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Exact Post-selection Inference For Tracking S&P500

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  • Farshad Noravesh
  • Hamid Boustanifar

Abstract

The problem that is solved in this paper is known as index tracking. The method of Lasso is used to reduce the dimensions of S&P500 index which has many applications in both investment and portfolio management algorithms. The novelty of this paper is that post-selection inference is used to have better modeling and inference for Lasso approach to index tracking. Both confidence intervals and curves indicate that the performance of Lasso type method for dimension reduction of S&P500 is remarkably high. Keywords: index tracking, lasso, post-selection inference, S&P500

Suggested Citation

  • Farshad Noravesh & Hamid Boustanifar, 2021. "Exact Post-selection Inference For Tracking S&P500," Papers 2112.15448, arXiv.org.
    • Handle: RePEc:arx:papers:2112.15448
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    File URL: http://arxiv.org/pdf/2112.15448
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    References listed on IDEAS

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    1. Sant’Anna, Leonardo Riegel & Caldeira, João Frois & Filomena, Tiago Pascoal, 2020. "Lasso-based index tracking and statistical arbitrage long-short strategies," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
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    More about this item

    Keywords

    index tracking; lasso; post-selection inference; s&p500;
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