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Journal articles on the topic 'Optimal hedge ratio'

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Published: 4 June 2021

Last updated: 18 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ai, Chunrong, Arjun Chatrath, and Frank Song. "A semiparametric estimation of the optimal hedge ratio." Quarterly Review of Economics and Finance 47, no. 2 (May 2007): 366–81. http://dx.doi.org/10.1016/j.qref.2005.07.003.

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12

Kostika, Eleftheria, and Raphael N. Markellos. "Optimal Hedge Ratio Estimation and Effectiveness Using ARCD." Journal of Forecasting 32, no. 1 (January 23, 2012): 41–50. http://dx.doi.org/10.1002/for.1249.

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13

Nishi, Hirofumi. "Cointegration, Price-Adjustment Delays, and Optimal Hedge Ratio in the Precious Metal Markets." Applied Finance Letters 8 (February 28, 2019): 14–23. http://dx.doi.org/10.24135/afl.v8i0.125.

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Firms seeking to apply hedge accounting treatment under the Accounting Standards Codification Topic 815 must demonstrate higher hedge effectiveness, for which the regression analysis is commonly used as a testing method. An autoregressive distributed lag (ARDL) model is adopted in this article to examine the hedge effectiveness in the presence of nonsynchronous trading of spot and futures contracts as well as a long-run cointegrating relationship between their prices. Using precious metal market data, our study empirically demonstrates that a hedge ratio estimated with a conventional OLS model tends to be downwardly biased. Our finding also indicates that the omitted-variable bias becomes apparent only when the difference between the transaction frequencies in spot and futures markets is significantly large.

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14

Kim, In Joon, and Dong Haeng Lee. "Hedging Price Risk in the Presence of Quantity Risk." Journal of Derivatives and Quantitative Studies 23, no. 1 (February 28, 2015): 1–27. http://dx.doi.org/10.1108/jdqs-01-2015-b0001.

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This research looks into hedge strategies to resolve foreign exchange-related risks, generated when investing in overseas financial assets, as an example of quantity risk. If an investor has information with no uncertainty over the volume and there is only a price risk he want to hedge, an investor will be able to reduce or eliminate risks by using relative derivative securities such as forwards or futures contracts. However, if there are uncertainties over the volume of hedging targets that is called quantity risk, it is impossible to set the optimal hedge ratio with the traditional method without considering the presence of quantity risk. In this paper, we theoretically draw an optimal hedge ratio which is estimated via minimal variance criterion under static hedge structure. We also analyze its hedge performance and the impact of change in covariance on the optimal hedge ratio and variance of investment return denominated as its own country currency. For theoretical approach, we review the impact that overseas financial assets’ yield and exchanges rates distribution will have on optimal hedge ratio through simple numerical analysis. Empirical analysis is carried out by using the stock indices of the U.S., Europe and Asian countries, and the results indicate that hedge strategies taken with quantity risk for all markets produced better hedging performance than the strategies taken without quantity risk. Since there is a need for systematic research on risks involving foreign exchanges that occur in the event of foreign investments aimed to develop the domestic financial industry, we hope that our research serve as a stepping-stone for further research.

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15

Lien, Donald. "Cointegration and the optimal hedge ratio: the general case." Quarterly Review of Economics and Finance 44, no. 5 (December 2004): 654–58. http://dx.doi.org/10.1016/j.qref.2003.08.004.

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16

Mehrara, Mohsen, and Monire Hamldar. "Optimal Hedge Ratio for Brent Oil Market; Baysian Approach." International Letters of Social and Humanistic Sciences 37 (August 2014): 82–87. http://dx.doi.org/10.18052/www.scipress.com/ilshs.37.82.

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This paper examines the optimal hedging ratio (OHR) for the Brent Crude Oil Futures using daily data over the period 1990/17/8-2014/11/3. To gain OHR, it is employed a Vector Autoregressive (VAR) and Vector Error Correction (VEC) and Baysian Vector Autoregressive (BVAR) models. At last, the efficiency of these calculated OHR are compared through Edrington's index.

