Relevant bibliographies by topics / Optimal hedge ratio

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Optimal hedge ratio'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Optimal hedge ratio"

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Myers, Robert J., and Stanley R. Thompson. "Generalized Optimal Hedge Ratio Estimation." American Journal of Agricultural Economics 71, no. 4 (November 1989): 858–68. http://dx.doi.org/10.2307/1242663.

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Lien, Donald, Keshab Shrestha, and Jing Wu. "Quantile Estimation of Optimal Hedge Ratio." Journal of Futures Markets 36, no. 2 (March 5, 2015): 194–214. http://dx.doi.org/10.1002/fut.21712.

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Lee, Cheng-Few, Kehluh Wang, and Yan Long Chen. "Hedging and Optimal Hedge Ratios for International Index Futures Markets." Review of Pacific Basin Financial Markets and Policies 12, no. 04 (December 2009): 593–610. http://dx.doi.org/10.1142/s0219091509001769.

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This empirical study utilizes four static hedging models (OLS Minimum Variance Hedge Ratio, Mean-Variance Hedge Ratio, Sharpe Hedge Ratio, and MEG Hedge Ratio) and one dynamic hedging model (bivariate GARCH Minimum Variance Hedge Ratio) to find the optimal hedge ratios for Taiwan Stock Index Futures, S&P 500 Stock Index Futures, Nikkei 225 Stock Index Futures, Hang Seng Index Futures, Singapore Straits Times Index Futures, and Korean KOSPI 200 Index Futures. The effectiveness of these ratios is also evaluated. The results indicate that the methods of conducting optimal hedging in different markets are not identical. However, the empirical results confirm that stock index futures are effective direct hedging instruments, regardless of hedging schemes or hedging horizons.
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Miller, Daren E. "Robust Estimation of the Optimal Hedge Ratio." CFA Digest 34, no. 1 (February 2004): 36–37. http://dx.doi.org/10.2469/dig.v34.n1.1417.

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Hatemi-J, Abdulnasser, and Youssef El-Khatib. "Stochastic optimal hedge ratio: theory and evidence." Applied Economics Letters 19, no. 8 (September 9, 2011): 699–703. http://dx.doi.org/10.1080/13504851.2011.572841.

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Harris, Richard D. F., and Jian Shen. "Robust estimation of the optimal hedge ratio." Journal of Futures Markets 23, no. 8 (June 26, 2003): 799–816. http://dx.doi.org/10.1002/fut.10085.

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Liu, Wei-Han. "Optimal hedge ratio estimation and hedge effectiveness with multivariate skew distributions." Applied Economics 46, no. 12 (February 11, 2014): 1420–35. http://dx.doi.org/10.1080/00036846.2013.875112.

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Li, Qing, Yanli Zhou, Xinquan Zhao, and Xiangyu Ge. "Dynamic Hedging Based on Fractional Order Stochastic Model with Memory Effect." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/6817483.

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Many researchers have established various hedge models to get the optimal hedge ratio. However, most of the hedge models only discuss the discrete-time processes. In this paper, we construct the minimum variance model for the estimation of the optimal hedge ratio based on the stochastic differential equation. At the same time, also by considering memory effects, we establish the continuous-time hedge model with memory based on the fractional order stochastic differential equation driven by a fractional Brownian motion to estimate the optimal dynamic hedge ratio. In addition, we carry on the empirical analysis to examine the effectiveness of our proposed hedge models from both in-sample test and out-of-sample test.
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Singh, Gurmeet. "Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures." Jindal Journal of Business Research 6, no. 2 (September 4, 2017): 108–31. http://dx.doi.org/10.1177/2278682117715358.

