The Experts below are selected from a list of 12531 Experts worldwide ranked by ideXlab platform
Li Yang - One of the best experts on this subject based on the ideXlab platform.
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Hedging with futures does anything beat the naive Hedging Strategy
Management Science, 2015Co-Authors: Yudong Wang, Li YangAbstract:This paper investigates out-of-sample performance of the naive Hedging Strategy relative to that of the minimum variance Hedging Strategy, in which the covariance parameters are estimated from 18 e...
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Hedging with futures does anything beat the naive Hedging Strategy
Social Science Research Network, 2014Co-Authors: Yudong Wang, Li YangAbstract:This article investigates out-of-sample performance of the naive Hedging Strategy relative to that of the minimum variance Hedging Strategy, in which the covariance parameters are estimated from eighteen econometric models. Hedging performance is compared across twenty-four futures markets. Our main findings suggest that it is difficult to find a Strategy under the minimum variance framework that outperforms the naive Hedging Strategy both consistently and significantly. Our findings are robust to different sample periods, estimation windows and Hedging horizons, and can be partly explained by the effects of estimation error and model misspecification.
Irja Ida Ratikainen - One of the best experts on this subject based on the ideXlab platform.
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individual reversible plasticity as a genotype level bet Hedging Strategy
Journal of Evolutionary Biology, 2021Co-Authors: Thomas Ray Haaland, Jonathan Wright, Irja Ida RatikainenAbstract:Reversible plasticity in phenotypic traits allows organisms to cope with environmental variation within lifetimes, but costs of plasticity may limit just how well the phenotype matches the environmental optimum. An additional adaptive advantage of plasticity might be to reduce fitness variance, in other words: bet-Hedging to maximize geometric (rather than simply arithmetic) mean fitness. Here, we model the evolution of plasticity in the form of reaction norm slopes, with increasing costs as the slope or degree of plasticity increases. We find that greater investment in plasticity (i.e. a steeper reaction norm slope) is favoured in scenarios promoting bet-Hedging as a response to multiplicative fitness accumulation (i.e. coarser environmental grains and fewer time steps prior to reproduction), because plasticity lowers fitness variance across environmental conditions. In contrast, in scenarios with finer environmental grain and many time steps prior to reproduction, bet-Hedging plays less of a role and individual-level optimization favours evolution of shallower reaction norm slopes. However, the opposite pattern holds if plasticity costs themselves result in increased fitness variation, as might be the case for production costs of plasticity that depend on how much change is made to the phenotype each time step. We discuss these contrasting predictions from this partitioning of adaptive plasticity into short-term individual benefits versus long-term genotypic (bet-Hedging) benefits, and how this approach enhances our understanding of the evolution of optimum levels of plasticity in examples from thermal physiology to advances in avian lay dates.
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Individual reversible plasticity as a genotype-level bet-Hedging Strategy
2020Co-Authors: Thomas Ray Haaland, Jonathan Wright, Irja Ida RatikainenAbstract:Reversible plasticity in phenotypic traits allows organisms to cope with environmental variation within lifetimes, but costs of plasticity may limit just how well the phenotype matches the environmental optimum. An additional adaptive advantage of plasticity might be to reduce fitness variance, or bet-Hedging to maximize geometric (rather than simply arithmetic) mean fitness. Here we model the evolution of reaction norm slopes, with increasing costs as the slope or degree of plasticity increases. We find that greater investment in plasticity (i.e. steeper reaction norm slopes) is favoured in scenarios promoting bet-Hedging as a response to multiplicative fitness accumulation (i.e. coarser environmental grains and fewer time steps prior to reproduction), because plasticity lowers fitness variance across environmental conditions. In contrast, in scenarios with finer environmental grain and many time steps prior to reproduction, bet-Hedging plays less of a role and individual-level optimization favours evolution of shallower reaction norm slopes. We discuss contrasting predictions from this partitioning of the different adaptive causes of plasticity into short-term individual benefits versus long-term genotypic (bet-Hedging) benefits under different costs of plasticity scenarios, thereby enhancing our understanding of the evolution of optimum levels of plasticity in examples from thermal physiology to advances in avian lay dates.
Yudong Wang - One of the best experts on this subject based on the ideXlab platform.
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Hedging with futures does anything beat the naive Hedging Strategy
Management Science, 2015Co-Authors: Yudong Wang, Li YangAbstract:This paper investigates out-of-sample performance of the naive Hedging Strategy relative to that of the minimum variance Hedging Strategy, in which the covariance parameters are estimated from 18 e...
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Hedging with futures does anything beat the naive Hedging Strategy
Social Science Research Network, 2014Co-Authors: Yudong Wang, Li YangAbstract:This article investigates out-of-sample performance of the naive Hedging Strategy relative to that of the minimum variance Hedging Strategy, in which the covariance parameters are estimated from eighteen econometric models. Hedging performance is compared across twenty-four futures markets. Our main findings suggest that it is difficult to find a Strategy under the minimum variance framework that outperforms the naive Hedging Strategy both consistently and significantly. Our findings are robust to different sample periods, estimation windows and Hedging horizons, and can be partly explained by the effects of estimation error and model misspecification.
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Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?
Energy Economics, 2012Co-Authors: Yudong Wang, Chongfeng WuAbstract:In this paper, we forecast energy market volatility using both univariate and multivariate GARCH-class models. First, we forecast volatilities of individual assets and find that multivariate models display better performance than univariate models. Second, we forecast crack spread volatility and contrast the performance of multivariate models for two underlyings, with the alternative of univariate ones for crack spreads directly. Our evidence shows that univariate models allowing for asymmetric effects display the greatest accuracy. We also discuss the Hedging Strategy based on multivariate models and its implications for market participants.
