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Hedging Performance (hedging + performance)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	100%

Distribution within Business, Economics, Finance and Accounting

General & Introductory Finance & Investments	62%
General & Introductory Economics	18%

Selected Abstracts

Hedging Performance and Stock Market Liquidity: Evidence from the Taiwan Futures Market

ASIA-PACIFIC JOURNAL OF FINANCIAL STUDIES, Issue 3 2010
Hsiu-Chuan Lee
G14; G15; G18 Abstract This paper examines the impact of stock market liquidity on the hedging performance of stock index futures, and extends the conditional OLS model described by Miffre [Journal of Futures Markets 24 (2004) 945] by including stock market liquidity in the regression model. The empirical results indicate that information regarding stock market liquidity is useful in predicting the optimal hedge ratio under different market conditions. In a bear market, the conditional OLS model with stock market liquidity provides the best hedging performance for the out-of-sample period. Although the OLS model outperforms the generalized autoregressive conditional heteroskedasticity and conditional OLS models for the out-of-sample period in a bull market, the conditional OLS model with stock market liquidity outperforms the conditional OLS model without stock market liquidity in terms of downside risks (lower partial moment). [source]

Estimation and hedging effectiveness of time-varying hedge ratio: Flexible bivariate garch approaches

THE JOURNAL OF FUTURES MARKETS, Issue 1 2010
Sung Yong Park
Bollerslev's (1990, Review of Economics and Statistics, 52, 5,59) constant conditional correlation and Engle's (2002, Journal of Business & Economic Statistics, 20, 339,350) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models are usually used to estimate time-varying hedge ratios. In this study, we extend the above model to more flexible ones to analyze the behavior of the optimal conditional hedge ratio based on two (BGARCH) models: (i) adopting more flexible bivariate density functions such as a bivariate skewed- t density function; (ii) considering asymmetric individual conditional variance equations; and (iii) incorporating asymmetry in the conditional correlation equation for the DCC-based model. Hedging performance in terms of variance reduction and also value at risk and expected shortfall of the hedged portfolio are also conducted. Using daily data of the spot and futures returns of corn and soybeans we find asymmetric and flexible density specifications help increase the goodness-of-fit of the estimated models, but do not guarantee higher hedging performance. We also find that there is an inverse relationship between the variance of hedge ratios and hedging effectiveness. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:71,99, 2010 [source]

Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets

THE JOURNAL OF FUTURES MARKETS, Issue 1 2007
Lorne N. Switzer
This study investigates the efficiency of the New York Mercantile Exchange (NYMEX) Division light sweet crude oil futures contract market during recent periods of extreme conditional volatility. Crude oil futures contract prices are found to be cointegrated with spot prices and unbiased predictors of future spot prices, including the period prior to the onset of the Iraqi war and until the formation of the new Iraqi government in April 2005. Both futures and spot prices exhibit asymmetric volatility characteristics. Hedging performance is improved when asymmetries are accounted for. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:61,84, 2007 [source]

An empirical investigation of the GARCH option pricing model: Hedging performance

THE JOURNAL OF FUTURES MARKETS, Issue 12 2003
Haynes H. M. Yung
In this article, we study the empirical performance of the GARCH option pricing model relative to the ad hoc Black-Scholes (BS) model of Dumas, Fleming, and Whaley. Specifically, we investigate the empirical performance of the option pricing model based on the exponential GARCH (EGARCH) process of Nelson. Using S&P 500 options data, we find that the EGARCH model performs better than the ad hoc BS model both in terms of in-sample valuation and out-of-sample forecasting. However, the superiority of out-of-sample performance EGARCH model over the ad hoc BS model is small and insignificant except in the case of deep-out-of-money put options. The out-performance diminishes as one lengthens the forecasting horizon. Interestingly, we find that the more complicated EGARCH model performs worse than the ad hoc BS model in hedging, irrespective of moneyness categories and hedging horizons. For at-the-money and out-of-the-money put options, the underperformance of the EGARCH model in hedging is statistically significant. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:1191,1207, 2003 [source]

Ex Ante Hedging Effectiveness of UK Stock Index Futures Contracts: Evidence for the FTSE 100 and FTSE Mid 250 Contracts

EUROPEAN FINANCIAL MANAGEMENT, Issue 4 2000
Darren Butterworth
Ex ante hedging effectiveness of the FTSE 100 and FTSE Mid 250 index futures contracts is examined for a range of portfolios, consisting of stock market indexes and professionally managed portfolios (investment trust companies). Previous studies which focused on ex post hedging performance using spot portfolios that mirror market indexes are shown to overstate the risk reduction potential of index futures. Although ex ante hedge ratios are found to be characterised by intertemporal instability, ex ante hedging performance of direct hedges and cross hedges approaches that of the ex post benchmark when hedge ratios are estimated using a sufficient window size. [source]

Determination of stock closing prices and hedging performance with stock indices futures