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This study attempts to study and suggest an optimal hedge ratio to Indian investors and traders by examining the three main indices of National Stock Exchange of India (NSE), namely, NIFTY, Bank NIFTY, and IT NIFTY, over the sample period from January 2011 to December 2015. The present study estimated the hedge ratio through six econometric models, namely, OLS, GARCH, EGARCH, TARCH, VAR, and VECM, in the minimum variance hedge ratio framework as suggested by Ederington (1979). The findings of the present study confirm the theoretical properties of Indian cash and futures market and suggest that the optimal hedge ratio estimated through EGARCH model was lowest for the NIFTY and Bank NIFTY, and that for IT NIFTY, the OLS model shows the lowest optimal hedge ratio as compared to that estimated through other models.
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Bohdalová, Mária, and Michal Greguš. "ESTIMATING THE HEDGE RATIOS." CBU International Conference Proceedings 4 (September 17, 2016): 229–34. http://dx.doi.org/10.12955/cbup.v4.874.

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This paper examines the problem of hedging portfolio returns. Many practitioners and academicians endeavor to solve the problem of how to calculate the optimal hedge ratio accurately. In this paper we compare estimates of the hedge ratio from a classical approach of a linear quantile regression, based on selected quantiles as medians, with that of a non-linear quantile regression. To estimate the hedge ratios, we have used a calibrated Student t distribution for the marginal densities and a Student t copula of the portfolio returns using a maximum likelihood estimation. We created two portfolios of the assets, one for equal weight and another for optimal weight in respect of minimal risk. Our findings show that an assumption of Student t marginal leads to a better estimation of the hedge ratio.
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Dissertations / Theses on the topic "Optimal hedge ratio"

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Máková, Barbora. "Hedge Ratio Estimation in Inventory Management." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-198395.

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Companies dependent on commodities for their production have to deal with volatile commodity prices and should employ measures for risk reduction as unfavourable spot price development may cause significant losses. A useful tool for diminishing the risk is hedging on futures market; however, this approach faces a crucial question of optimal hedge ratio determination (ratio between spot and futures units). Our thesis examines nine different ways of optimal hedge ratio estimation (naive, Sharpe, mean extended Gini coefficient, generalized semivariance, value at risk, and minimum variance through OLS, error correction, GARCH, and bivariate GARCH models) and evaluates their efficiency using the data on eight different commodities. The results differ across the respective commodities and cannot be generalized. Two conclusions resulting from the analysis refer to performance of naive and OLS hedge ratios and constant vs time varying hedge ratios. We find that complex hedge ratios, such as bivariate GARCH or VaR hedge ratios, do not outperform naive and OLS hedge ratios and that the results of constant hedge ratios are mostly as good as results of time-varying hedge ratios.
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Turner, Peter Alistair. "Determining the Optimal Commodity and Hedge Ratio for Cross-Hedging Jet Fuel." Thesis, North Dakota State University, 2014. https://hdl.handle.net/10365/27250.

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Airlines are exposed to risks in swings in the price of jet fuel. While there are many different options that they can use to hedge this risk, airlines often underutilize them. This study establishes the minimum variance hedge ratio for an airline wishing to hedge with futures, while also establishing the best cross-hedging asset. Airlines hedging with futures would create the most effective hedge by using 3-month maturity contracts of heating oil. 3- Month maturity contracts are slightly more effective as hedging tools than the next month, but beyond the 3-Month veil, increased maturity makes heating oil less effective as a cross hedging tool.
Upper Great Plains Transportation Institute (UGPTI)
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Haglund, Fredrik, and Svensson Johan. "The volatility race in Commodities : The optimal hedge ratio in Copper, Gold, Oil and Cotton." Thesis, Jönköping University, JIBS, Business Administration, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-88.

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Introduction: Companies that are dependent on different commodities as input or output are exposed to price risk in these commodities. The price changes can be expressed as volatility and higher volatility results in higher risk. Hedging the commodity contracts with futures can offset this risk. One of the most important questions in this field is to what extent the risk exposure should be hedged with futures contract, i.e. the optimal hedge ratio.

Purpose: The study aims to conduct an analysis of the variance in different commodities contracts and provide evidence of the optimal hedge ratio in the respective commodities.

Method: We used a quantitative study with daily spot and futures price changes of Copper, Gold, Cotton and Oil. We investigated the 6-month hedging behaviour where timeseries were created for the period January-June each year during 2001-2004. We used a simple linear regression of the futures and spot price changes and a minimum variance model in order to calculate the optimal hedge ratio.

