Statistical process control (SPC) methods have been widely popular not only in industrial applications but also in recent years in several nonindustrial scientific areas. The basic tools of SPC are control charts. SPC methods have been applied to various financial applications such as portfolio surveillance, stock trading, and interest rates. The purpose of this paper is to present the main applications of statistical monitoring process methods, specifically control charts, to the financial area. We focus on applications in stock markets such as technical trading rules, portfolio monitoring problems providing some directions for future research.

Affine term structure models are widely used for studying the relationship between yields on assets of different maturities. However, it can be a helpful tool for the construction of fixed-income portfolios. The monitoring of these bond portfolios is of great importance for the investor. The purpose of this work is twofold. Firstly, we construct and optimize fixed-income portfolios using Markowitz’s portfolio approach to a multifactor Gaussian affine term structure model (ATSM) under no-arbitrage conditions estimated with the minimum chi square estimation method. The fixed-income portfolios based on the term structure model are compared with some benchmark portfolio strategies, and our findings show that our proposed approach performs well under the risk–return tradeoff. Secondly, we propose control chart procedures for monitoring the optimal weights of government bond portfolios in order to detect possible changes. The results indicate that control chart procedures can be useful in the detection of changes in the optimal asset allocation of fixed income portfolios.