ACCOUNTING & FINANCE, Issue 4 2009
Hsiu-Chuan Lee
G14; G15; G18 Abstract This paper examines the impact of the determination of stock closing prices on futures price efficiency and hedging effectiveness with stock indices futures. The empirical results indicate that the increase in the length of the batching period of the stock closing call improves price efficiency in the futures closing prices and then enhances hedging performance in terms of the hedging risks. Additionally, from a utility-maximization point of view, hedging performance does not improve after the introduction of the 5 min stock closing call, which can be explained by an improvement in price efficiency at the futures market close. [source]

The incremental value of a futures hedge using realized volatility

THE JOURNAL OF FUTURES MARKETS, Issue 9 2010
Yu-Sheng Lai
A number of prior studies have developed a variety of multivariate volatility models to describe the joint distribution of spot and futures, and have applied the results to form the optimal futures hedge. In this study, the authors propose a new class of multivariate volatility models encompassing realized volatility (RV) estimates to estimate the risk-minimizing hedge ratio, and compare the hedging performance of the proposed models with those generated by return-based models. In an out-of-sample context with a daily rebalancing approach, based on an extensive set of statistical and economic performance measures, the empirical results show that improvement can be substantial when switching from daily to intraday. This essentially comes from the advantage that the intraday-based RV potentially can provide more accurate daily covariance matrix estimates than RV utilizing daily prices. Finally, this study also analyzes the effect of hedge horizon on hedge ratio and hedging effectiveness for both the in-sample and the out-of-sample data. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:874,896, 2010 [source]

Pricing VIX futures: Evidence from integrated physical and risk-neutral probability measures

THE JOURNAL OF FUTURES MARKETS, Issue 12 2007
Yueh-Neng Lin
This study derives closed-form solutions to the fair value of VIX (volatility index) futures under alternate stochastic variance models with simultaneous jumps both in the asset price and variance processes. Model parameters are estimated using an integrated analysis of integrated volatility and VIX time series from April 21, 2004 to April 18, 2006. The stochastic volatility model with price jumps outperforms for the short-dated futures, whereas additionally including a state-dependent volatility jump can further reduce out-of-sample pricing errors for other futures maturities. Finally, adding volatility jumps enhances hedging performance except for the short-dated futures on a daily-rebalanced basis. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:1175,1217, 2007 [source]

An empirical analysis of multi-period hedges: Applications to commercial and investment assets

THE JOURNAL OF FUTURES MARKETS, Issue 6 2005
Jimmy E. Hilliard
This study measures the performance of stacked hedge techniques with applications to investment assets and to commercial commodities. The naive stacked hedge is evaluated along with three other versions of the stacked hedge, including those which use exponential and minimum variance ratios. Three commercial commodities (heating oil, light crude oil, and unleaded gasoline) and three investment assets (British Pounds, Deutsche Marks, and Swiss Francs) are examined. The evidence suggests that stacked hedges perform better with investment assets than with commercial commodities. Specifically, deviations from the cost-of-carry model result in nontrivial hedge errors in the stacked hedge. Exponential and minimum variance hedge ratios were found to marginally improve the hedging performance of the stack. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:587,606, 2005 [source]

A Markov regime switching approach for hedging stock indices

THE JOURNAL OF FUTURES MARKETS, Issue 7 2004
Amir Alizadeh
In this paper we describe a new approach for determining time-varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the "state of the market," one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out-of-sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649,674, 2004 [source]

Hedging Performance and Stock Market Liquidity: Evidence from the Taiwan Futures Market

A neural network versus Black,Scholes: a comparison of pricing and hedging performances

JOURNAL OF FORECASTING, Issue 4 2003
Henrik Amilon
Abstract An Erratum has been published for this article in Journal of Forecasting 22(6-7) 2003, 551 The Black,Scholes formula is a well-known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black,Scholes formula. The neural network method is applied to the out-of-sample pricing and delta-hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge-analysis is stressed further in this paper. As benchmarks, the Black,Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level.,Copyright © 2003 John Wiley & Sons, Ltd. [source]

Out-of-sample Hedge Performances for Risk Management in China Commodity Futures Markets,

ASIAN ECONOMIC JOURNAL, Issue 3 2009
Sang-Kuck Chung
C13; C32; G13 We consider a new time-series model that describes long memory and asymmetries simultaneously under the dynamic conditional correlation specification, and that can be used to assess an extensive evaluation of out-of-sample hedging performances using aluminum and fuel oil futures markets traded on the Shanghai Futures Exchange. Upon fitting it to the spot and futures returns of aluminum and fuel oil markets, it is found that a parsimonious version of the model captures the salient features of the data rather well. The empirical results suggest that separating the effects of positive and negative basis on the market volatility, and the correlation between two markets as well as jointly incorporating the long memory effect of the basis on market returns not only provides better descriptions of the dynamic behaviors of commodity prices, but also plays a statistically significant role in determining dynamic hedging strategies. [source]