Conclusion: Companies that are dependent on Copper, Gold, Cotton and Oil can significantly reduce the risk by engaging in futures contracts. The optimal hedge ratio for Copper is (96%), Gold (52%), Cotton (96%) and Oil (88%). By applying the optimal hedge ratio, a company may reduce their risk exposure up to 90% compared to an unhedged position.

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Xu, Weijun Banking &amp Finance Australian School of Business UNSW. "Optimal hedging strategy in stock index future markets." Awarded by:University of New South Wales. Banking & Finance, 2009. http://handle.unsw.edu.au/1959.4/43728.

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In this thesis we search for optimal hedging strategy in stock index futures markets by providing a comprehensive comparison of variety types of models in the related literature. We concentrate on the strategy that minimizes portfolio risk, i.e., minimum variance hedge ratio (MVHR) estimated from a range of time series models with different assumptions of market volatility. There are linear regression models assuming time-invariant volatility; GARCH-type models capturing time-varying volatility, Markov regime switching (MRS) regression models assuming state-varying volatility, and MRS-GARCH models capturing both time-varying and state-varying volatility. We use both Maximum Likelihood Estimation (MLE) and Bayesian Gibbs-Sampling approach to estimate the models with four commonly used index futures contracts: S&P 500, FTSE 100, Nikkei 225 and Hang Seng index futures. We apply risk reduction and utility maximization criterions to evaluate hedging performance of MVHRs estimated from these models. The in-sample results show that the optimal hedging strategy for the S&P 500 and the Hang Seng index futures contracts is the MVHR estimated using the MRS-OLS model, while the optimal hedging strategy for the Nikkei 225 and the FTSE 100 futures contracts is the MVHR estimated using the Asymmetric-Diagonal-BEKK-GARCH and the Asymmetric-DCC-GARCH model, respectively. As in the out-of sample investigation, the time-varying models such as the BEKK-GARCH models especially the Scalar-BEKK model outperform those state-varying MRS models in majority of futures contracts in both one-step- and multiple-step-ahead forecast cases. Overall the evidence suggests that there is no single model that can consistently produce the best strategy across different index futures contracts. Moreover, using more sophisticated models such as MRS-GARCH models provide some benefits compared with their corresponding single-state GARCH models in the in-sample case but not in the out-of-sample case. While comparing with other types of models MRS-GARCH models do not necessarily improve hedging efficiency. Furthermore, there is evidence that using Bayesian Gibbs-sampling approach to estimate the MRS models provides investors more efficient hedging strategy compared with the MLE method.
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Neto, Carlos Santos Amorim. "Efetividade do hedge para o boi gordo com contratos da BM&FBOVESPA: análise para os estados de São Paulo e Goiás." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11132/tde-12032015-152555/.

