Modeling Volatility Dynamics

Slides:

Advertisements

Similar presentations

GARCH Models and Asymmetric GARCH models

Advertisements

Volatility in Financial Time Series

ARCH (Auto-Regressive Conditional Heteroscedasticity)

Weighted Least Squares Regression Dose-Response Study for Rosuvastin in Japanese Patients with High Cholesterol "Randomized Dose-Response Study of Rosuvastin.

Homoscedasticity equal error variance. One of the assumption of OLS regression is that error terms have a constant variance across all value so f independent.

K. Ensor, STAT Spring 2004 Garch-m The process or return is dependent on the volatility , c are constants C is the “risk premium parameter”; c>0.

Copula approach to modeling of ARMA and GARCH models residuals Anna Petričková FSTA 2012, Liptovský Ján

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.

Primbs, MS&E 345, Spring The Analysis of Volatility.

The Garch model and their Applications to the VaR

Stochastic Trend With Seasonality 1.Seasonal Difference 2.Multiplicative Seasonal ARMA.

Modeling Cycles By ARMA

1 Takehome One month treasury bill rate.

Car Sales Analysis of monthly sales of light weight vehicles. Laura Pomella Karen Chang Heidi Braunger David Parker Derek Shum Mike Hu.

1 Ka-fu Wong University of Hong Kong Volatility Measurement, Modeling, and Forecasting.

Arch-Garch PPIFGS. Producer Price Index Finished Goods 1982=100.

1 Arch-Garch Lab Nine. 2 Producer Price Index for Finished Goods, 1982 =100, –

Volatility Models Fin250f: Lecture 5.2 Fall 2005 Reading: Taylor, chapter 9.

Financial Time Series CS3. Financial Time Series.

March 7, 2006Lecture 8bSlide #1 Class Analysis Review Predict expected temperature change (c4_34_tc), using the following independent variables: –Age (c5_3_age)

An Introduction to Financial Econometrics: Time-Varying Volatility and ARCH Models Prepared by Vera Tabakova, East Carolina University.

Regression Diagnostics Checking Assumptions and Data.

1 CHAPTER 14 FORECASTING VOLATILITY II Figure 14.1 Autocorrelograms of the Squared Returns González-Rivera: Forecasting for Economics and Business, Copyright.

Dealing with Heteroscedasticity In some cases an appropriate scaling of the data is the best way to deal with heteroscedasticity. For example, in the model.

Time-Varying Volatility and ARCH Models

Box Jenkins or Arima Forecasting. H:\My Documents\classes\eco346\Lectures\chap ter 7\Autoregressive Models.docH:\My Documents\classes\eco346\Lectures\chap.

Realized Volatility Distribution in Japanese Stock Market Tetsuya Takaishi Hiroshima University of Economics To be published in Evolutionary and Institutional.

FINANCE AND THE FUTURE In this great future you can’t forget your past … by David Pollard 1.

1 MF-852 Financial Econometrics Lecture 10 Serial Correlation and Heteroscedasticity Roy J. Epstein Fall 2003.

EC 532 Advanced Econometrics Lecture 1 : Heteroscedasticity Prof. Burak Saltoglu.

NEW FRONTIERS FOR ARCH MODELS Prepared for Conference on Volatility Modeling and Forecasting Perth, Australia, September 2001 Robert Engle UCSD and NYU.

STAT 497 LECTURE NOTE 9 DIAGNOSTIC CHECKS 1. After identifying and estimating a time series model, the goodness-of-fit of the model and validity of the.

MULTIVARIATE TIME SERIES & FORECASTING 1. 2 : autocovariance function of the individual time series.

Seasonal ARMA forecasting and Fitting the bivariate data to GARCH John DOE.

Testing for unit roots in Eviews

GARCH Models Þættir í fjármálum Verkefni 1-f Bjartur Logi Ye Shen

MODELING VOLATILITY BY ARCH-GARCH MODELS

1 Chapter 5 : Volatility Models Similar to linear regression analysis, many time series exhibit a non-constant variance (heteroscedasticity). In a regression.

