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Courses
Econometrics II The course will introduce the most useful statistical and econometric methods for the analysis of time series data. The applications will generally be from economics and finance but the methodology will apply to a wide range of disciplines. The course will strive to be both rigorous and intuitive. The topics include Stationary Time Series and Forecasting, Diagnostic Testing and Model Selection, Trends and Seasonality, Unit Roots, Cointegration, State Space and Regime Switching Models. Financial Theory II The course is designed to introduce the econometric tools most used in finance and to gain understanding of the sources and characteristics of financial data. The class will use various sources for financial data, and EViews software to build ARCH and other time series models. This course presumes familiarity with finance as well as a course in graduate econometrics. Ideal preparation is Econometrics I and Finance Theory I. The course covers Large Sample Theory for MLE, QMLE and GMM estimators, Volatilitiy Models, Options, Extreme Value Distributions and Copulas, Asset Allocation and Correlation Models, Microstructure Econometrics and Liquidity. Volatility The most fascinating aspect of financial market prices is their volatility. Students will learn how to measure and forecast financial volatility. They will become proficient with ARCH/GARCH models, exponential smoothing and historical volatilities. These tools will be used to measure risk and analyze alternative approaches to calculating Value at Risk. Implied volatilities from options will be introduced and compared statistically and economically. Then the course will turn to the multi-asset problem and discuss traditional and new approaches to measuring and forecasting correlations. These tools will be applied to the problem of dynamic portfolio selection and risk control. Prerequisites are Foundations of Finance and a familiarity with simple probability and statistics including least squares regression. There will be substantial use of the EViews econometric software which is available in the computer labs. Futures and Options The course introduces advanced MBA and undergraduate finance students to the theoretical and practical aspects of the markets for financial futures, options and other derivatives. Over the last 30 years, the markets for these versatile instruments have grown enormously and have generated a profusion of innovative products and ideas. Derivatives have become one of the most important, and interesting, tools of modern finance, both academic and practical. The course is divided roughly into thirds: futures and forwards, options and advanced derivatives topics. The subject matter inherently requires greater use of quantitative methods and theoretical reasoning than many other courses. However, the class focuses more on the practical than the theoretical aspects of derivatives. There is a multi-part project with market data, and other homework analyzing derivatives with Excel. Advanced Futures and Options This course consists of three parts. The first section of the course is a detailed examination of the pricing and hedging of option contracts, with particular emphasis on the application of these concepts to the design of derivatives instruments and trading strategies. The first part of this section is a review and re-examination of materials covered in the basic course, but with greater rigor and depth of coverage. The emphasis in the second part of this first section is on trading applications and risk management. The second section of the course is designed to provide a broad exposure to the subject of interest rate derivative products, both swaps and options. The last section of the course deals with recent innovations in the derivatives markets such as exotic options, credit derivatives and catastrophe derivatives. Forecasting Time Series Data Presented in this course are practical time series forecasting techniques with emphasis on the Box-Jenkins ARIMA (autoregressive integrated moving average) method and conditional volatility ARCH (autoregressive conditional heterogeneity) and GARCH (generalized autoregressive conditional heterogeneity) models. The course gives a mix of practical data analysis along with an introduction to the relevant theory. The ARIMA models are used to forecast series like interest spreads, while ARCH models are used in estimating and forecasting the volatility of series like stock returns and exchange rate returns. Students analyze data sets of their own choice in projects. Additional topics of interest covered in the course are methods of testing for nonstationary (Dickey-Fuller tests) as well as models for capturing seasonality as seen, for example, in series of monthly sales figures. The low-cost forecasting method of exponential smoothing is discussed, and its connection to the RiskMetricsTM methods of JPMorgan Chase and GARCH models is explored. Time permitting, the courses explores methods of forecasting multivariate time series, where information from several series is pooled to forecast a single series. The concept of co-integration or comovement of multivariate series is discussed (interest rates being a prime example), along with their implications for forecasts. Other potential topics in the course include the use of ARCH models in value at risk (VAR) analysis and in option pricing. Time Series Analysis This course presents the Fourier analysis of time series. The frequency domain approach covered here provides a complementary outlook on time series to the usual time domain Box-Jenkins approach. Topics include periodicity (cycles) in time series data, the periodogram and its distribution, linear filters and transfer functions, spectral density, spectral representations of autocovariances and stationary processes, ARMA (autoregressive moving average) models and their spectra, model selection, the linear forecasting problem and spectral estimation. The course also covers long memory models, including fractional ARIMA (autoregressive integrated moving average) and nonlinear time series, including ARCH (autoregressive conditional heterogeneity) models and chaos. Frequency Domain Time Series Analysis Frequency Domain Time Series is an advanced course on foundations and applications of time series. Methods involving periodograms and spectral densities are emphasized. Linear filtering and spectral representations (stochastic integrals) for stationary time series are used as unifying themes. The second half of the course considers GARCH models, fractals, long memory and fractional cointegration. Again, emphasis is on insights gained from the frequency domain viewpoint. The mathematics used in the course is Fourier analysis, a useful tool for all technically-oriented students. All mathematical results are presented in a self-contained manner. Risk Management In today's world of complex financial engineering, rising volatility and regulatory oversight, prudent management increasingly requires understanding, measuring and managing risk. Banks, securities dealers, asset managers, insurance companies and firms with significant financing operations all require real-time, enterprise-wide risk management systems for handling market, credit and operational risk. Such systems establish standards for aggregating disparate information, including positions and market data and operational risk, calculating consistent risk measures and creating timely reporting tools. This course is directed toward both finance and technology-oriented students who are interested in understanding how large-scale risk systems need to be evaluated, acquired, architected and managed. It identifies the business and technical issues, regulatory requirements and techniques to measure and report risk across an organization or market.
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