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O objetivo geral deste trabalho foi avaliar a eficiência do mercado futuro como forma de mitigação do risco associado aos preços do boi gordo para as praças de Araçatuba (SP) e Goiânia (GO). Calculou-se a efetividade do hedge por meio da razão ótima de hedge para as praças estudadas no período de 2002 a 2013, utilizando três tipos de modelos econométricos. No primeiro modelo, as variâncias e covariâncias condicionais foram tratadas como constantes e os preços spot e futuro não foram considerados correlacionados no tempo; no segundo modelo, relaxou-se a hipótese de que os preços spot e futuro não são correlacionados no tempo, portanto, adicionou-se um vetor de correção de erros ao modelo; e, no terceiro modelo, assumiu-se que as variâncias e covariâncias condicionais não são constantes. Os resultados obtidos por esses métodos indicaram que o uso do contrato futuro de boi gordo diminuiu a variância dos retornos no período estudado, de modo que as estimativas dinâmicas foram inferiores na efetividade em diminuir o risco de preço diante das estimativas obtidas por modelos estáticos. Ainda com o intuito de avaliar a eficiência do mercado futuro de boi gordo, foram quantificados a variância e os retornos do confinador nas praças estudadas através de simulações de compra de boi magro e posterior venda de boi gordo, realizando, simultaneamente, o hedge no mercado futuro. Observou-se que a utilização do contrato futuro diminuiu o coeficiente de variação para os períodos analisados em comparação às estratégias que não realizaram a utilização do hedge.
The general objective of this research was to evaluate the efficiency of futures market in order to mitigate the risk of price of live cattle to the producers of Araçatuba (SP) and Goiânia (GO). To measure this effectiveness, we estimated the optimal hedge ratio from the period of 2002 to 2013, using three types of econometric models. In the first model, conditional variances and covariances were treated as constant and the spot and future prices were not considered correlated in time; in the second model, we relaxed the hypothesis that spot and future prices were not correlated in time, so, we added an error correction vector to the model; and, in the third model, we assumed that the conditional variances and covariances are not constant. The results obtained by these methods indicated that the use of live cattle contract was able to reduce the risk and also that the dynamic estimates do not overcome the static estimates. We also calculated the variance of returns for the producers of Araçatuba e Goiânia by purchasing simulations of steers and subsequent sale of live cattle, performing simultaneously the hedge on the market future. It was observed that the use of the futures contract decreased the coefficient of variation for the periods analyzed compared to the strategies that did not undergo the use of hedging.
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Nogueira, Cinthya Muyrielle da Silva. "Efici?ncia e raz?o de hedge: uma an?lise dos mercados futuro brasileiros de boi, caf?, etanol, milho e soja." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/12233.

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This research aims to investigate the Hedge Efficiency and Optimal Hedge Ratio for the future market of cattle, coffee, ethanol, corn and soybean. This paper uses the Optimal Hedge Ratio and Hedge Effectiveness through multivariate GARCH models with error correction, attempting to the possible phenomenon of Optimal Hedge Ratio differential during the crop and intercrop period. The Optimal Hedge Ratio must be bigger in the intercrop period due to the uncertainty related to a possible supply shock (LAZZARINI, 2010). Among the future contracts studied in this research, the coffee, ethanol and soybean contracts were not object of this phenomenon investigation, yet. Furthermore, the corn and ethanol contracts were not object of researches which deal with Dynamic Hedging Strategy. This paper distinguishes itself for including the GARCH model with error correction, which it was never considered when the possible Optimal Hedge Ratio differential during the crop and intercrop period were investigated. The commodities quotation were used as future price in the market future of BM&FBOVESPA and as spot market, the CEPEA index, in the period from May 2010 to June 2013 to cattle, coffee, ethanol and corn, and to August 2012 to soybean, with daily frequency. Similar results were achieved for all the commodities. There is a long term relationship among the spot market and future market, bicausality and the spot market and future market of cattle, coffee, ethanol and corn, and unicausality of the future price of soybean on spot price. The Optimal Hedge Ratio was estimated from three different strategies: linear regression by MQO, BEKK-GARCH diagonal model, and BEKK-GARCH diagonal with intercrop dummy. The MQO regression model, pointed out the Hedge inefficiency, taking into consideration that the Optimal Hedge presented was too low. The second model represents the strategy of dynamic hedge, which collected time variations in the Optimal Hedge. The last Hedge strategy did not detect Optimal Hedge Ratio differential between the crop and intercrop period, therefore, unlikely what they expected, the investor do not need increase his/her investment in the future market during the intercrop
Esta pesquisa objetivou investigar a efici?ncia e raz?o ?tima de hedge para os mercados futuro de boi, caf?, etanol, milho e soja. Este trabalho tratou a raz?o ?tima e efetividade de hedge atrav?s de modelos GARCH multivariados com termo de corre??o de erro, atentando para o poss?vel fen?meno de diferenciais de raz?o ?tima de hedge nos per?odos de safra e entressafra. A raz?o ?tima de hedge deve ser maior na entressafra devido ? maior incerteza com rela??o a um poss?vel choque de oferta (LAZZARINI, 2010). Dentre os contratos futuros tratados nesta pesquisa, os contratos de caf?, etanol e soja ainda n?o foram objeto de investiga??o desse fen?meno. Al?m disso, os contratos futuros de milho e etanol ainda n?o foram objeto de pesquisas que tratam de estrat?gias de hedge din?mico. Este trabalho se diferencia ainda por incluir o mecanismo de corre??o de erro na modelagem GARCH, o que nunca foi considerado ao se investigar poss?veis diferenciais de raz?o ?tima de hedge nos per?odos de safra e entressafra. Foram utilizadas como pre?o futuro das commodities as cota??es das mesmas no mercado futuro da BM&FBOVESPA e como pre?o ? vista o ?ndice CEPEA, no per?odo de maio de 2010 a junho de 2013 para boi, caf?, etanol e milho e at? agosto de 2012 para a soja, com frequ?ncia di?ria. Foram obtidos resultados semelhantes para todas as commodities. H? rela??o de longo prazo entre os mercados ? vista e futuro, bicausalidade entre os pre?os ? vista e futuro do boi, caf?, etanol e milho, e unicausalidade do pre?o futuro da soja sobre o pre?o ? vista. A raz?o ?tima de hedge foi estimada a partir de tr?s diferentes estrat?gias: regress?o linear por MQO, modelo BEKK-GARCH diagonal e modelo BEKK-GARCH diagonal com dummy de entresssafra. O modelo de regress?o por MQO apontou para a inefici?ncia de hedge, tendo em vista que as raz?es ?timas apresentadas foram muito baixas. O segundo modelo, que representa a estrat?gia de hedge din?mico, captou varia??es temporais na raz?o ?tima. A ?ltima estrat?gia de hedge n?o detectou diferencial de raz?es ?timas de hedge entre os per?odos de safra e entressafra, logo, ao contr?rio do que se esperava, o investidor n?o precisa aumentar seu investimento no mercado futuro durante a entressafra
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Júnior, José César Cruz. "Modelo de razão de hedge ótima e percepção subjetiva de risco nos mercados futuros." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11132/tde-05082009-075152/.

