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# Risk Parity Optimization

Risk parity portfolio optimization is a type of investment strategy that seeks to allocate portfolio assets in a way that balances risk across multiple asset classes. It is based on the idea that each asset class's contribution to the portfolio's overall risk should be approximately equal, regardless of the size or historical performance of the asset class. To achieve this balance, the strategy uses a mathematical model that estimates the risk contribution of each asset class based on historical data. The model considers volatility, correlation, and covariance factors to determine how much of each asset class should be included in the portfolio. Risk parity portfolio optimization aims to create a diversified portfolio that is not overly exposed to any single asset class and has a consistent level of risk across all asset classes. As a result, this approach can potentially lead to more stable returns over the long term, as the portfolio is less likely to be impacted by sudden shifts in the market.

Mean-Variance Optimization (MVO)

This foundational model of portfolio theory balances risk and return, aiming for the highest expected return for a given risk level using historical data. However, its reliance on statistical estimates can lead to over-concentration in certain assets.

Risk Parity

Instead of focusing on capital allocation like MVO, this model equally distributes risk across all assets or asset classes in a portfolio. It results in more diversified portfolios and is less sensitive to market changes compared to MVO.

Hierarchical Risk Parity (HRP)

Combining aspects of MVO and risk parity, HRP uses a hierarchical clustering algorithm to allocate risk within asset clusters. This approach leads to enhanced diversification and is effective in managing market instabilities.

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