Index Tracking via Temporally Weighted Least Squares and Gaussian Process Regressions
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new index tracking method using temporally weighted least-squares regression. The approach improves tracking accuracy by assigning varying importance to historical financial data.
Area Of Science
- Quantitative Finance
- Computational Finance
Background
- Index tracking is a passive investment strategy aiming to replicate a financial market index.
- Current methods often assume equal importance of all historical data, potentially overlooking valuable insights.
- This can lead to suboptimal tracking accuracy and consistency.
Purpose Of The Study
- To develop an improved index tracking method that accounts for the varying importance of historical data.
- To introduce a novel approach using temporally weighted least-squares regression for index tracking.
- To enhance the accuracy and consistency of passive investment strategies.
Main Methods
- The proposed method employs temporally weighted least-squares regression.
- Weights for historical periods are determined by the reciprocal of largest absolute residuals in subsequent periods.
- The weight for the most recent period is derived using Gaussian process regression.
Main Results
- Experimental results on seven major stock markets demonstrate the effectiveness of the proposed approach.
- The method shows improved tracking accuracy compared to traditional equally weighted methods.
- Consistent performance was observed across different market conditions.
Conclusions
- Temporally weighted least-squares regression offers a superior approach to index tracking.
- Accounting for the time-varying importance of historical data enhances investment strategy performance.
- The proposed method provides a more robust and accurate passive investment solution.
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