Optimal Hedging with Higher Moments

Brooks, Chris; Černý, Aleš; Miffre, Joëlle

doi:10.2139/ssrn.945807

Cited by 17 publications

(19 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the performance of these models has been mixed and in particular their out-of-sample performance has been average at best (Brooks Cerny and Miffre, 2012). This suggests that there is little to be gained in terms of hedging efficiency from the use of GARCH models despite their additional complexity, as compared with rolling window OLS models (Miffre, 2004).…”

Section: Model Based Hedgementioning

confidence: 99%

See 1 more Smart Citation

Performance of utility based hedges

Cotter

Hanly

2015

Energy Economics

View full text Add to dashboard Cite

Section: Model Based Hedgementioning

confidence: 99%

“…It has been applied in a hedging context by a number of papers including, Lien (2003), and Brooks, Cerny and Miffre (2012). It is defined as follows:…”

Section: Exponential Utilitymentioning

confidence: 99%

Performance of utility based hedges

Cotter

Hanly

2015

Energy Economics

View full text Add to dashboard Cite

“…Rao and Thakur (2007) also examined the efficiency for the Indian Futures market and Indian options markets by comparing the JSE (Johnson, 1960;Stein, 1961 andEderington, 1979) and HKM (Herbst, Kare and Marshall, 1993) The estimation of an optimal hedge ratio in all models assumes that the distribution of returns on the hedged portfolio is normal, which means that the mean and variance alone are sufficient to determine the optimal hedge ratio. However, there exists indisputable evidence to suggest that the return distributions of speculative assets depart from normality (Brooks et al, 2012). Therefore, Brooks et al, (2012) observed that ignorance of higher moments leads to sub-optimal hedge ratios and suggests a new methodology considering the impact of higher moments on the determination of optimal hedge ratios.…”

Section: Review Of Literaturementioning

confidence: 99%

“…However, there exists indisputable evidence to suggest that the return distributions of speculative assets depart from normality (Brooks et al, 2012). Therefore, Brooks et al, (2012) observed that ignorance of higher moments leads to sub-optimal hedge ratios and suggests a new methodology considering the impact of higher moments on the determination of optimal hedge ratios.…”

Section: Review Of Literaturementioning

confidence: 99%

Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India

Gupta

Kaur

2015

South East European Journal of Economics and Business

View full text Add to dashboard Cite

The present study examines the impact of the 2008 financial crisis on the hedging effectiveness of three index futures contracts traded on the National Stock Exchange of India for near, next and far month contracts over the sample period of January 2000 – June 2014. The hedge ratios were calculated using eight methods; Naive hedging, Ederington’s Model, Autoregressive Integrated Moving Average, Vector Autoregressive, Vector Error Correction Methodology, Generalized Autoregressive Conditional Heteroskedasticity, Exponential Generalized Autoregressive Conditional Heteroscedasticity and Threshold Generalized Autoregressive Conditional Heteroskedasticity. The study finds an improvement in hedging effectiveness during the post-crisis period, which implies that during the high-volatility period hedging effectiveness also improves. It was also found that near month futures contracts are a more effective tool for hedging as compared to next and far month contracts, which imply that liquidity is a more important determinant of hedging effectiveness than hedge horizons. The study also finds that a time-invariant hedge ratio is more efficient than time-variant hedging. Therefore, knowledge of sophisticated econometrical tools does not help to improve hedge effectiveness.

show abstract

“…Brooks, Cerny and Miffre (2007) There are a number of shortcomings in terms of the application of risk aversion in the variance based hedging literature. In the first place, few papers have incorporated risk aversion despite its centrality to the idea of hedging.…”

Section: Risk Aversion In the Hedging Literaturementioning

confidence: 99%

Time Varying Risk Aversion: An Application to Energy Hedging

Cotter

Hanly

2009

SSRN Journal

View full text Add to dashboard Cite

Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison.

show abstract

Optimal Hedging with Higher Moments

Cited by 17 publications

References 55 publications

Performance of utility based hedges

Performance of utility based hedges

Impact Of Financial Crisis On Hedging Effectiveness Of Futures Contracts: Evidence From The National Stock Exchange Of India

Time Varying Risk Aversion: An Application to Energy Hedging

Contact Info

Product

Resources

About