Profit Margin Hedging

Brorsen, B. Wade; Anderson, Kim B.

doi:10.1093/ajae/aap038

Cited by 18 publications

(7 citation statements)

References 37 publications

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“…Previous research has not found evidence of mean reversion patterns in commodity futures (Irwin, Zulauf, and Jackson (1996) and Kim, Brorsen, and Anderson (2010)). Therefore, the theoretical model used here assumes that futures prices are not mean reverting.…”

Section: Discussionmentioning

confidence: 82%

Price Mean Reversion, Seasonality, and Options Markets

Hart¹,

Lence

Hayes

et al. 2015

American J Agri Economics

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Options on agricultural commodities with maturities exceeding one year seldom trade. One possible reason to explain this lack of trading is that we do not have an accurate option pricing model for products where mean reversion in spot-price levels can be expected. Standard option pricing models assume proportionality between price variance and time to maturity. This proportionality is not a valid assumption for commodities whose supply response brings prices back to production costs. The model proposed here incorporates mean reversion in spot-price levels and includes a correction for seasonality. Mean reversion and seasonality are both observed in the soybean market. The empirical analysis lends strong support to the model. KeywordsBayesian statistics, commodity markets, mean reversion, options, seasonality Disciplines Agricultural and Resource Economics | Growth and Development | Regional Economics CommentsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in American Journal of Agricultural Economics following peer review. AbstractOptions on agricultural commodities with maturities exceeding one year seldom trade. One

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Section: Discussionmentioning

confidence: 82%

Price Mean Reversion, Seasonality, and Options Markets

Hart¹,

Lence

Hayes

et al. 2015

American J Agri Economics

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“…In addition, PT fits the agricultural context particularly well. On the one hand, it is likely that farmers do have reference points for outcome valuation, such as target prices on the futures markets (based on production costs) (Kim, Brorsen and Anderson, 2010), subsistence incomes and solvency thresholds in the context of production choices (e.g. Mahul, 2000), and pollution thresholds (Qiu, Prato and McCamley, 2001).…”

Section: Introductionmentioning

confidence: 99%

Expected utility or prospect theory maximisers? Assessing farmers' risk behaviour from field-experiment data

Bocquého

Jacquet

Reynaud

2013

European Review of Agricultural Economics

141

152

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We elicit the risk preferences of a sample of French farmers in a field-experiment setting, considering both expected utility and cumulative prospect theory. Under the EU framework, our results show that farmers are characterised by a concave utility function for gain outcomes implying risk aversion. The CPT framework confirms this result, but also suggests that farmers are twice as sensitive to losses as to gains and tend to pay undue attention to unlikely extreme outcomes. Accounting for loss aversion and probability weighting can make a difference in the design of effective and efficient policies, contracts or insurance schemes.

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“…As discussed in Kim et al (2010), although profit margin hedging is not the optimal rule for mean reversion, it can still be profitable if prices are mean-reverting. The need for a measure, which will represent both profit and loss, has led us to an extended equation.…”

Section: Trading Systems Assessment Factorsmentioning

confidence: 99%

Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets

Vezeris¹,

Kyrgos

Karkanis

et al. 2020

Investment Management and Financial Innovations

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Given the popularity and propagation of automated trading systems in financial markets among institutional and individual traders in recent decades, this work attempts to compare and evaluate such ten systems based on different popular technical indicators in combination – for the first time – with the d-Backtest PS method for parameter selection. The systems use the technical indicators of Moving Averages (MA), Average Directional Index (ADX), Ichimoku Kinko Hyo, Moving Average Convergence/Divergence (MACD), Parabolic Stop and Reverse (SAR), Pivot, Turtle and Bollinger Bands (BB), and are enhanced by Stop Loss Strategies based on the Average True Range (ATR) indicator. Improvements in the speed of the back-testing computations used by the d-Backtest PS method over weekly intervals allowed examining all systems on a 3.5 years trading period for 7 assets in financial markets, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, XAU/USD, WTI, and BTC/USD. To evaluate the systems more holistically, a weighted metric is introduced and examined, which, apart from profit, takes into account more factors after normalization like the Sharpe Ratio, the Maximum Drawdown and the Expected Payoff, as well as a newly introduced Extended Profit Margin factor. Among the automated systems examined and evaluated using the weighted metric, the Adaptive Double Moving Average (Ad2MA) system stands out, followed by the Adaptive Pivot (AdPivot), and the Adaptive Average Directional Index (AdADX) systems. AcknowledgmentsWe would like to thank Dr. Christos Schinas for his time and invaluable guidance towards the methodology of the weighted metric. We would also like to thank Michalis Foulos for the hardware setup and support and Nektarios Mitakidis for his contribution to the representation of the results.This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-02342).

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Profit Margin Hedging

Cited by 18 publications

References 37 publications

Price Mean Reversion, Seasonality, and Options Markets

Price Mean Reversion, Seasonality, and Options Markets

Expected utility or prospect theory maximisers? Assessing farmers' risk behaviour from field-experiment data

Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets

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