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17

Mehrara, Mohsen, and Monire Hamldar. "Time-Varying Optimal Hedge Ratio for Brent Oil Market." International Letters of Social and Humanistic Sciences 56 (July 2015): 103–6. http://dx.doi.org/10.18052/www.scipress.com/ilshs.56.103.

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This paper examines the optimal hedging ratio (OHR) for the Brent Crude Oil Futures using daily data over the period 1990/17/8-2014/11/3. To estimate OHR, we employ multivariate BEKK MV-GARCH model. At last, the efficiency of this approach are compared with the constant OHR captured from OLS through Edrington's index.

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18

Lien, Donald, and Keshab Shrestha. "Estimating the optimal hedge ratio with focus information criterion." Journal of Futures Markets 25, no. 10 (2005): 1011–24. http://dx.doi.org/10.1002/fut.20166.

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19

Howard, Charles T., and Louis J. D'Antonio. "The cost of hedging and the optimal hedge ratio." Journal of Futures Markets 14, no. 2 (April 1994): 237–58. http://dx.doi.org/10.1002/fut.3990140208.

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20

McNew, Kevin P., and Paul L. Fackler. "Nonconstant optimal hedge ratio estimation and nested hypotheses tests." Journal of Futures Markets 14, no. 5 (August 1994): 619–35. http://dx.doi.org/10.1002/fut.3990140508.

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21

Kim, Myeong Jun, and Sung Y. Park. "Optimal conditional hedge ratio: A simple shrinkage estimation approach." Journal of Empirical Finance 38 (September 2016): 139–56. http://dx.doi.org/10.1016/j.jempfin.2016.06.002.

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22

ZOU, BIN. "OPTIMAL INVESTMENT IN HEDGE FUNDS UNDER LOSS AVERSION." International Journal of Theoretical and Applied Finance 20, no. 03 (April 24, 2017): 1750014. http://dx.doi.org/10.1142/s0219024917500145.

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We study optimal investment problems in hedge funds for a loss averse manager under the framework of cumulative prospect theory. We obtain explicit solutions for a general utility function satisfying the Inada conditions and a piece-wise exponential utility function. Through a sensitivity analysis, we find that the manager reduces the risk of the hedge fund when her/his loss aversion, risk aversion, ownership in the fund, or management fee ratio increases. However, the increase of incentive fee ratio drives the manager to seek more risk in order to achieve higher prospect utility.

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23

LIEN, DONALD, and KESHAB SHRESTHA. "THE EFFECTS OF PRICE DYNAMICS ON OPTIMAL FUTURES HEDGING." Annals of Financial Economics 07, no. 02 (December 2012): 1250008. http://dx.doi.org/10.1142/s201049521250008x.

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In this paper, we analytically derive the adjustments needed for the conventional hedge ratio due to the presence of short-run and long-run dynamics. We also analytically show the performance impact of these dynamics. We apply the method discussed in the paper to eight different stock index futures contracts from seven different countries. It is found that the short-run dynamics has no effect whereas the long-run dynamics may produce significant effects on the optimal hedge ratio and the hedging performance.

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24

Halkos and Tsirivis. "Energy Commodities: A Review of Optimal Hedging Strategies." Energies 12, no. 20 (October 18, 2019): 3979. http://dx.doi.org/10.3390/en12203979.

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Energy is considered as a commodity nowadays and continuous access along with price stability is of vital importance for every economic agent worldwide. The aim of the current review paper is to present in detail the two dominant hedging strategies relative to energy portfolios, the Minimum-Variance hedge ratio and the expected utility maximization methodology. The Minimum-Variance hedge ratio approach is by far the most popular in literature as it is less time consuming and computationally demanding; nevertheless by applying the appropriate multivariate model Garch family volatility model, it can provide a very reliable estimation of the optimal hedge ratio. However, this becomes possible at the cost of a rather restrictive assumption for infinite hedger’s risk aversion. Within an uncertain worldwide economic climate and a highly volatile energy market, energy producers, retailers and consumers had to become more adaptive and develop the necessary energy risk management and optimal hedging strategies. The estimation gap of an optimal hedge ratio that would be subject to the investor’s risk preferences through time is filled by the relatively more complex and sophisticated expected utility maximization methodology. Nevertheless, if hedgers share infinite risk aversion or if alternatively the expected futures price is approximately zero the two methodologies become equivalent. The current review shows that when evidence from the energy market during periods of extremely volatile economic climate is considered, both hypotheses can be violated, hence it becomes reasonable that especially for extended hedging horizons it would be wise for potential hedgers to take into consideration both methodologies in order to build a successful and profitable hedging strategy.

