Risk Parity Etf Investment Portfolio for Diversified Returns

A risk parity ETF investment portfolio is designed to provide diversified returns by allocating assets based on their risk contribution rather than their expected return. This approach allows for a more balanced portfolio that can potentially reduce overall portfolio risk.

Risk parity ETFs aim to allocate assets in proportion to their risk contribution, which can be measured by various metrics such as volatility or value-at-risk. By doing so, investors can potentially benefit from a more stable portfolio that is less sensitive to market fluctuations.

Investing in a risk parity ETF can be a great way to diversify your portfolio, especially for those who are new to investing or want to reduce their overall risk exposure.

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Risk Parity ETFs are designed to allocate assets in a way that each one contributes the same amount of "risk" to the portfolio, with volatility being the proxy used for risk.

For example, the historical volatility of long U.S. stocks is about 15%, while U.S. treasury bonds have a volatility of about 10%, so a 40/60 allocation of stocks to long treasuries achieves risk parity, with each asset contributing the same 6% volatility.

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A 40/60 portfolio is considered pretty conservative, and the theory behind risk parity is that diversification benefits are maximized when assets are allocated in this way, leading to higher risk-adjusted returns.

The greatest risk-adjusted return is achieved historically by a 30/70 allocation of stocks and intermediate treasury bonds, which is roughly risk parity for those two assets.

Bitcoin's volatility is so high that its risk parity weighting in a portfolio alongside stocks and long treasury bonds is only 3%, with long bonds getting 54% and stocks getting 43%.

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Risk parity ETFs provide a balanced mix of publicly traded assets that seeks to outperform equities with comparable risk.

By diversifying across four asset classes, each with unique environmental biases, risk parity can potentially offer higher returns relative to risk compared to equities or equity-centric portfolios.

A risk parity ETF can be used as an alternative asset or a total public portfolio solution, offering a more diversified style of long-only asset allocation.

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Here's a breakdown of how risk parity can be used:

By improving diversification, risk parity can potentially offer higher returns relative to risk compared to equities or equity-centric portfolios, making it a valuable addition to any investment strategy.

A low roll period is a term used to describe the worst-case scenario for an investment portfolio. In the case of a Risk Parity Portfolio, the low roll period was -13.63% over a 1 year period.

This is significantly better than the 60/40 Portfolio, which had below water performance that would've required investors to endure 5 years of misery.

The Risk Parity Portfolio's low roll period is a major advantage over the 60/40 Portfolio, making it a more attractive option for investors.

Risk parity is a strategy that provides balance by spreading risk equally across four diverse asset classes. This approach has been shown to achieve reliable diversification based on a dependable relationship between asset class returns and the economic environment.

A balanced mix of publicly traded assets can offer attractive returns relative to risk, potentially outperforming equities with comparable risk. By diversifying across four asset classes, each with unique environmental biases, risk parity can provide a more stable investment option.

The RPAR ETF is a time-tested risk parity methodology used by sophisticated investors, implemented in a tax-efficient and liquid ETF structure. This approach seeks an equity-like return with less risk over the long run.

Here are some key benefits of risk parity:

By improving diversification, risk parity can potentially offer higher returns relative to risk compared to equities or equity-centric portfolios.

Factor Based Investing is a strategy that focuses on matching the risk factor exposures of a target asset or portfolio using a combination of assets. This approach can help investors achieve their desired level of risk and return.

By using a tool that finds the combination of assets that most closely clones the factor exposures of the target asset or portfolio, investors can create a diversified portfolio that matches their desired risk profile. This can be achieved through the "Match Risk Factor Exposure" tool.

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For example, if an investor wants to match the factor exposures of a specific ETF, they can use the "Match Risk Factor Exposure" tool to find the combination of assets that most closely clones the factor exposures of that ETF. This can help investors create a tailored portfolio that meets their investment goals.

Risk parity is another key concept in factor based investing. It involves spreading risk equally across four diverse asset classes, such as equities, fixed income, commodities, and gold. This approach can help investors achieve reliable diversification and attractive returns relative to risk.

Here are some examples of how risk parity can be implemented:

By optimizing portfolio asset allocation based on shifts in targeted risk factor exposures, investors can create a more diversified portfolio that matches their desired level of risk and return. This approach can also help investors achieve higher returns relative to risk compared to equities or equity-centric portfolios.

Tactical Dual Momentum is a model that combines relative momentum with an absolute momentum based trend-following filter. This approach can help investors make more informed decisions by considering both the relative performance of assets and the overall market trend.

The dual momentum model is an adaptive asset allocation strategy that adjusts its weights based on the performance of different asset classes. For example, it can hold top two best performing assets with risk parity weighting, as seen in the Adaptive Allocation model.

