Econometrics & Finance

Description

garch04The analysis of econometric and financial data is typified by non-Gaussian multivariate observations which exhibit complex dependencies: heavy-tailed and skewed marginal distributions are commonly encountered; serial dependence, such as auto-correlation and conditional heteroscedasticity, appear in time-ordered sequences; and non-linear, higher-order, and tail dependence are widespread.sr_week_returns (1)

We develop new methods and software for assessing departures from normality, modeling univariate and multivariate data, copula and tail dependence models, statistical factor models, structural break detection, and predictive models for asset returns, their co-movements, and their volatilities. We also consider applications in yield curve modeling, robust portfolio optimization, statistical arbitrage, hedging, and risk management.

Related Publications

Textbook

Ruppert, D., Matteson, D.S. (2015). Statistics and Data Analysis for Financial Engineering (2nd ed., pp. 721). New York, NY: Springer.

Book Chapter

Matteson, D.S., James, N.A. and Nicholson, W.B. (2015), “Statistical Measures of Dependence For Financial Data,” In Press, Financial Signal Processing and Machine Learning, Wiley.

Preprints

Nicholson, W.B., Bien, J. and Matteson, D.S. (2015), “HVAR: High Dimensional Forecasting via Interpretable Vector Autoregression.”

Nicholson, W.B., Matteson, D.S. and Bien, J. (2015), “VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables.”

Kowal, D.R., Matteson, D.S. and Ruppert, D. (2015), “A Bayesian Multivariate Functional Dynamic Linear Model.”

James, N.A. and Matteson, D.S. (2015), “Change Points via Probabilistically Pruned Objectives.”

Articles

Matteson, D.S. and Tsay, R.S. (2015), “Independent Component Analysis via Distance Covariance,” To Appear, Journal of the American Statistical Association.

James, N.A. and Matteson, D.S. (2015), “ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data,” Journal of Statistical Software, Vol. 62, No. 7: 1-25.

Matteson, D.S. and James, N.A. (2014), “A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data,” Journal of the American Statistical Association, Vol. 109, No. 505, 334-345.

Matteson, D.S., James, N.A., Nicholson, W.B. and Segalini, L.C. (2013), “Locally Stationary Vector Processes and Adaptive Multivariate Modeling,” Acoustics, Speech and Signal Processing, IEEE, 8722-8726.

Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 485-499.

Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “Rejoinder, An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 504-505.

Matteson, D.S. and Ruppert, D. (2011), “GARCH Models of Dynamic Volatility and Correlation,” Signal Processing Magazine, IEEE, Vol. 28, No. 5, 72-82.

Woodard, D.B., Matteson, D.S. and Henderson S.G. (2011), “Stationarity of Generalized Autoregressive Moving Average Models,” Electronic Journal of Statistics, Vol. 5, No. 0, 800-828.

Matteson, D.S. and Tsay, R.S. (2011), “Dynamic Orthogonal Components for Multivariate Time Series,” Journal of the American Statistical Association, Vol. 106, No. 496, 1450-1463.

Matteson, D.S. and Tsay, R.S. (2007), “High Dimensional Volatility Models,” JSM Proceedings, Business and Economics Statistics Section, Alexandria, VA: American Statistical Association, 1006-1013.

Code

Research Projects