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25

Dewally, Michaël, and Luke Marriott. "Effective Basemetal Hedging: The Optimal Hedge Ratio and Hedging Horizon." Journal of Risk and Financial Management 1, no. 1 (December 31, 2008): 41–76. http://dx.doi.org/10.3390/jrfm1010041.

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26

Carpantier, Jean-Francois, and Besik Samkharadze. "The Asymmetric Commodity Inventory Effect on the Optimal Hedge Ratio." Journal of Futures Markets 33, no. 9 (June 21, 2012): 868–88. http://dx.doi.org/10.1002/fut.21566.

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27

Lien, Donald, and Keshab Shrestha. "Estimating optimal hedge ratio: a multivariate skew-normal distribution approach." Applied Financial Economics 20, no. 8 (April 2010): 627–36. http://dx.doi.org/10.1080/09603100903459907.

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28

Tejeda, Hernan, and Dillon Feuz. "Determining the effectiveness of optimal time-varying hedge ratios for cattle feeders under multiproduct and single commodity settings." Agricultural Finance Review 74, no. 2 (July 1, 2014): 217–35. http://dx.doi.org/10.1108/afr-11-2013-0038.

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Purpose – The purpose of this paper is to determine and contrast the risk mitigating effectiveness from optimal multiproduct time-varying hedge ratios, applied to the margin of a cattle feedlot operation, over single commodity time-varying and naive hedge ratios. Design/methodology/approach – A parsimonious regime-switching dynamic correlations (RSDC) model is estimated in two-stages, where the dynamic correlations among prices of numerous commodities vary proportionally between two different regimes/levels. This property simplifies estimation methods for a large number of parameters involved. Findings – There is significant evidence that resulting simultaneous correlations among the prices (spot and futures) for each commodity attain different levels along the time-series. Second, for in and out-of-sample data there is a substantial reduction in the operation's margin variance provided from both multiproduct and single time-varying optimal hedge ratios over naive hedge ratios. Lastly, risk mitigation is attained at a lower cost given that average optimal multiproduct and single time-varying hedge ratios obtained for corn, feeder cattle and live cattle are significantly below the naive full hedge ratio. Research limitations/implications – The application studied is limited in that once a hedge position has been set at a particular period, it is not possible to modify or update at a subsequent period. Practical implications – Agricultural producers, specifically cattle feeders, may profit from a tool using improved techniques to determine hedge ratios by considering a larger amount of up-to-date information. Moreover, these agents may apply hedge ratios significantly lower than one and thus mitigate risk at lower costs. Originality/value – Feedlot operators will benefit from the potential implementation of this parsimonious RSDC model for their hedging operations, as it provides average optimal hedge ratios significantly lower than one and sizeable advantages in margin risk mitigation.

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29

Choudhry, Taufiq. "Short-run deviations and optimal hedge ratio: evidence from stock futures." Journal of Multinational Financial Management 13, no. 2 (April 2003): 171–92. http://dx.doi.org/10.1016/s1042-444x(02)00042-7.

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30

Dömötör, Barbara. "Optimal hedge ratio in a biased forward market under liquidity constraints." Finance Research Letters 21 (May 2017): 259–63. http://dx.doi.org/10.1016/j.frl.2016.11.009.

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31

Li, Jackie, Chong It Tan, Sixian Tang, and Jia Liu. "On the optimal hedge ratio in index-based longevity risk hedging." European Actuarial Journal 9, no. 2 (March 21, 2019): 445–61. http://dx.doi.org/10.1007/s13385-019-00199-w.

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32

Khurana, Rachna, and Umang Khetan. "VALUE-AT-RISK BASED APPROACH FOR CURRENCY HEDGING." Indian Journal of Finance and Banking 5, no. 1 (January 29, 2021): 23–37. http://dx.doi.org/10.46281/ijfb.v5i1.961.