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O objetivo deste trabalho foi investigar motivos pelos quais os produtores brasileiros de boi gordo e milho fazem relativamente pouco uso dos mercados futuros como ferramenta de gerenciamento de risco de preços. Duas abordagens diferentes foram apresentadas na pesquisa. Para o mercado de boi gordo, onde a presença de hedgers parece ser maior, um modelo de razão de hedge ótima alternativo ao tradicional modelo de mínima variância foi utilizado. O modelo alternativo faz uso de uma função de utilidade com aversão relativa ao risco constante para modelar as preferências dos indivíduos. Esta abordagem é considerada mais realista, por permitir que o nível absoluto de aversão ao risco se altere com a riqueza. Além disso, uma medida de downside risk e o relaxamento das hipóteses do modelo tradicional de mínima variância foram adicionados na análise. De acordo com os resultados, quando consideradas a possibilidade de se realizar investimento em um ativo alternativo ao mercado agropecuário, e a presença de custos de transação, o incentivo ao hedge se reduz acentuadamente. A utilização de uma medida alternativa de risco colaborou para esta redução, que foi mais acentuada para indivíduos menos aversos ao risco. Isto pode ser concluído observando-se que as razões de hedge ótimas, obtidas através da maximização da utilidade esperada dos indivíduos, foram, em grande parte, inferiores àquelas obtidas pelo modelo tradicional. Além disso, na maior parte dos casos, a utilização das razões de hedge ótimas alternativas mostrou-se mais eficiente que a obtida pelo modelo tradicional, pois possibilitou a obtenção de maiores razões retorno/risco no período selecionado para teste. Para o mercado de milho, um questionário foi aplicado a 90 produtores no sul e centro-oeste do Brasil. O questionário teve o objetivo de verificar se existem sinais de excesso de confiança nos preços por parte dos produtores de milho entrevistados. Adicionalmente, perguntas sobre o conhecimento do mercado futuro na BM&FBOVESPA foram também apresentadas. Em relação a este último tema, a maior parte dos produtores respondeu que conhece sobre o mercado futuro na bolsa brasileira, mas não fazem uso do mesmo. O principal motivo apontado pelos produtores foi não possuir informação suficiente sobre os mercados futuros. Associado a este resultado, descobriu-se que existe pouco incentivo para que os produtores realizem proteção de preços da produção, pois, para a maior parte dos entrevistados, as variâncias subjetivas de preços foram significativamente inferiores às variâncias dos preços históricos no mercado físico e futuro. Este resultado permitiu concluir que o excesso de confiança nos preços pode ser considerado uma explicação alternativa para o baixo uso dos mercados futuros como ferramenta de gestão de risco de preços. Como conclusões gerais, ações que visem promover reduções de custos de transação no mercado futuro e uma maior divulgação dos benefícios desta importante ferramenta na redução de risco de preços devem ser mais exploradas pela BM&FBOVESPA. Além disso, a promoção do maior conhecimento a respeito de como se negociar nesse mercado pode ser também uma boa estratégia para se fazer com que um maior número de produtores passe a negociar nesse mercado.
This research aimed to investigate the significant underuse of futures markets as a risk management tool by Brazilian live cattle and corn producers. To this end, the paper used two different approaches. In the live cattle market, where there appears a higher participation of hedgers trading, an alternative hedge ratio model was used instead of the standard minimum variance model. The alternative model uses a constant relative risk aversion utility function to model individual preferences. This approach is considered more realistic as use of the constant relative risk aversion utility function allows for the absolute level of risk aversion to change with wealth. In addition, a downside risk measure was introduced and certain restrictive assumptions to the minimum variance model were relaxed. According to the results, when the possibility of investment in an alternative asset and transaction costs are considered, the incentive to hedge is dramatically reduced. The use of an alternative risk measure also proved important to this reduction, which was higher for less risk averse individuals. This conclusion may be drawn after observing that the optimal hedge ratios obtained from the expected utility maximization are, in most cases, lower than those obtained by the standard model. Moreover, in most cases the use of alternative optimal hedge ratios provides higher return/risk ratios during the test period. For the corn market, a survey questionnaire was conducted of ninety producers in South and Central- West Brazil. The survey was conducted in order to verify the presence of overconfidence in prices among corn producers. The survey also asked questions regarding their knowledge of futures markets at BM&FBOVESPA. Most respondents answered that while they know about futures markets at the Brazilian board of trade, they do not trade on it because they do not have enough information about trading. The results also revealed that there is a low incentive for producers to hedge their production in futures markets because for most producers, subjective price variances are significantly lower than the variance of historical futures and spot prices. Given the results, one may conclude that the overconfidence effect in prices can be considered an alternative explanation to the low use of futures markets as a price risk management tool. Furthermore, actions which promote transaction costs reductions and promote the benefits to producers of using this important risk management tool while trading in the futures markets must be more carefully explored by the BM&FBOVESPA. Moreover, promoting knowledge of trading in futures markets may likely be a successful strategy for the wider adoption of futures trading among corn and live cattle producers.
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Engström, Daniel, and Niklas Gustafsson. "Swedish Equity Sectors Risk Management with Commodities : Revisiting dynamic conditional correlations and hedge ratios." Thesis, Linköpings universitet, Nationalekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139040.

