Using derivatives in major currencies for cross-hedging currency risks in Asian emergency markets

Aggarwal, Raj; DeMaskey, Andrea L.

doi:10.1002/(sici)1096-9934(199710)17:7<781::aid-fut3>3.0.co;2-j

Cited by 22 publications

(18 citation statements)

References 12 publications

(20 reference statements)

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“…Nonetheless, some recent studies have suggested that trading strategies guided by forecasts on the direction of the change in price level may be more effective and thus can generate higher profits. Aggarwal and Demaskey (1997) report that the performance of cross hedging improves significantly if the direction of changes in exchange rates can be predicted. In another study, Maberly (1986) explores the relationship between the direction of inter-day and intra-day price changes on the S&P 500 futures.…”

Section: Data Selection and Model Developmentmentioning

confidence: 99%

“…To this end, it has been found that stock trading driven by a certain forecast with a small forecasting error may not be as profitable as trading guided by an accurate prediction of the sign of stock return (Aggarwal and Demaskey, 1997;Leung et al, 2000;Maberly, 1986;Wu and Zhang, 1997). Nonetheless, having an accurate prediction of a certain stock or stock index return still has numerous benefits.…”

Section: Introductionmentioning

confidence: 99%

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The use of data mining and neural networks for forecasting stock market returns

Enke

Thawornwong

2005

Expert Systems with Applications

393

130

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Section: Data Selection and Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of data mining and neural networks for forecasting stock market returns

Enke

Thawornwong

2005

Expert Systems with Applications

393

130

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“…However, it is a well-established fact that a financial time series observes time varying patterns, and that volatility clustering is their innate feature (Gupta and Singh, 2009). Hence, voluminous literature has found that time-varying hedge ratios are superior to constant hedge ratios (Myers, 1991;Park and Switzer, 1995;Aggarwal and Demaskey, 1997;Moschini and Myers, 2002;Harris and Shen, 2003;Pattarin and Ferretti, 2004;Kofman and McGlenchy, 2005;Floros and Vougas, 2006;Bhaduri and Durai, 2007;Yoder, 2007 andYang andLai, 2009). Furthermore, Yang and Allen (2004) confirmed that a time-varying hedge ratio performs better during out of sample hedging.…”

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

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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.

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“…[3] investigated on the predictability of the direction of change in the future spot exchange. [4] concluded that the performance of cross-hedging improves if the direction of changes in exchange rates can be predicted. [1] provided a comparative evaluation of the forecasting performance of a group of classification models to that of a group of level estimation models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Stock Price Movement Using Continuous Time Models

Sonono¹,

Mashele²

2015

JMF

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Predicting stock price movement is generally accepted to be challenging such that until today it is continuously being attempted. This paper attempts to address the problem of stock price movement using continuous time models. Specifically, the paper provides comparative analysis of continuous time models-General Brownian Motion (GBM) and Variance Gamma (VG) in predicting the direction and accurate stock price levels using Monte Carlo methods-Quasi Monte Carlo (QMC) and Least Squares Monte Carlo (LSMC). The hit ratio and mean-absolute percentage error (MAPE) were used to evaluate the models. The empirical tests suggest that either the GBM model or VG model in any Monte Carlo method can be used to predict the direction of stock price movement. In terms of predicting the stock price values, the empirical findings suggest that the GBM model performs well in the QMC method and the VG model performs well in the LSMC method.

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Using derivatives in major currencies for cross-hedging currency risks in Asian emergency markets

Cited by 22 publications

References 12 publications

The use of data mining and neural networks for forecasting stock market returns

The use of data mining and neural networks for forecasting stock market returns

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

Prediction of Stock Price Movement Using Continuous Time Models

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