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Corporate FX risk management has gained complexity with an increased number of currencies involved and varying correlations among them. Existing literature has highlighted the need to account for cross-currency correlations when optimizing hedge ratios for portfolio management (Dowd, 1999). In this paper, we propose a Value-at-Risk (VaR) based model to estimate the optimal hedge ratio for a multi-national corporate that aims to minimize the cost of hedging at a given tolerance level of expected loss arising out of FX movement. The paper illustrates both parametric and historical methods of VaR estimation at a portfolio level as the first step in risk management. As a second step, an efficient-frontier is derived based on the expected VaR level at various hedge ratios and compared with associated hedge cost. The benefits of this approach include: identification of net exposures after correlations among currencies are accounted for in order to avoid duplication of hedges, and condensation of the parameters governing hedging decision into a single, intuitively-appealing number. The paper also highlights the need to frequently update the model’s assumptions as currency correlations and corporate exposures remain dynamic. JEL Classification Codes: C10, F31, G32, M20.

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33

Shanthi, A., and R. Thamilselvan. "Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India." Restaurant Business 118, no. 3 (March 11, 2019): 137–52. http://dx.doi.org/10.26643/rb.v118i3.7637.

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The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

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34

Barbi, Massimiliano, and Silvia Romagnoli. "Optimal hedge ratio under a subjective re-weighting of the original measure." Applied Economics 48, no. 14 (October 6, 2015): 1271–80. http://dx.doi.org/10.1080/00036846.2015.1096008.

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35

Hsu, Yu-Chia, and An-Pin Chen. "A clustering time series model for the optimal hedge ratio decision making." Neurocomputing 138 (August 2014): 358–70. http://dx.doi.org/10.1016/j.neucom.2014.01.026.

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36

Zorzi, Robin, and Bettina Friedl. "The Optimal Hedge Ratio — An Analytical Decision Model Considering Periodical Accounting Constraints." Review of Pacific Basin Financial Markets and Policies 17, no. 04 (November 28, 2014): 1450024. http://dx.doi.org/10.1142/s0219091514500246.

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In practice, it is observable that firms tend to smooth periodical earnings because periodical earnings are considered by capital markets as a proxy for firms' success, and therefore, are often operationalized by the respective compensation plans for managers. Considering financial hedging strategies such as purchasing financial derivatives, income smoothing can lead to a restricted use of financial derivatives even if it decreases firm value because the periodical changes of the value of financial derivatives possibly cause undesired volatilities in periodical earnings. In this paper, a deductive multi-period analytical decision model based on the capital market theory is presented to explain the influence of income smoothing on firms' hedging strategy. Thereby, principal-agent relations between a firm's investors and managers (decision makers) are assumed. Moreover, the decision model is applied to a real data set by conducting a sensitivity analysis. To our knowledge, this is the first paper to operationalize accounting constraints to determine how much a firm should hedge its risk exposure.

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37

Pradhan, Kailash. "The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India." South East European Journal of Economics and Business 6, no. 1 (April 1, 2011): 111–23. http://dx.doi.org/10.2478/v10033-011-0010-2.

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The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms of within sample and out of sample risk-return trade-off at various forecasting horizons. The analysis found that the VEC-GARCH time varying hedge ratio provides the greatest portfolio risk reduction and generates the highest portfolio returns.

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38

Hatemi-J, Abdulnasser, and Eduardo Roca. "Estimating the optimal hedge ratio in the presence of potential unknown structural breaks." Applied Economics 46, no. 8 (January 21, 2014): 790–95. http://dx.doi.org/10.1080/00036846.2013.854303.

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39

Bhaduri, Saumitra N., and S. Raja Sethu Durai. "Optimal hedge ratio and hedging effectiveness of stock index futures: evidence from India." Macroeconomics and Finance in Emerging Market Economies 1, no. 1 (March 2008): 121–34. http://dx.doi.org/10.1080/17520840701859856.

