trade occurs after one or more days without trade, the reported return represents several days’

worth of trend; this results in spurious negative autocorrelation. Even if a stock does trade on a given

day, the reported “daily closing price” is the price at which the last transaction occurred, which might be

several hours before the market closed.

Autocorrelation of returns is a well known attribute of certain discrete time

asset models (described generically as VAR models, not to be confused with

value at risk) that are commonly referred to as “mean reverting”.

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Definition 2: An asset model is mean reverting if returns are negatively auto-correlated.

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We use regression analysis to predict a continuous dependent variable from a number of independent variables. One point we keep in mind with regression analysis is that causal relationships among the variables cannot be determined i.e. X "predicts" Y, but we cannot say that X "causes" Y.

Assumptions of regression:

After collecting the data and checking its accuracy we run a regression from 1st January, 2008 to 26th October, 2010.

* Y = b1X1 + b2X2 + ... + A

where Y is the dependent variable you...