By combining relative strength momentum with inverse volatility or minimum variance based asset weights, the dual momentum model aims to optimize portfolio returns while managing risk. This is a key consideration for investors looking to balance their portfolio's performance and volatility.

To implement a dual momentum strategy, you can use a combination of moving averages, momentum, market valuation, and volatility targeting. This will help you identify the best-performing assets and adjust your portfolio's weights accordingly.

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Here's a table illustrating the dual momentum model's asset allocation:

Note that this is just one example of a dual momentum model, and you may need to adjust the weights and asset classes based on your individual investment goals and risk tolerance.

Asset Allocation is a crucial aspect of a Risk Parity ETF. The RPAR ETF Equity Allocation offers investors a diverse mix of US, International Developed, and Emerging Markets equities.

US equities take up half of the equation, making up 12.5% of the 25% equity sleeve. This suggests a significant focus on domestic markets.

International Developed and Emerging Markets round out the equity sleeve, taking up 5% and 7.5% of the 25% allocation, respectively.

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In fixed income, diversification is key, and the RPAR ETF Fixed Income Allocation is a great example of this. They allocate 35% to US TIPS (15+ year) and 35% to US Treasuries (10 Year and Ultra via futures).

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The 10-Year Treasury and Ultra Treasury are covered by capital efficient futures, which is a clever move.

High correlation between the 10-Year Treasury and Long Term Treasury is evident, with a correlation coefficient of 0.93. This is a red flag for investors who want to spread their risk.

TIPS, on the other hand, provide a diversification benefit by being less correlated to both the 10-Year Treasury (0.69) and Long-Term Treasury (0.64). This makes them a great addition to the mix.

The lack of short-term and intermediate-term treasuries in the RPAR ETF Fixed Income Allocation is a potential liability at times.

The RPAR ETF takes a unique approach by focusing on commodity producing equities rather than direct commodity exposure. This decision is reflected in its 141 stock portfolio.

One of the benefits of this approach is the diversification it provides, with allocations spread across 5.25% Energy, 5.25% Diversified Mining, 3.0% Agriculture, 0.75% Clean Energy, and 0.75% Water.

The RPAR ETF also has a 10% allocation to the SPDR GOLD MINISHARES TRUST ETF (GLDM) for added gold exposure.

Risk Management is key to achieving long-term success with a Risk Parity ETF. Stability is a hallmark of this investment strategy, as evidenced by its performance during historical market stress periods such as the Subprime Crisis, Dotcom Crash, and Black Monday Period.

The use of leverage is a crucial risk management tool in Risk Parity investing. By judiciously applying leverage, investors can reduce the volatility of their portfolio and achieve equity-like returns with reduced risk. For example, a portfolio with 35% allocated to equities and 65% to bonds can see a 15% reduction in risk when using moderate leverage of around 2.1 times the amount of capital.

The Risk Parity Portfolio has demonstrated remarkable stability during historical market stress periods, including the Subprime Crisis, Dotcom Crash, and Black Monday Period.

This is particularly notable compared to the 60/40 Portfolio, which has shown more significant drawdowns during these periods.

The Risk Parity Portfolio's stability is a key benefit, as it can help investors navigate turbulent markets with less risk.

In fact, its worst-case scenario is a drawdown of -14.97%, which is significantly lower than the 60/40 Portfolio's worst-case scenario of -28.54%.

This highlights the Risk Parity Portfolio's ability to manage max drawdowns and provide more consistent returns over time.

By diversifying risk and using leverage, the Risk Parity Portfolio can reduce overall portfolio risk while still allowing for substantial returns.

US stock markets show a correlation of 0.75 with Emerging Markets, which is lower than the 0.85 correlation with International Developed Markets. This suggests a lower risk of a simultaneous downturn in US markets and Emerging Markets.

A more sizeable allocation to Emerging Markets than to International Developed Markets can be seen as a contrarian decision, but it makes sense from a correlations standpoint.

The lower correlation between US and Emerging Markets can help reduce overall portfolio risk.

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Correlations are a crucial aspect of risk management, and understanding how they work can help you make informed investment decisions. A correlation coefficient measures the statistical relationship between two asset prices, ranging from -1 (perfect inverse relationship) to +1 (perfect linear relationship).

A correlation of 0 indicates no statistical relationship between asset prices. However, including assets with negative correlations can improve the diversity of a portfolio.

You can see this in action with the RPAR Fixed Income Monthly Correlations, where the 10-Year Treasury and Long Term Treasury have a high correlation of 0.93, but TIPS provides a diversification benefit with lower correlations of 0.69 and 0.64.

Here's a breakdown of the correlations for the selected assets in the Asset Correlations section:

This table shows the daily correlations from 01/01/2011 to 12/31/2016 for the Vanguard Total Stock Market ETF (VTI), Vanguard Real Estate ETF (VNQ), SPDR Gold Shares ETF (GLD), and Vanguard Total Bond Market ETF (BND).

