Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market

Paper: arXiv 2304.04676

Authors: Ke Zhang

Abstract

In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group of factors by our MAXFLAT low-pass volatility model. All of our experiments are under China’s CSI 300 and CSI 500 universe which represent China’s large cap stocks and mid-small cap stocks. Our result shows adjust factors by MAXFLAT volatility model have better performance in both large cap and small cap universe than original factors or other risk adjust factors in China A share. Also the portfolio constructed by MAXFLAT risk adjust factors have continuous excess return and lower beta compare with benchmark index.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an interesting signal-processing approach to factor risk adjustment using MAXFLAT low-pass filters applied to China A-shares (CSI 300/500). While the mathematical derivation of signal filters provides solid theoretical grounding and empirical backtests show improved risk-adjusted returns, the presentation suffers from minor language clarity issues and lacks open-source implementation details.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve factor performance & portfolio construction in China A-shares using MAXFLAT low-pass volatility model] --> B[Data Inputs: China CSI 300 Large Cap & CSI 500 Mid-Small Cap Universe]
    B --> C[Methodology: Apply MAXFLAT Low-Pass Filter to model volatility and adjust factor groups]
    C --> D[Computational Process: Construct risk-adjusted factor portfolios & backtest against benchmarks]
    D --> E[Outcome 1: MAXFLAT adjusted factors outperform original & traditional risk-adjusted factors]
    D --> F[Outcome 2: Portfolios yield continuous excess returns with lower beta relative to benchmark indices]