5.4 GARCH models..

Slides:



Advertisements
Similar presentations
What is the sum of the following infinite series 1+x+x2+x3+…xn… where 0
Advertisements

Some Financial Mathematics. The one-period rate of return of an asset at time t. where p t = the asset price at time t. Note: Also if there was continuous.
Econ 240 C Lecture Part I. ARCH-M Modeks w In an ARCH-M model, the conditional variance is introduced into the equation for the mean as an explanatory.
1 Arch-Garch Lab Nine. 2 Producer Price Index for Finished Goods, 1982 =100, –
Lecture 8 Topics Fourier Transforms –As the limit of Fourier Series –Spectra –Convergence of Fourier Transforms –Fourier Transform: Synthesis equation.
Tapering and prewhitening fFT taper, h(u). Need for prewhitening/prefiltering periodogram is generally biased.
11/20/2015 Fourier Series Chapter /20/2015 Fourier Series Chapter 6 2.
Significant Figures.
Seasonal ARMA forecasting and Fitting the bivariate data to GARCH John DOE.
Testing for unit roots in Eviews
Estimating Volatilities and Correlations
1. What’s the homologous series?. 2. What’s the homologous series?
Significant Figures. Significant Figures One sig fig One sig fig Two sig figs One sig fig One sig fig One sig fig four sig figs.
Social Media SIG 8/17/15. Privacy Settings.
Descriptive Exploratory Data Analysis II Jagdish S. Gangolly State University of New York at Albany.
Measurements Every measurements has UNITS
Distance between 2 points Mid-point of 2 points
Descriptive Exploratory Data Analysis II
Vera Tabakova, East Carolina University
Estimating Volatilities and Correlations
Clearly define the problem or challenge
Time Series Analysis and Its Applications
GCSE Revision (Section B) - Branding and Marketing.
Washington State University
STA 282 Introduction to Statistics
6.4 GARCH models..
WIND TURBINE GENERATORS.
Festas Juninas 2018
Stochastic time series.
Segue from time series to point processes.
Dividends options on Forwards/Futures (Black model)
Significant Figure Review
Recursion & Linked Lists
With joint time series data and either a bivariate model, data (X(t),Y(t); t = 0,...,T-1) or a regression/transfer function model the following R functions.
Forecasting with non-stationary data series
محاظرة : التاسعة العشرة
Test the series for convergence or divergence. {image}
Topic H: Electrical circuits
The importance of prefiltering.
בית הספר המשותף חוף הכרמל
ARCH(m) Example. S&P/TSX Capped Composite
An ARCH(m) example S&P/TSX Capped Composite 4/26/2010 to 4/26/2013
Test the series for convergence or divergence. {image}
postscript(file="sales.eps",paper="letter")
ARMA models 2012 International Finance CYCU
Unit Roots 31/12/2018.
Determine the current through each resistor, the total current and the voltage across each resistor. VB R4 R5.
(a) the equivalent resistance, (b) V0,
WORD GENERATION power point by Lisa Brubaker-Crammer
Prop-50 IP Address Transfers
How many sig figs are in each of the
Significant Figures.
Algebra U = ∑ a(t) Y(t) E{U} = c Y ∑ a(t)
Comparing Series and Parallel Circuits
General Concept On Operational Excellence Open for discussion.
If x is a variable, then an infinite series of the form
Numerical solution of first-order ordinary differential equations
Lecture 11: Power, type I error and FDR
Eni Sumarminingsih, SSi, MM
*** Series PRS Version 1, ***
*** Series 3RS Version 1, ***
Autocorrelation Dr. A. PHILIP AROKIADOSS Chapter 5 Assistant Professor
Generating Sequences © T Madas.
The Rule of a Series Lesson # 30.
PARIS21 Foundation 2019 PARIS21 Board Meeting 3 April 2019.
Calculation with Significant Figures
The CIDOC CRM Recent Developments
Numerical solution of first-order ordinary differential equations 1. First order Runge-Kutta method (Euler’s method) Let’s start with the Taylor series.
Presentation transcript:

5.4 GARCH models.

ARCH(m) GARCH(m.r)

A martingale difference series, E(yt |Yt-1 } = 0 “Learning a potential function …”

Cov not 0 generally

postscript(file="arch.ps",paper="letter",hor=FALSE) par(mfrow=c(2,1)) library(tseries) set.seed(28112006) a0<-1;a1<-.75 ylast<-1;Y<-ylast Sig<-NULL for(i in 1:250){ sig2<-a0+a1*ylast**2 y<-sqrt(sig2)*rnorm(1) ylast<-y Y<-c(Y,y) Sig<-c(Sig,sqrt(sig2)) } plot(Y,type="l",main="Data",xlab="time",ylab="",las=1) plot(Sig,type="l",main="Sig",xlab="time",ylab="",las=1) acf(Y,main="acf of data",xlab="lag",ylab="",las=1) acf(Y**2,main="acf of data-squared",xlab="lag",ylab="",las=1) graphics.off()