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<p>Risk parity, an evolving approach in portfolio management that aims to distribute risk equitably among assets, is reshaping traditional views on asset allocation and diversification. This study proposes a novel two-step strategy within the risk parity framework with the primary objective to induce sparsity. In the first step, assets are ranked by defining a three state markov chain based on second order stochastic dominance criterion and top <i>k</i> assets are filtered to match the desired cardinality. The optimal portfolio is then generated using a variance-based risk parity framework for these selected <i>k</i> assets. The proposed strategy is evaluated against a comprehensive suite of benchmarks, including the market index, equally weighted (1/<i>n</i>) portfolios, the traditional risk parity, minimum variance, mean variance, along with their cardinality constrained extensions, and two generalized risk parity frameworks. The analysis spans five levels of cardinality; <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(k=10\%, 25\%\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(50\%\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(75\%\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(90\%\)</EquationSource> </InlineEquation> of the total number of assets. A comprehensive empirical investigation is conducted on weekly data of four global datasets, namely, S&amp;P Asia 50 (Asia), HangSeng (Hong Kong), FTSE 100 (UK) and BSE 200 (India) and daily data of two global indices, namely, S&amp;P 500 and Russell 1000 that highlights the superior out-of-sample performance of the proposed strategy in terms of mean return, Sharpe ratio, Sortino ratio, and Stable tail-adjusted return ratio (STARR) across all datasets. Additionally, analysis on different market phases further demonstrates the robustness of the proposed framework to changing market dynamics. The proposed framework strikes a favorable balance between computational efficiency and out-of-sample performance when compared to the traditional benchmarks and the cardinality-constrained models.</p>

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A two-step risk parity strategy using markov chain driven asset ranking

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Vrinda Dhingra,
  • Amita Sharma,
  • S. K. Gupta

摘要

Risk parity, an evolving approach in portfolio management that aims to distribute risk equitably among assets, is reshaping traditional views on asset allocation and diversification. This study proposes a novel two-step strategy within the risk parity framework with the primary objective to induce sparsity. In the first step, assets are ranked by defining a three state markov chain based on second order stochastic dominance criterion and top k assets are filtered to match the desired cardinality. The optimal portfolio is then generated using a variance-based risk parity framework for these selected k assets. The proposed strategy is evaluated against a comprehensive suite of benchmarks, including the market index, equally weighted (1/n) portfolios, the traditional risk parity, minimum variance, mean variance, along with their cardinality constrained extensions, and two generalized risk parity frameworks. The analysis spans five levels of cardinality; \(k=10\%, 25\%\) , \(50\%\) , \(75\%\) and \(90\%\) of the total number of assets. A comprehensive empirical investigation is conducted on weekly data of four global datasets, namely, S&P Asia 50 (Asia), HangSeng (Hong Kong), FTSE 100 (UK) and BSE 200 (India) and daily data of two global indices, namely, S&P 500 and Russell 1000 that highlights the superior out-of-sample performance of the proposed strategy in terms of mean return, Sharpe ratio, Sortino ratio, and Stable tail-adjusted return ratio (STARR) across all datasets. Additionally, analysis on different market phases further demonstrates the robustness of the proposed framework to changing market dynamics. The proposed framework strikes a favorable balance between computational efficiency and out-of-sample performance when compared to the traditional benchmarks and the cardinality-constrained models.