Optimal futures hedging under jump switching dynamics

Lee, Hsiang‐Tai

doi:10.1016/j.jempfin.2008.12.001

Cited by 40 publications

(37 citation statements)

References 22 publications

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“…Our results show that contracts with three months to delivery are the most liquid as they exhibit the lowest effective spread, the lowest percentage of zero returns, and the smallest value for the Amihud (2002) illiquidity measure. This is different from the majority of futures markets and contracts for which the nearby contracts are usually the most liquid (see Baillie et al 2007;Lee 2009; and the references therein). Crucially, this liquidity pattern results from the unique institutional environment in which trading takes place.…”

Section: Introductionmentioning

confidence: 83%

Volatility forecasting in the Chinese commodity futures market with intraday data

Jiang

Ahmed

Liu

2016

Rev Quant Finan Acc

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Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.

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Section: Introductionmentioning

confidence: 83%

Volatility forecasting in the Chinese commodity futures market with intraday data

Jiang

Ahmed

Liu

2016

Rev Quant Finan Acc

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“…utility function described above. Mean-variance utility has been commonly adopted as a measure of hedging performance (Lee, 2009;Lien & Yang, 2008;Gagnon et al, 1998;Kroner & Sultan, 1993). By measuring performance using the same risk aversion level as assumed in the calculation of the mean-variance hedge ratio, we give a consistent view of the hedger's preferences.…”

Section: Hedging Performancementioning

confidence: 99%

“…In order to test the performance of the prescribed hedging strategy at each risk aversion level and time horizon, the time-series is split into two equal segments, where the first is used to calculate the optimal hedge ratio in-sample and the second to test the performance of the strategy out-of-sample (Lee, 2009). At each time horizon, the out-of-sample performance captures the average performance realized by an investor with that horizon, rather than the real-time hedging performance.…”

Section: )mentioning

confidence: 99%

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Commodity futures hedging, risk aversion and the hedging horizon

Conlon

Cotter

Gençay

2015

The European Journal of Finance

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This paper examines the impact of investor preferences on the optimal futures hedging strategy and associated hedging performance. Explicit risk aversion levels are often overlooked in hedging analysis. Applying a mean-variance hedging objective, the optimal futures hedging ratio is determined for a range of investor preferences on risk aversion, hedging horizon and expected returns. Wavelet analysis is applied to illustrate how investor time horizon shapes hedging strategy. Empirical results reveal substantial variation of the optimal hedge ratio for distinct investor preferences and are supportive of the hedging policies of real firms.Hedging performance is then shown to be strongly dependent on underlying preferences. In particular, investors with high levels of risk aversion and a short horizon reduce the risk of the hedge portfolio but achieve inferior utility in comparison to those with low risk aversion.

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“…Bungton & Elliott (2002) derive an approximate formula for American option prices. Beyond the Gaussian mixture models, extensions address GARCH eects (Duan et al, 2002) and jumps (Lee, 2009a), for example. hedging is the cornerstone of any hedging strategy since it allows for perfect replication.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Optimal hedging when the underlying asset follows a regime-switching Markov process

François

Gauthier

Godin

2014

European Journal of Operational Research

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Abstract:We develop a flexible discrete-time hedging methodology that minimizes the expected value of any desired penalty function of the hedging error within a general regimeswitching framework. A numerical algorithm based on backward recursion allows for the sequential construction of an optimal hedging strategy. Numerical experiments comparing this and other methodologies show a relative expected penalty reduction ranging between 0.9% and 12.6% with respect to the best benchmark.

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Optimal futures hedging under jump switching dynamics

Cited by 40 publications

References 22 publications

Volatility forecasting in the Chinese commodity futures market with intraday data

Volatility forecasting in the Chinese commodity futures market with intraday data

Commodity futures hedging, risk aversion and the hedging horizon

Optimal hedging when the underlying asset follows a regime-switching Markov process

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