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40

Hatemi-J, Abdulnasser, and Eduardo Roca. "Calculating the optimal hedge ratio: constant, time varying and the Kalman Filter approach." Applied Economics Letters 13, no. 5 (April 15, 2006): 293–99. http://dx.doi.org/10.1080/13504850500365848.

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41

Barbi, Massimiliano, and Silvia Romagnoli. "A Copula-Based Quantile Risk Measure Approach to Estimate the Optimal Hedge Ratio." Journal of Futures Markets 34, no. 7 (April 4, 2013): 658–75. http://dx.doi.org/10.1002/fut.21617.

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42

Heaney, John, and Geoffrey Poitras. "Estimation of the optimal hedge ratio, expected utility, and ordinary least squares regression." Journal of Futures Markets 11, no. 5 (October 1991): 603–12. http://dx.doi.org/10.1002/fut.3990110508.

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43

Echaust, Krzysztof. "How Firms Can Hedge Against Market Risk." Studies in Logic, Grammar and Rhetoric 37, no. 1 (August 8, 2014): 39–49. http://dx.doi.org/10.2478/slgr-2014-0016.

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Abstract The article presents a problem of proper hedging strategy in expected utility model when forward contracts and options strategies are available. We consider a case of hedging when an investor formulates his own expectation on future price of underlying asset. In this paper we propose the way to measure effectiveness of hedging strategy, based on optimal forward hedge ratio. All results are derived assuming a constant absolute risk aversion utility function and a Black-Scholes framework.

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44

Habibi, Reza. "SOME NOTES ABOUT THE MARTINGALE REPRESENTATION THEOREM AND THEIR APPLICATIONS." Ural Mathematical Journal 6, no. 2 (December 26, 2020): 76. http://dx.doi.org/10.15826/umj.2020.2.008.

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An important theorem in stochastic finance field is the martingale representation theorem. It is useful in the stage of making hedging strategies (such as cross hedging and replicating hedge) in the presence of different assets with different stochastic dynamics models. In the current paper, some new theoretical results about this theorem including derivation of serial correlation function of a martingale process and its conditional expectations approximation are proposed. Applications in optimal hedge ratio and financial derivative pricing are presented and sensitivity analyses are studied. Throughout theoretical results, simulation-based results are also proposed. Two real data sets are analyzed and concluding remarks are given. Finally, a conclusion section is given.

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45

Jose, Babu, and D. Lazar. "Should Investor invest in both future and spot market? : An Analysis through Optimal Hedge Ratio." Asian Business Review 1, no. 1 (2012): 21–29. http://dx.doi.org/10.18034/abr.v1i1.140.

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46

Jose, Babu, and D. Lazar. "Should Investor invest in both future and spot market? : An Analysis through Optimal Hedge Ratio." Asian Business Review 1, no. 1 (March 3, 2015): 21. http://dx.doi.org/10.18034/abr.v1i1.333.

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47

YU, Chao, Guo-tai CHI, and Zhong-yuan YANG. "Optimal Model of Hedge Ratio based on Incremental and Existing Portfolio of the Maximum Return Probability." Systems Engineering - Theory & Practice 29, no. 10 (October 2009): 1–12. http://dx.doi.org/10.1016/s1874-8651(10)60074-9.

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48

Williams, Owen. "Foreign currency exposure within country exchange traded funds." Studies in Economics and Finance 33, no. 2 (June 6, 2016): 222–43. http://dx.doi.org/10.1108/sef-10-2014-0196.

