Building a high quality long term hold portfolio with options

I want to build a balanced portfolio using covered puts and then generate cash using covered calls. To me it seems simple but everyone around me is telling me not to do it. They're suggesting I just lump sum into it which makes absolutely no sense to me since whether I sell puts and get exercised and the stock price goes down is the same as whether I lump summed into it today and rode it down. At least with first method, I collect premium to soften the blow. What am i missing? I've looked at this fifty times and don't understand what the 'additional risk' is

So this is what I am contemplating and I'd like suggestions for the best ETFs to use for good put and call premiums:

1 all market / broad market ETF – thinking SPY/SPLG/VTI

1 bond market etf – no idea what but should have strong options activity

1 international stocks etf – thinking VEA or VXUS

1 dividend ETF – thinking VIG

1 REIT etf – thinking VNQ or REZ

These would be the entirety of my portfolio and the idea would be to buy them using cash covered puts until I get exercised, then sell covered calls and rinse and repeat so that I'm holding high quality stocks and generating (hopefully) substantial cash flow from them using options

Would these be the right stocks to do this with or is there a better selection / way to do it?

Other info: Total investable funds today are $375k cash. have no margin on my accounts and don't want it. I have no interest in trading options or buying unsecured options. This is purely to acquire a great portfolio and generate returns above and beyond what the stocks pay by selling options against them. It is all going to be in a taxable account.

Building a high quality long term hold portfolio with options
byu/_A_Silly_Goose_ inoptions

Posted by _A_Silly_Goose_

Few_Quarter5615 on September 11, 2024 12:17 am

You practically need a portfolio comprised of liquid etfs, with options chains that have different sector, factor and asset coverage that you want to weigh in such a way to minimize risk, maximize sharpe, maximize diversification, or other portfolio optimization model that fits your risk appetite in conjunction with the market regime and/or volatility regime.

These are the most known portfolio optimization models according to chat GPT:

– There are several portfolio optimization models, each designed to address different investment objectives, constraints, and preferences, such as maximizing return, minimizing risk, accounting for liquidity, and factoring in other real-world considerations. Here’s a list of the most common and advanced portfolio optimization models:

### 1. **Mean-Variance Optimization (MVO)**
– **Objective**: Maximizes the expected return for a given level of risk or minimizes risk for a given expected return.
– **Key Concept**: Balances risk (variance) and return based on historical data.
– **Pioneered By**: Harry Markowitz.
– **Challenges**: Sensitive to estimation errors in expected returns and covariances.

### 2. **Black-Litterman Model**
– **Objective**: Combines market equilibrium returns with an investor’s views to create a more robust portfolio.
– **Key Concept**: Starts with a global market-capitalization-weighted portfolio and adjusts based on the investor’s unique views.
– **Strength**: Reduces sensitivity to estimation errors in the expected returns used in Mean-Variance Optimization.

### 3. **Minimum-Variance Portfolio**
– **Objective**: Constructs a portfolio that minimizes total portfolio variance (risk), without explicitly considering expected returns.
– **Key Concept**: Focuses purely on minimizing risk by weighting assets that have low covariance with each other.

### 4. **Maximum Sharpe Ratio (Tangency Portfolio)**
– **Objective**: Maximizes the Sharpe ratio, which measures return per unit of risk.
– **Key Concept**: Seeks the portfolio on the efficient frontier that has the highest Sharpe ratio (return-to-risk ratio).
– **Strength**: Balances return and risk more efficiently than other models when comparing relative performance.

### 5. **Equal-Weight Portfolio**
– **Objective**: Allocates an equal percentage of capital to all assets in the portfolio.
– **Key Concept**: A simple approach that disregards variance, covariance, and returns.
– **Strength**: Easy to implement but lacks optimization in terms of return or risk.

### 6. **Risk Parity**
– **Objective**: Allocates capital in such a way that each asset contributes equally to the total risk of the portfolio.
– **Key Concept**: Focuses on distributing risk evenly across all portfolio components, rather than capital allocation.
– **Strength**: Can improve risk-adjusted returns, especially in volatile markets.

### 7. **Maximum Diversification**
– **Objective**: Maximizes diversification by selecting assets with the lowest correlation to each other.
– **Key Concept**: Aims to construct a portfolio where the diversification ratio (the ratio of weighted average asset volatility to portfolio volatility) is maximized.

### 8. **Mean Conditional Value at Risk (CVaR) Optimization**
– **Objective**: Minimizes Conditional Value at Risk (CVaR), or the expected loss in the worst-case scenarios beyond a certain confidence level.
– **Key Concept**: Focuses on extreme downside risk (tail risk) rather than variance.
– **Strength**: Suitable for risk-averse investors, especially in markets prone to significant tail risks.