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The purpose of this study is to investigate changes in dynamic conditional correlations between Swedish equity sector indices and commodities using oil, gold, copper and a general commodity index. Additionally the purpose is to evaluate which of the two methods, DCC- GARCH or GO-GARCH that is more efficient in estimating correlation for hedge ratio calculation. Daily data on the FTSE30 index of Sweden and its sector indices have been studied between the years 1994 and 2017. A DCC-GARCH (1,1) and GO-GARCH (1,1) model with one autoregressive term AR(1) using multivariate Student t- and Multivariate Affine Negative Inverse Gaussian distribution were used to estimate conditional correlations. Correlations between Swedish FTSE30, its sector indices and commodities are considerably lower than previous research has found American or emerging markets correlation with commodities to be. This suggests better diversification opportunities with commodities for the Swedish market. Optimal hedge ratios (OHR) was calculated and back tested using a rolling window analysis with 1000 days forecast length and 20 days re-estimation window and evaluated using a calculated hedge effectiveness index (HE). Determined by HE, copper is the best hedge for the Swedish composite FTSE30 and sector indices using conditional correlation from the GO-GARCH during the data period. Gold is considered as a semi-strong safe haven due to its negative correlation with all sectors. Additionally, this study identifies a temporarily large increase in the correlation between the Swedish equities sectors and composite index with commodities around the years 2015/2016. This study also emphasizes the difference between stressful and calm periods in the market.
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Leite, Gustavo Ribas de Almeida. "Hedge de crédito através de equity: uma análise empírica com uso de ativos corporativos brasileiros." reponame:Repositório Institucional do FGV, 2011. http://hdl.handle.net/10438/9777.

