Financial Engineering and Econometrics

Signal processing and financial engineering are seemingly different areas that share strong connections underneath. Both areas rely on the statistical analysis and modeling of systems and signals, either from the financial markets or from communication channels. In both cases, accurate characterization is essential to predict the behavior of practical algorithms and optimize their performance. The exploration of these connections reveals ways to capitalize on existing mathematical tools and methodologies developed and widely applied in the context of signal processing applications. As a matter of fact, the techniques underlying optimal strategies for reliable communications over wireless links prove to be very useful in approaching open issues and recurring problems in quantitative finance.

Software

• highOrderPortfolios: Design of High-Order Portfolios via Mean, Variance, Skewness, and Kurtosis

• imputeFin: Imputation of Financial Time Series with Missing Values

• fitHeavyTail: Mean and Covariance Matrix Estimation under Heavy Tails

• portfolioBacktest: Automated Backtesting of Portfolios over Multiple Datasets

• riskParityPortfolio: Design of Risk Parity Portfolios

  • Featured in R-bloggers and RStudio.
  • Listed in: CRAN Task View on Empirical Finance and awesome-quant.

• sparseIndexTracking: Design of Portfolio of Stocks to Track an Index

  • Listed in: awesome-quant.

Books

• Konstantinos Benidis, Yiyong Feng, and Daniel P. Palomar, Optimization Methods for Financial Index Tracking: From Theory to Practice, Foundations and Trends® in Optimization, Now Publishers, 2018. [pdf]

• Yiyong Feng and Daniel P. Palomar, A Signal Processing Perspective on Financial Engineering, Foundations and Trends® in Signal Processing, Now Publishers, 2016. [pdf]

Papers

• Rui Zhou and Daniel P. Palomar, “Solving High-Order Portfolios via Successive Convex Approximation Algorithms,” IEEE Trans. on Signal Processing, vol. 69, pp. 892-904, Feb. 2021.

• Esa Ollila, Daniel P. Palomar, and Frédéric Pascal, “Shrinking the Eigenvalues of M-estimators of Covariance Matrix,” IEEE Trans. on Signal Processing, vol. 69, pp. 256-269, Jan. 2021.

• Rui Zhou, Junyan Liu, Sandeep Kumar, and Daniel P. Palomar, “Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling,” IEEE Trans. on Signal Processing, vol. 68, pp. 6198-6211, Oct. 2020.

• Rui Zhou and Daniel P. Palomar, “Understanding the Quintile Portfolio,” IEEE Trans. on Signal Processing, vol. 68, pp. 4030-4040, July 2020.

• Linlong Wu, Yiyong Feng, and Daniel P. Palomar, “General Sparse Risk Parity Portfolio Design via Successive Convex Optimization,” Signal Processing, vol. 170, pp. 1-13, Dec. 2019.

• Junyan Liu and Daniel P. Palomar, “Regularized Robust Estimation of Mean and Covariance Matrix for Incomplete Data,” Signal Processing, vol. 165, pp. 278-291, July 2019.

• Junyan Liu, Sandeep Kumar, and Daniel P. Palomar, “Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM,” IEEE Trans. Signal Processing, vol. 67, no. 8, pp. 2159-2172, April 2019. [​R package imputeFin]

• Ziping Zhao, Rui Zhou, and Daniel P. Palomar, “Optimal Mean-Reverting Portfolio With Leverage Constraint for Statistical Arbitrage in Finance,” IEEE Trans. on Signal Processing, vol. 67, no. 7, pp. 1681-1695, April 2019.

• Licheng Zhao and Daniel P. Palomar, “A Markowitz Portfolio Approach to Options Trading,” IEEE Trans. on Signal Processing, vol. 66, no. 16, pp. 4223-4238, Aug. 2018.

• Ziping Zhao and Daniel P. Palomar, “Mean-Reverting Portfolio With Budget Constraint,” IEEE Trans. on Signal Processing, vol. 66, no. 9, pp. 2342-2357, May 2018.

• Konstantinos Benidis, Yiyong Feng, and Daniel P. Palomar, “Sparse Portfolios for High-Dimensional Financial Index Tracking,” IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. [R package sparseIndexTracking]

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions,” IEEE Trans. on Signal Processing, vol. 64, no. 14, pp. 3576-3590, July 2016. [Matlab code]

• Yiyong Feng and Daniel P. Palomar, “SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design,” IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300, Oct. 2015. [R package riskParityPortfolio]

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions,” IEEE Trans. on Signal Processing, vol. 63, no. 12, pp. 3096-3109, June 2015. [Matlab code] [R package fitHeavyTail]

• Junxiao Song, Prabhu Babu, and Daniel P. Palomar, “Sparse Generalized Eigenvalue Problem via Smooth Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 7, pp. 1627-1642, April 2015. [Matlab code]

• Yiyong Feng, Daniel P. Palomar, and Francisco Rubio, “Robust Optimization of Order Execution,” IEEE Trans. on Signal Processing, vol. 63, no. 4, pp. 907-920, Feb. 2015.

• Yang Yang, Francisco Rubio, Gesualdo Scutari, and Daniel P. Palomar, “Multi-Portfolio Optimization: A Potential Game Approach,” IEEE Trans. on Signal Processing, vol. 61, no. 22, pp. 5590-5602, Nov. 2013.

• Mengyi Zhang, Francisco Rubio, Daniel P. Palomar, and Xavier Mestre, “Finite-Sample Linear Filter Optimization in Wireless Communications and Financial Systems,” IEEE Trans. on Signal Processing, vol. 61, no. 20, pp. 5014-5025, Oct. 2013.

• Mengyi Zhang, Francisco Rubio, and Daniel P. Palomar, “Improved Calibration of High-Dimensional Precision Matrices,” IEEE Trans. on Signal Processing, vol. 61, no. 6, pp. 1509-1519, March 2013.

• Francisco Rubio, Xavier Mestre, and Daniel P. Palomar, “Performance Analysis and Optimal Selection of Large Minimum-Variance Portfolios under Estimation Risk,” IEEE Journal on Selected Topics in Signal Processing: Special Issue on Signal Processing Methods in Finance and Electronic Trading, vol. 6, no. 4, pp. 337-350, Aug. 2012.