### 9. **Mean-Variance Skewness Optimization (MVS)**
– **Objective**: Considers skewness (asymmetry in returns) in addition to mean and variance.
– **Key Concept**: Investors prefer positive skewness (greater chance of extreme positive returns), so this model incorporates that preference into the optimization process.

### 10. **Mean-Variance Skewness Kurtosis Optimization (MVSK)**
– **Objective**: Extends MVO by considering not only mean, variance, and skewness but also kurtosis (the “fat tails” in return distributions).
– **Key Concept**: Addresses both asymmetry and fat tails in return distributions, which is important in modeling real-world return patterns.

Continuation in reply below

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3 Comments

CSachen on September 10, 2024 11:53 pm

Everyone tells you to not do it because in back tests a buy-and-hold strategy outperforms a covered call strategy (or any wheel strategy).

It performs particularly poorly when the underlying moves consistently up like it has in 2024.
Few_Quarter5615 on September 11, 2024 12:17 am

You practically need a portfolio comprised of liquid etfs, with options chains that have different sector, factor and asset coverage that you want to weigh in such a way to minimize risk, maximize sharpe, maximize diversification, or other portfolio optimization model that fits your risk appetite in conjunction with the market regime and/or volatility regime.

These are the most known portfolio optimization models according to chat GPT:

– There are several portfolio optimization models, each designed to address different investment objectives, constraints, and preferences, such as maximizing return, minimizing risk, accounting for liquidity, and factoring in other real-world considerations. Here’s a list of the most common and advanced portfolio optimization models:

### 1. **Mean-Variance Optimization (MVO)**
– **Objective**: Maximizes the expected return for a given level of risk or minimizes risk for a given expected return.
– **Key Concept**: Balances risk (variance) and return based on historical data.
– **Pioneered By**: Harry Markowitz.
– **Challenges**: Sensitive to estimation errors in expected returns and covariances.

### 2. **Black-Litterman Model**
– **Objective**: Combines market equilibrium returns with an investor’s views to create a more robust portfolio.
– **Key Concept**: Starts with a global market-capitalization-weighted portfolio and adjusts based on the investor’s unique views.
– **Strength**: Reduces sensitivity to estimation errors in the expected returns used in Mean-Variance Optimization.

### 3. **Minimum-Variance Portfolio**
– **Objective**: Constructs a portfolio that minimizes total portfolio variance (risk), without explicitly considering expected returns.
– **Key Concept**: Focuses purely on minimizing risk by weighting assets that have low covariance with each other.

### 4. **Maximum Sharpe Ratio (Tangency Portfolio)**
– **Objective**: Maximizes the Sharpe ratio, which measures return per unit of risk.
– **Key Concept**: Seeks the portfolio on the efficient frontier that has the highest Sharpe ratio (return-to-risk ratio).
– **Strength**: Balances return and risk more efficiently than other models when comparing relative performance.

### 5. **Equal-Weight Portfolio**
– **Objective**: Allocates an equal percentage of capital to all assets in the portfolio.
– **Key Concept**: A simple approach that disregards variance, covariance, and returns.
– **Strength**: Easy to implement but lacks optimization in terms of return or risk.

### 6. **Risk Parity**
– **Objective**: Allocates capital in such a way that each asset contributes equally to the total risk of the portfolio.
– **Key Concept**: Focuses on distributing risk evenly across all portfolio components, rather than capital allocation.
– **Strength**: Can improve risk-adjusted returns, especially in volatile markets.

### 7. **Maximum Diversification**
– **Objective**: Maximizes diversification by selecting assets with the lowest correlation to each other.
– **Key Concept**: Aims to construct a portfolio where the diversification ratio (the ratio of weighted average asset volatility to portfolio volatility) is maximized.

### 8. **Mean Conditional Value at Risk (CVaR) Optimization**
– **Objective**: Minimizes Conditional Value at Risk (CVaR), or the expected loss in the worst-case scenarios beyond a certain confidence level.
– **Key Concept**: Focuses on extreme downside risk (tail risk) rather than variance.
– **Strength**: Suitable for risk-averse investors, especially in markets prone to significant tail risks.

### 9. **Mean-Variance Skewness Optimization (MVS)**
– **Objective**: Considers skewness (asymmetry in returns) in addition to mean and variance.
– **Key Concept**: Investors prefer positive skewness (greater chance of extreme positive returns), so this model incorporates that preference into the optimization process.

### 10. **Mean-Variance Skewness Kurtosis Optimization (MVSK)**
– **Objective**: Extends MVO by considering not only mean, variance, and skewness but also kurtosis (the “fat tails” in return distributions).
– **Key Concept**: Addresses both asymmetry and fat tails in return distributions, which is important in modeling real-world return patterns.

Continuation in reply below
Front_Expression_892 on September 11, 2024 12:24 am

I think that 100% VT/VTI/VOO (depending on your risk appetite and beliefs about how large an “all-market” index should be) is a great start while you take your time and learn about active investing using a paper account.

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