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This paper aims to analyze the results of an operation to hedge a diversified credit portfolio through the use of equity. Initially, a reference to the main theoretical aspects of this dissertation with their definitions and literature review will be made. Furthermore, there will be an explanation about the basic parameters of the selection of the sample used and the period during which such protection strategy will be implemented.
Este trabalho tem como objetivo analisar os resultados de uma operação de hedge de um diversificado portfólio de crédito de empresas brasileiras através do uso de ativos de equity. Inicialmente, faz-se uma alusão aos principais aspectos teóricos da presente dissertação com suas definições e revisão bibliográfica. Posteriormente, são apresentados os parâmetros básicos da seleção da amostra utilizada e do período durante o qual tal estratégia de proteção será implementada.
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Li, Chiu-chan, and 李秋貞. "Leptokurtic Distribution and Optimal Hedge Ratio." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/84503226020231308652.

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碩士
國立高雄第一科技大學
金融營運所
92
When we use futures to hedge a portfolio of risky assets, the most important objective is to estimate the optimal hedge ratio (OHR). When the futures price follows a martingale and investors have mean-variance utility, the OHR is equal to the minimum variance hedge ratio. Owing to time-varying volatility in financial asset returns, moving average, GARCH, or EWMA models are commonly employed to estimate OHR. All of the approaches to estimating the OHR described above are based on the sample variance and covariance estimators of returns. These are consistent estimators of the population variance and covariance, irrespective of the underlying distribution of data, but they are not in general efficient. In particular, when the distribution of the data is leptokurtic, these estimators will attach too much weight to extreme observations. This paper uses the Power EWMA estimator of Guermat and Harris (2002) to estimate OHR. The Power EWMA estimator (that is, the robust estimator) can capture the leptokurtic distribution of the data. We also compare the results of the robust estimator to those based on the standard estimators. Our empirical analysis is restricted to the SGX-DT and the TAIFEX Taiwan stock index futures. The empirical results show that use of the robust estimator generates reductions in the variance of the hedged portfolio and the volatility of the OHR for the SGX-DT futures market, and for subperiod 1 of high kurtosis. It also reduce the transaction costs of rebalancing that are associated with changes in the OHR.
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Books on the topic "Optimal hedge ratio"

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Delaney, Brian. Dynamic hedging and time-varying optimal hedge ratio estimation with foreign currency futures. Dublin: University College Dublin, 1995.

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Book chapters on the topic "Optimal hedge ratio"

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El-Khatib, Youssef, and Abdulnasser Hatemi-J. "Asymmetric Optimal Hedge Ratio with an Application." In Lecture Notes in Electrical Engineering, 231–37. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2317-1_19.

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Dunis, Christian L., and Pierre Lequeux. "High Frequency Data and Optimal Hedge Ratios." In Advances in Quantitative Asset Management, 113–36. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4389-3_6.

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Lee, Cheng-Few, Jang-Yi Lee, Kehluh Wang, and Yuan-Chung Sheu. "A Generalized Model for Optimum Futures Hedge Ratio." In Handbook of Quantitative Finance and Risk Management, 873–82. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-77117-5_57.

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Lee, Cheng-Few, Jang-Yi Lee, Kehluh Wang, and Yuan-Chung Sheu. "A Generalized Model for Optimum Futures Hedge Ratio." In Handbook of Financial Econometrics and Statistics, 2561–76. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7750-1_94.

