Volatility‐managed commodity futures portfolios

Kang, Jangkoo; Kwon, Kee Hwan

doi:10.1002/fut.22175

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…From Panel B, we find that volatility management does not provide a universal performance improvement net of TC, as RB, skewness, and HP factors continue to report insignificant average returns, while volatility‐managed carry and BM factors deliver quantitatively identical results to their unmanaged counterparts. Consistent with Kang and Kwon (2021), and Xu and Wang (2021), we find momentum to be the only factor that gains meaningful improvements over the unmanaged factor. Turning to Panel C, we observe that net of TCs, factor strategies implemented with trailing‐stop continue to deliver statistically and economically significant average returns, with net Sharpe ratios ranging from 1.04 (skewness) to 1.43 (carry), and maximum drawdown ranging from −9.1% (carry) to −20% (skewness).…”

Section: Resultssupporting

confidence: 88%

Commodity premia and risk management

Fan,

Zhang

2024

Journal of Futures Markets

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We examine the role of risk management in the context of commodity factor premia. Stopping losses in individual commodities effectively improves the average returns of long‐short commodity premia through persistent reduction in the frequency and severity of drawdowns. The magnitude of improvement is related to the quality of the signal, commodity return volatility, and autocorrelations, as well as transaction costs. The efficacy of a stop‐loss strategy can be enhanced by dynamically calibrating loss thresholds in accordance with realized volatility, and it performs best in high conviction weighting schemes. Overall, we highlight the pivotal role of risk management beyond volatility targeting and risk‐parity in harnessing commodity risk premia.

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Section: Resultssupporting

confidence: 88%

Commodity premia and risk management

Fan,

Zhang

2024

Journal of Futures Markets

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“…The core explanatory variables include the volatility of the underlying asset and the forecast error. For other explanatory variables, we refer to some option factors mentioned by Buechner and Kelly (2022) and Kang and Kwon (2020). The time series regression is as follows where X indicates the different explanatory variables, including realized volatility, absolute value of predictive error, implied volatility (IV), embedded leverage (EL), and five Greek value of options.…”

Section: Regression Analysismentioning

confidence: 99%

Intelligent option portfolio model with perspective of shadow price and risk-free profit

2023

Financ Innov

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Since Markowitz proposed modern portfolio theory, portfolio optimization has been being a classic topic in financial engineering. Although it is generally accepted that options help to improve the market, there is still an improvement for the portrayal of their unique properties in portfolio problems. In this paper, an intelligent option portfolio model is developed that allows selling options contracts to earn option fees and considers the high leverage of options in the market. Deep learning methods are used to predict the forward price of the underlying asset, making the model smarter. It can find an optimal option portfolio that maximizes the final wealth among the call and put options with multiple strike prices. We use the duality theory to analyze the marginal contribution of initial assets, risk tolerance limit, and portfolio leverage limit for the final wealth. The leverage limit of the option portfolio has a significant impact on the return. To satisfy the investors with different risk preferences, we also give the conditions for the option portfolio to gain a risk-free return and replace the Conditional Value-at-Risk. Numerical experiments demonstrate that the intelligent option portfolio model obtains a satisfactory out-of-sample return, which is significantly positively correlated with the volatility of the underlying asset and negatively correlated with the forecast error of the forward price. The risk- free option model is effective in achieving the goal of no drawdown and gaining satisfactory returns. Investors can adjust the balance point between returns and risks according to their risk preference.

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Managing volatility in commodity momentum

Wang

2021

Journal of Futures Markets

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We empirically investigate the effect of managing volatility in commodity momentum. Using Chinese commodity futures data, we show that managing volatility significantly improves the performance of commodity momentum. Including the new strategy significantly enhances asset allocation performance. Then, we explore several potential explanations for the profitability of the new strategy. Commodity‐specific factors, business cycle risk, financial market conditions, investor sentiments, transaction costs, leverage constraints, and data‐snooping bias do not fully account for this profitability. The profitability is in line with the predictability of momentum‐specific risk rather than aggregate market risk. Comprehensive robustness checks support our main findings.

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Volatility‐managed commodity futures portfolios

Cited by 4 publications

References 33 publications

Commodity premia and risk management

Commodity premia and risk management

Intelligent option portfolio model with perspective of shadow price and risk-free profit

Managing volatility in commodity momentum

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