Department of Economics, Queens College, City University of New York
BUS 386: FINANCIAL ECONOMETRICS
Professor Magdalena Sokalska
e-mail: magdalena.sokalska@qc.cuny.edu
Office: Powdermaker Hall, Room 306-E
Office hours:
Webpage: Blackboard
PREREQUISITES
ECON 382 or BUS 384; and MATH 241 or permission by the instructor.
COURSE DESCRIPTION
The course will introduce students to methods of empirical analysis of financial markets. It will cover
modern statistical and econometric techniques necessary for both professional and academic quantitative
research in finance. Particular emphasis will be placed on measuring risk of holding and trading financial
assets. Topics include: autoregressive and moving average models, ARCH, GARCH, analysis of high
frequency intraday financial data.
REQUIRED TEXTBOOK
Brooks, C. Introductory Econometrics for Finance, Cambridge University Press, 2002.
ADDITIONAL MATERIAL (for your reference only)
Tsay, Ruey S. Analysis of Financial Time Series, 2e, Wiley, 2005.
Taylor, Stephen J. Asset Price Dynamics, Volatility, and Prediction, Princeton University Press, 2005.
REQUIRED SOFTWARE
The software for this course is STATA (www.stata.com). Students may also use Eviews.
GRADING
Active Class Participation
Midterm I
Computer/Empirical Projects
Final (cumulative)
(about 5%)
(about 25%)
(about 35%)
(about 35%)
COURSE OUTLINE
TOPICS
RANDOM CHARACTER OF ASSET PRICES (7 sessions)

Stylized characteristics of financial data. Returns in financial modeling.

Random character of asset prices vs. predictability.

A short review of MA and AR processes.

Estimation of MA and AR processes.
PREDICTABILITY OF VOLATILITY (12 sessions)

Predicting volatility. Popular volatility forecasting methods: historical volatility, EWMA,
implied volatility, range. ARCH models.

GARCH models. ML Estimation.

Extensions of the basic GARCH models.

Bivariate GARCH models. (time permitting)

Evaluation of volatility forecasts. Volatility proxies. Diebold and Mariano test.

Value at Risk estimation.
ECONOMETRICS OF TRANSACTION LEVEL DATA (7 sessions)

Periodicity in high frequency data. High frequency volatility.

RV models.

Short introduction to quote-driven vs. order driven markets. Bid-ask spread.

Modeling irregularly spaced data. Calendar time and trading time. Bid-ask bounce.

Predicting trade durations. Hazard functions.

ACD Models.

Measuring liquidity and market pressure.
OTHER TOPICS (time permitting)

Discussion of evidence on long horizon predictability.

Stock market anomalies.