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"The Research of Futures Optimal Hedge Ratio with Different Objective Function." In International Conference on Information Technology and Management Engineering (ITME 2011), 87–90. ASME Press, 2011. http://dx.doi.org/10.1115/1.859827.paper21.

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Mishin, Andrey, and Polina Kisarina. "Calendar Spread Hedging Mechanism for Mining Companies." In Advances in Business Strategy and Competitive Advantage, 1–12. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-0361-4.ch001.

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This chapter attempts to answer the question of how much of its current production a mining company should hedge by forward selling by using a model that allows a company to determine the optimal (profit-maximising) hedge. The risk estimation model of company failure is based on the forward price of metals; the miner's operating costs is based on quantitative approach for mining companies. The chapter considers the transition to advanced digital, intelligent manufacturing technologies, robotic systems, the creation of systems for processing large amounts of data, machine learning, and artificial intelligence. The model calculates the present value of future income distributed as dividends in order to determine the value of the company from the perspective of the owner or investor, a multiple of the current value of the product. By simulating the work of several companies working with different levels of forward, it is possible to determine the relative profitability and survival in the market that allows one to determine the optimal hedging ratio.
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Conference papers on the topic "Optimal hedge ratio"

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Doemoetoer, Barbara. "Modelling Optimal Hedge Ratio In The Presence Of Funding Risk." In 27th Conference on Modelling and Simulation. ECMS, 2013. http://dx.doi.org/10.7148/2013-0282.

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Hsu, Yu-Chia, and An-Pin Chen. "Clustering Time Series Data by SOM for the Optimal Hedge Ratio Estimation." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.408.

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Yan-jun, Yang. "Optimal Hedge Ratio and the Performance of Hedging in China's Cotton Futures Market." In 2007 International Conference on Management Science and Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icmse.2007.4422093.

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Aditya Jahja, Jason, and Ika Yanuarti Loebiantoro. "Analysis of optimal hedge ratio and hedging effectiveness in Taiwan stock exchange capitalization weighted stock index (TAIEX) futures." In 15th International Symposium on Management (INSYMA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/insyma-18.2018.7.

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Herdin, Gu¨nther, Johann Klausner, Martin Weinrotter, Josef Graf, and Andreas Wimmer. "GE Jenbacher’s Update on Laser Ignited Engines." In ASME 2006 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/icef2006-1547.

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The focus in research year 05 was on the optimization of optical coupling and minimization of laser energy especially in connection with very lean combustion and with high exhaust gas recirculation rates for low NOx emissions. The direct comparison of laser ignition with conventional spark ignitions, without any measures implemented in favor of laser ignition (high compression ratio, high turbulence ratio), consistently shows advantages in the case of laser ignition. With extension of the Lambda window, in the case of a spark ignition engine with a 2.4 1 piston displacement it is possible to shift the engine 0.3 units in the direction of “lean combustion” (possible reduction of NOx level less than 30% of the state of the art); EGR compatibility is increased by about 15% to a recirculation rate of about 40%. With regard to EGR compatibility, in coordination with SWRI (HEDGE Program) similar tests on determination of potential were carried out as well. In this case too no essential measures were implemented in favor of the exploitation of the potential of laser ignition; however, a minor increase of the compression ratio already allows recognition of the theoretically possible and expected potentials. Regarding stoichiometric conditions, from the viewpoint of the researchers working jointly on the project it is possible to reduce the energy to less than 1 mJ. Conversely, in the event of the utilization of lean-burn combustion, appreciably more energy must be provided. Additionally, measures regarding combustion control in the area of the extended lean-burn limit must also be carried out. Only then is it possible to ensure optimal values for burning durations and the variation coefficient. Initial results in this regard will also be presented.
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Li Shu-sheng and Liang Zhao-hui. "Measuring difference of optimal hedge ratios between long position and short position using lower partial moments." In 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. IEEE, 2008. http://dx.doi.org/10.1109/drpt.2008.4523423.

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