13 th AFIR Colloquium 2003 The estimation of Market VaR using Garch models and a heavy tail distributions The dynamic VaR and The Static VaR The Garch.

ARCH AND GARCH V AIBHAV G UPTA MIB, D OC, DSE, DU.

THE EXPONENTIAL GARCH MODEL. To allow for asymmetric effects between positive and negative asset returns, he considers the weighted innovation where θ.

Standard and Poor index (S&P500) Standard and Poor index (S&P500) : Returns (first difference)

Nonlinear dynamics: evidence for Bucharest Stock Exchange Dissertation paper: Anca Svoronos(Merdescu)

AUTOCORRELATION 1 Assumption B.5 states that the values of the disturbance term in the observations in the sample are generated independently of each other.

Time Series Analysis PART II. Econometric Forecasting Forecasting is an important part of econometric analysis, for some people probably the most important.

Econometric methods of analysis and forecasting of financial markets Lecture 3. Time series modeling and forecasting.

1 Lecture Plan : Modelling volatility and correlation in energy markets Time Varying Volatility and Correlation in Energy Markets Models for.

Lecture 8 Stephen G. Hall ARCH and GARCH. REFS A thorough introduction ‘ARCH Models’ Bollerslev T, Engle R F and Nelson D B Handbook of Econometrics vol.

MODELING VOLATILITY BY ARCH- GARCH MODELS 1. VARIANCE A time series is said to be heteroscedastic, if its variance changes over time, otherwise it is.

K. Ensor, STAT Spring 2004 Volatility Volatility – conditional variance of the process –Don’t observe this quantity directly (only one observation.

Vera Tabakova, East Carolina University

Ch8 Time Series Modeling

ARCH/GARCH Modelling of Exchange Rates in Turkey

Chapter 6: Autoregressive Integrated Moving Average (ARIMA) Models

The Nature of Heteroscedasticity

STAT 497 LECTURE NOTE 9 DIAGNOSTIC CHECKS.

Leveraging Bad news – negative shocks – have a larger impact on volatility than good news or positive shocks. This asymmetry is incorporated in a GARCH.

CIS 2033 based on Dekking et al

ASSET PRICE VOLATILITY: THE ARCH AND GARCH MODELS

Multiple Regression A curvilinear relationship between one variable and the values of two or more other independent variables. Y = intercept + (slope1.

أنماط الإدارة المدرسية وتفويض السلطة الدكتور أشرف الصايغ

Applied Econometric Time Series Third Edition

Chapter 4 Other Stationary Time Series Models

Forecasting the Return Volatility of the Exchange Rate

Serial Correlation and Heteroscedasticity in

Chapter 4, Regression Diagnostics Detection of Model Violation

Homoscedasticity/ Heteroscedasticity In Brief

Homoscedasticity/ Heteroscedasticity In Brief

Lecturer Dr. Veronika Alhanaqtah

Serial Correlation and Heteroscedasticity in

Presentation transcript:

Modeling Volatility Dynamics ARCH and GARCH Modeling Volatility Dynamics

Modeling Unequal Variability Equal Variability: Homoscedasticity Unequal Variability: Heteroscedasticity Means any variability (around the mean) that is not homoscedasticity Models must be developed for specific cases

What These Acronym Mean? ARCH Autoregressive Conditional Heteroscedasticity GARCH Generalized ARCH

Information in e2 Let et have the mean 0 and the variance st. Let et be the residual of a model fitted. Then: et estimates et et2 estimates the variance st2.

ARCH Modeling of st2. ARCH(1) ARCH as AR(1) on

GARCH GARCH(1) GARCH (1) as ARMA(1,1) on

Asymmetry in GARCH - TARCH d = 1 if et < 0, and = 0 if et > 0

Asymmetry in GARCH - EGARCH

ARCH(p, q) series_name c Eviews Command ARCH(p, q) series_name c