Statistics 434: Bullet Points for Day 2 Noise, AR(1), S-Plus, Estimation, and Simulation
We begin with an exploration of the normal noise model, then we look at its simplest alternative, the AR(1) mod
Statistics 434: Bullet Points for Day 22 Comparing Asset Returns in the Context of Risks
Any asset manger, asset class, or investment strategy will be judged on the basis of the historical returns vie
Statistics 434: Bullet Points for Day 23 Look Back and Forward Accomplishment and Anticipation
The mission statement for the course has been at the top of the course home page for the whole semester.
Statistics 434 Homework No. 2: An Experiment with Ljung-Box
First recall the Ljung-Box Statistic on k lags:
k
LB = T (T + 2)
j=1
j 2 T j
Write an S-Plus function that computes a vector that holds the
Statistics 434 Homework No. 3: WRDS and Testing for Normality
As preparation, you should skim the material in Zivot and Wang on the creation of time series objects and the use of the timeDate() functi
Statistics 434 Homework No. 4 ARMA, ACF, PACF, and a Betting Simulation
Reading Review the material in Zivot and Wang on arima.sim, arima.mle, and arima.forecast. Also review the information on these
Statistics 434 Homework No. 5: Kelly Betting on AR(1)
This homework is more open-ended than those we have done before. It provides plenty of room for you to exercise your good sense and to show that y
Statistics 434: Homework No. 6 Stationarity, Extremes, and Opportunities
Part 1: Checking Out the Unit Root Tests Obtain 500 days of price and return data for a stock of your choosing. Use the unitroo
Statistics 434: Homework No. 8
Data Get four years (or a little less) of daily returns from CRSP for one of your favorite rms, or perhaps of some ragged mutual fund, or some interesting ETF. For this
Statistics 434: Bullet Points for Day 1 Getting Started Big Picture, S-Plus, Look at a Model
The Bullet Point Day Plans serve to organize the class time and to provide you with quick reviews of what t
Statistics 434: Bullet Points for Day 4 Autocorrelation in Theory, Practice, and Tests
The rst question that one must ask of a stationary time series is simply Do I have any reason to believe that the
Statistics 434: Bullet Points for Day 5 Autocorrelation Tests Especially the Ljung-Box Test
When is it feasible to treat a series of returns as if they are independent? In other words, when do returns
Statistics 434: Bullet Points for Day 6 WRDS, CRSP, Real Asset Returns, and Normality Assumptions
The main task today is rock-bottom practical: How does one access the CRSP data via WRDS? We also ask
Statistics 434 : Bullet Points for Day 9 Simulating and Fitting ARIMA(p,d,q) Models
The main formal task is to pick up the computation tools for studying the ARIMA(p,d,q) models. In particular, we con
Statistics 434: Bullet Points for Day 10 Bet Sizing and Long-term Wealth Growth Rates
Today we take a little side tour from our investigation of the ARIMA(p,d,q) model to look at what the general ques
Statistics 434: Bullet Points for Day 21 Co-integration and Statistical Arbitrage
When one reasons about asset prices, there is a recurring tension between "trend following" and "mean reversion." So f
Bullet Points for Day 20 Rolling Statistics and Momentum Strategies
It never makes sense to ignore data, and we always want to use all the data that is available to us at the time we must act. This me
Bullet Points for Day 18 After GARCH? In Comes the GARCH ZOO!
We begin with a celebration of the model with that is AR(1) in "mean" and GARCH(1,1) in "error." It's claim to fame is that it is our firs
Statistics 434: Bullet Points for Day 3
AR(1) Estimation Point Estimates and Their Distributions
The AR(1) model will serve as our model for a model. That is, what we
can see and say about AR(1) provi
Statistics 434: Bullet Points for Day 7 AR(p) with p 2
We now consider the autoregressive models with p 2, beginning with a look at the most useful of these the modest AR(2) model that was made famous
Statistics 434: Bullet Points for Day 8
ARIMA(p,d,q) In Full
After considering the stylized facts suggested by HW3, we complete our
description of the most widely used class of univariate time series
Bullet Points for Day 19
Comparing GARCH Family Members
The large number of models in the GARCH family may give one an uneasy
feeling. With so many alternatives, how can one make a reasonable choice?
Statistics 434 Homework No. 1
Experience with S functions and Simulation Write a function getrhohat() which creates a realization of , the sample autocorrelation for the series cfw_yt which is a simu
Statistics 434: Homework No. 7
Exploration of Style Momentum
In the language of mutual funds, style has come to refer to the nine boxes
into which Morningstar jams all funds. These styles are given by
Statistics 434: Homework No. 9
Reading Read the S-Plus Help les for all of the new S-Plus functions that we have covered and explore the help les for the related functions that are given at the bottom
Statistics 434: Bullet Points A Pre-Thanksgiving Interlude
In the Wednesday afternoon before Thanksgiving, attendance typically runs under 60%, so the most practical to use of this time is to cover so
Statistics 434: Bullet Points for Day 12 Martingales: The Most Important Stochastic Processes
At the rst level, martingales are simple mathematical objects that help us understand fair games, includin
Statistics 434: Bullet Points for Day 13 Stationarity and Unit Root Tests
If a time series is not stationary, or cannot be transformed to be stationary say by taking dierences, then we are pretty clos
Bullet Points for Day 14 Value at Risk, Extreme Values, Risk-Adjusted Returns
When the time comes to quantifying risk, you almost have to turn to the alleged Yogi quote: In theory, theory and practice
Statistics 434: Bullet Points for Day 15 Time Series Regression, CAPM, and the Three Factor Model
For us, time series regressions are typied by the Capital Asset Pricing Model (CAPM) and the Fama Fren