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Purpose The purpose of this paper is to consider the implicit effect of the underlying foreign currency exposure on the performance characteristics of country exchange traded funds. Design/methodology/approach To arrive at an overall estimation of the exchange-traded fund (ETF)’s tracking error, the mean of the three measures of tracking error was calculated for both the hedged (r_LC) and unhedged (r_NAV) return series. Since tracking error does not capture all the risk inherent in a country index fund, the study extends the analysis using the Sortino and Modified Sharpe ratios. Findings The decision to hedge currency risk should not be taken on the sole basis of historical volatilities. The investor must also factor in transactions costs, the possible roll of futures contracts and prevailing interest rate differentials. If the rate on the foreign currency is greater than the dollar (euro) rate, the investor will pay for the hedge. If the rate on the foreign currency is less than the dollar (euro) rate, the investor will gain on the trade. Given that hedging entails additional costs, in cases where the neutralization of currency volatility only reduces risk modestly, it would be advisable to leave the exchange rate risk unhedged. We propose two metrics for ETF investors deciding whether to hedge a country ETF’s underlying currency risk. Originality/value The results highlight a key finding: while the majority of country funds accurately track the performance of the underlying foreign index when measured in the local currency, returns in the fund currency can be much more volatile. In breaking down the sources of country fund volatility, the paper demonstrates the impact of the underlying currency movements on overall fund risk. In cases where the currency impact has a significant impact on fund tracking errors, an index-oriented investor benefits from neutralizing the exchange rate effect. Additionally, as the Sortino and Modified Sharpe measures suggest that the underlying currency exposure offers in most cases a better risk-adjusted return for country exchange-traded funds (ETFs) in the listing currency, we also calculate the risk minimizing foreign currency exposure for each fund and propose a decision rule based on the net currency variance to decide whether to hedge the ETF’s currency risk. The optimal hedge ratio indicates that US-based investors should only partially hedge the underlying currency risk while European-based investors are better off fully hedging currency risk.

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Ahmad, Noryati, Ahmad Danial Zainudin, Fahmi Abdul Rahim, and Catherine S F Ho. "EFFECTIVE CROSS HEDGING: EVIDENCE FROM PHYSICAL CRUDE PALM OIL AND ITS INTER-RELATED AGRICULTURAL FUTURES CONTRACTS." Management and Accounting Review (MAR) 17, no. 2 (August 29, 2018): 123. http://dx.doi.org/10.24191/mar.v17i2.812.

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Since its establishment, Crude Palm Oil futures contract (FCPO) has been used to directly hedge its physical crude palm oil (CPO). However, due to the excessive speculation activities on crude palm oil futures market, it has been said to be no longer an effective hedging tool to mitigate the price risk of its underlying physical market. This triggers the need for market players to find possible alternatives to ensure that the hedging role can be executed effectively. Thus this investigation attempts to examine whether other inter-related grains and oil seed futures contracts could serve as effective cross-hedging mechanisms for the CPO. Weekly data of inter-related futures contracts from Chicago Board of Trade (CBOT) and Dalian Commodity Exchange (DCE) are employed to cross hedge the physical crude palm oil prices. The study starts from 2006 until 2016. Empirical results indicate that FCPO is still the best futures contract for hedging purposes while Chicago Soybean (CBOTBO) provides second best alternative if cross-hedging is considered. Keywords: Crude palm oil, Crude palm oil futures, Cross Hedging, Optimal Hedge Ratio, Effective Hedging

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Choi, Byungwook. "Hedging Effectiveness of KOSPI200 Index Futures and Options." Journal of Derivatives and Quantitative Studies 21, no. 3 (August 31, 2013): 275–305. http://dx.doi.org/10.1108/jdqs-03-2013-b0002.

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The purpose of this study is to investigate hedging effectiveness of KOSPI200 index futures and options using three measures proposed by Fishburn (1977), Ederington (1979), and Howard and D’Antonio (1987). The comparison of hedging effectiveness is conducted based on the market prices of KOSPI200 index futures and options traded in Korea Exchange (KRX) between January of 2001 and January of 2011, during which bootstrapping method is utilized to make a dataset of 100,000 random samples with holding period of 1, 3, 6, and 12 months, respectively. We examine the hedging performance of hedge portfolios made of short futures, protective puts and covered calls respectively based on three hedging effectiveness measures. One of our finding is that short futures hedging is better than options in minimizing total volatility risk as well as down-side risk, which is consistent to the previous researches. Also futures hedging is more effective in reducing the VaR than the others. Secondly, the optimal hedge ratios of futures in minimizing total risk and down-side risk are turned out to be 0.97~0.98 and 0.94~0.95 respectively. Third, OTM short call hedge is the best hedging instrument when hedgers would like to maximize the Sharpe ratio. Finally, protective put hedging strategy is in general inferior to the short futures and covered call hedge based on three hedging effectiveness measures.

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