Options portfolio optimization, Technical Note—Options Portfolio Selection
Optimal Option Portfolio Strategies
By Sonam Srivastava When constructing a multi-asset portfolio, coming up with the strategy to allocate weights to the portfolio components is a very important step in the process. Coming up with weights for a portfolio options portfolio optimization its components can be done in a number of ways and is a question that boggles even the most skilled managers.
So what is the most optimal way to do this? When we think naively about this, the most intuitive way of allocating to the securities would be based on our conviction for them.
Kelly vs. Markowitz Portfolio Optimization
As the trend following strategies saw crashes in early with increased volatility in the markets, traders started adding stop losses and take profits and staggered entry and exit to the strategies.
This strategy allocated equal risk budgets to all participating assets and does not look at investor views or expected return projections.
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The passive portfolios like the market index use a market-cap-weighted allocation. Other naive methodologies are the equal weight portfolio or the minimum variance portfolio. The literature around portfolio optimization is rich and vast. There are a wide variety of variations and improvements upon the basic methods and a lot of active research that goes around it. There is even a use case of machine learning methods like reinforcement learning methods that find a good fit for this problem.
In this post, we take an introductory glance at the rationale of some popular portfolio construction methods and their implementation in Python.
Portfolio Optimization Model with and without Options under Additional Constraints
We rebalance our portfolios quarterly and trade in a long-only fashion. Equal Weight This method assigns equal weights to all components.
This would be most useful when the returns across all interested assets are purely random and we have no views. The annualized return is This method gained popularity after the crisis. Risk parity works best in a world where the Sharpe ratios between all asset classes are the same and consequently equal risk contribution would contribute equal returns.
This would work best when the returns are not proportional to risk and lowering risk does not lead to lower returns as well.
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This looks quite similar to the equal weight example and could be because the risks of the indices are similar and the optimizer based solution to low-risk portfolio stops at a local minimum. We have taken the portfolio with the options portfolio optimization level of risk, one could actually choose a risk-based on her risk tolerance.
On an overall level, we options portfolio optimization that mean-variance optimization is possibly the best method for our example. Now surely each of these methods could be of choice under different conditions contingent on different factors.
Technical Note—Options Portfolio Selection
So what are the factors to look at? Factors Affecting Portfolio Optimization Behavioural Factors The investor's risk outlook or risk aversion is obviously the most important factor to keep in mind while deciding the portfolio construction method.
The choice of instruments and the investment horizon guide the diversification available and the methodology so this is the most important factor. Correlation Correlation guides diversification.
- Portfolio Optimization Methods
- Published26 Nov Abstract In this paper, first, we study mean-absolute deviation MAD portfolio optimization model with cardinality constraints, short selling, and risk-neutral interest rate.
- Technical Note—Options Portfolio Selection | Operations Research
- Optimal Option Portfolio Strategies
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In their research posted recently Resolve Asset Management demonstrate that the optimization-based methods outperform naive methods only when there is an opportunity to diversify and a large number of risk factors present. For imperfectly correlated assets the portfolio returns and volatility are different from the weighted sum.
So correlation plays a big role in the choice of the portfolio construction method.
Market Regime We showed that minimum variance is optimal when all return assumptions are same and risk parity is optimal when all risk-adjusted returns are the same. Such scenarios actually occur as the markets change regimes.
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There are actual regimes where returns are inversely correlated to risk while others where higher risk is rewarded by higher returns.
This makes market regimes a good factor to look at when evaluating the portfolio construction methods.
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Conclusion I hope this article gives the reader a good start in the exploration of the wonderful field of research on portfolio construction and optimization. This interesting area of research can add levels of sophistication to any systematic trading strategy. The accuracy, completeness, and validity of any statements made or the links shared within this article are not guaranteed. We accept no liability for any errors, omissions or representations.
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