Regular rebalancing is required to maintain a risk parity approach, especially when using leverage. This involves even out the leveraged investments to keep volatility exposure for each asset class level.

Rebalancing requires active management, particularly for assets like commodities and derivatives that demand closer attention. Margin calls may arise, necessitating cash to maintain positions.

Investors must also roll positions to a different month rather than holding contracts until expiration, which requires active management of those positions and cash in the portfolio.

Using leverage increases the risk, including the risk of counterparty default.

To illustrate the importance of rebalancing, consider the example of a portfolio with the following risk budget:

Analyzing the performance of a Risk Parity ETF can be a bit tricky, but it's essential to understand its historical performance. RPAR, a relatively new ETF, has shown mixed results in its backtest from January 2020 to June 2021, with a 60/40 global stocks and long treasuries benchmark outperforming it in terms of volatility and drawdowns.

RPAR's risk-adjusted return, measured by the Sharpe ratio, was the lowest among the portfolios tested. This suggests that the ETF may not be delivering on its promise of balanced risk and return.

Asset analytics can help you find funds based on various criteria, including asset class, style, and risk-adjusted performance. This can be a useful tool for investors looking to optimize their portfolios.

RPAR, the Risk Parity ETF, has a relatively short history, with a backtest from January 2020 through June 2021 showing some concerning trends.

RPAR's performance during this period was underwhelming, failing to deliver on its promise of reduced volatility and smaller drawdowns.

The 60/40 global stocks and long treasuries benchmark outperformed RPAR in terms of volatility and drawdowns.

RPAR's risk-adjusted return, measured by the Sharpe ratio, was actually the lowest among the portfolios tested.

In fact, a 10-year backtest using constituent funds and simulated proxies showed similar results, with RPAR struggling to keep up with SWAN and 60/40.

US equities have historically been a volatile asset class with a 15.76% standard deviation dating back to 1972.

Gold is even more volatile, with a whopping 19.81% standard deviation over the same period.

The 10-Year Treasury, on the other hand, is a relatively stable investment with an 8.05% standard deviation.

Intermediate Term Treasury is even more stable, with a 5.78% standard deviation.

To manage risk, it's essential to understand the volatility of different asset classes.

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Analyzing autocorrelation of returns is crucial for forecasting and risk management. It helps identify patterns in asset returns.

An asset's autocorrelation can be analyzed for multiple lag periods, providing insights into the persistence of its returns. This can aid in making informed investment decisions.

Autocorrelation is calculated for selected assets over a specified time period and lag. The results can reveal whether an asset's returns are influenced by previous returns.

By understanding an asset's autocorrelation, you can better anticipate its future performance. This knowledge can also help you manage risk more effectively.

Dynamic Performance is all about how well a portfolio performs over time. The key is to test a historical sequence of dynamic portfolio allocations, which means looking at how the portfolio's assets and weights changed over time.

In the example of Dynamic Allocation Performance, we see that the portfolio model's assets and weights changed significantly between January 2022 and April 2023. This is a crucial aspect of dynamic performance.

The allocation to SPY (a stock market index fund) increased from 50% on January 1, 2022, to 90% on January 1, 2023, indicating a strong focus on stocks during this period.

However, by April 2023, the allocation to SPY had decreased to 70%, while the allocation to BND (a bond fund) had increased to 20%. This suggests a shift towards a more balanced portfolio.

Here's a summary of the changes in the portfolio's allocations over time:

Manager Performance Analysis is a crucial step in evaluating the effectiveness of a manager's investment strategy.

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Analyzing the sources of risk and return of manager returns is essential to compare those against the selected benchmark.

The Monte Carlo simulation tool can be used to model the probability of different outcomes based on the given portfolio asset allocation and cashflows.

This tool helps to identify potential risks and opportunities, allowing for more informed investment decisions.

Principal Component Analysis is a powerful tool used to identify the underlying statistical factors driving asset returns. It's a way to break down complex data into its most fundamental components.

By using Principal Component Analysis, you can identify the independent sources of risk that drive portfolio variance. This is achieved by analyzing asset returns and extracting the statistical factors that are driving those returns.

Principal Component Analysis is particularly useful when trying to understand the relationships between different assets. For example, Example 7 states that Principal Component Analysis can be used to analyze asset returns and identify the underlying statistical factors.

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Here's an example of how Principal Component Analysis can be applied:

By understanding these statistical factors, you can gain valuable insights into the underlying drivers of asset returns. This can be particularly useful for investors looking to optimize their portfolio allocation.

In addition to identifying the underlying statistical factors, Principal Component Analysis can also be used to create a portfolio that is optimized for a specific set of risk factors.