Hong Zou - One of the best experts on this subject based on the ideXlab platform.
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the effectiveness of using a basis Hedging Strategy to mitigate the financial consequences of weather related risks
The North American Actuarial Journal, 2010Co-Authors: Linda L Golden, Charles C Yang, Hong ZouAbstract:Abstract This paper examines the effectiveness of using a Hedging Strategy involving a basis derivative instrument to reduce the negative financial consequences of weather-related risks. We examine the effectiveness of using this basis derivative Strategy for both summer and winter seasons, using both linear and nonlinear Hedging instruments and the impacts of default risk and perception errors on weather Hedging efficiency. We also compare the Hedging effectiveness obtained using weather indices produced by both the Chicago Mercantile Exchange (CME) and Risk Management Solutions, Inc. (RMS). The results indicate that basis Hedging is significantly more effective for the winter season than for the summer season, whether using the CME or RMS weather indices, and whether using linear or nonlinear derivative instruments. It is also found that the RMS regional weather indices are more effective than the CME weather indices, and the effectiveness of using either linear or nonlinear Hedging instruments for weat...
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the effectiveness of using a basis Hedging Strategy to mitigate the financial consequences of weather related risks
Social Science Research Network, 2009Co-Authors: Linda L Golden, Charles C Yang, Hong ZouAbstract:This paper examines the effectiveness of using a Hedging Strategy involving a basis derivative instrument to reduce the negative financial consequences of weather-related risks. We examine the effectiveness of using this basis derivative Strategy for both summer and winter seasons, using both linear and nonlinear Hedging instruments and the impacts of default risk and perception errors on weather Hedging efficiency. We also compare the Hedging effectiveness obtained using weather indices produced by both the Chicago Mercantile Exchange (CME) and Risk Management Solutions, Inc. (RMS). The results indicate that basis Hedging is significantly more effective for the winter season than for the summer season, whether using the CME or RMS weather indices, and whether using linear or nonlinear derivative instruments. It is also found that the RMS regional weather indices are more effective than the CME weather indices, and the effectiveness of using either linear or nonlinear Hedging instruments for weather risk management can vary significantly depending on the region of the country. In addition, the results indicate that default risk has some impact on nonlinear basis Hedging efficiency but no impact on linear basis Hedging efficiency, and reasonable perception errors on default risk have no impact on either linear or nonlinear basis Hedging efficiency.
Thomas Ray Haaland - One of the best experts on this subject based on the ideXlab platform.
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individual reversible plasticity as a genotype level bet Hedging Strategy
Journal of Evolutionary Biology, 2021Co-Authors: Thomas Ray Haaland, Jonathan Wright, Irja Ida RatikainenAbstract:Reversible plasticity in phenotypic traits allows organisms to cope with environmental variation within lifetimes, but costs of plasticity may limit just how well the phenotype matches the environmental optimum. An additional adaptive advantage of plasticity might be to reduce fitness variance, in other words: bet-Hedging to maximize geometric (rather than simply arithmetic) mean fitness. Here, we model the evolution of plasticity in the form of reaction norm slopes, with increasing costs as the slope or degree of plasticity increases. We find that greater investment in plasticity (i.e. a steeper reaction norm slope) is favoured in scenarios promoting bet-Hedging as a response to multiplicative fitness accumulation (i.e. coarser environmental grains and fewer time steps prior to reproduction), because plasticity lowers fitness variance across environmental conditions. In contrast, in scenarios with finer environmental grain and many time steps prior to reproduction, bet-Hedging plays less of a role and individual-level optimization favours evolution of shallower reaction norm slopes. However, the opposite pattern holds if plasticity costs themselves result in increased fitness variation, as might be the case for production costs of plasticity that depend on how much change is made to the phenotype each time step. We discuss these contrasting predictions from this partitioning of adaptive plasticity into short-term individual benefits versus long-term genotypic (bet-Hedging) benefits, and how this approach enhances our understanding of the evolution of optimum levels of plasticity in examples from thermal physiology to advances in avian lay dates.
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Individual reversible plasticity as a genotype-level bet-Hedging Strategy
2020Co-Authors: Thomas Ray Haaland, Jonathan Wright, Irja Ida RatikainenAbstract:Reversible plasticity in phenotypic traits allows organisms to cope with environmental variation within lifetimes, but costs of plasticity may limit just how well the phenotype matches the environmental optimum. An additional adaptive advantage of plasticity might be to reduce fitness variance, or bet-Hedging to maximize geometric (rather than simply arithmetic) mean fitness. Here we model the evolution of reaction norm slopes, with increasing costs as the slope or degree of plasticity increases. We find that greater investment in plasticity (i.e. steeper reaction norm slopes) is favoured in scenarios promoting bet-Hedging as a response to multiplicative fitness accumulation (i.e. coarser environmental grains and fewer time steps prior to reproduction), because plasticity lowers fitness variance across environmental conditions. In contrast, in scenarios with finer environmental grain and many time steps prior to reproduction, bet-Hedging plays less of a role and individual-level optimization favours evolution of shallower reaction norm slopes. We discuss contrasting predictions from this partitioning of the different adaptive causes of plasticity into short-term individual benefits versus long-term genotypic (bet-Hedging) benefits under different costs of plasticity scenarios, thereby enhancing our understanding of the evolution of optimum levels of plasticity in examples from thermal physiology to advances in avian lay dates.