2007
DOI: 10.1017/s0022109000003483
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Basis Convergence and Long Memory in Volatility When Dynamic Hedging with Futures

Abstract: When market returns follow a long memory volatility process, standard approaches to estimating dynamic minimum variance hedge ratios (MVHRs) are misspecified. Simulation results and an application to the S&P 500 index document the magnitude of the misspecification that results from failure to account for basis convergence and long memory in volatility. These results have important implications for the estimation of MVHRs in the S&P 500 example and other markets as well.

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Cited by 21 publications
(14 citation statements)
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“…Previous research has employed bivariate MS-GARCH (Lee and Yoder, 2007;Lee, 2009), MS-VECM-GARCH (Alizadeh et al, 2008) and bivariate FIGARCH (Dark, 2007), yet there has been no attempt to jointly capture cointegration, long memory, asymmetries and structural change.…”
Section: Applicationmentioning
confidence: 99%
See 1 more Smart Citation
“…Previous research has employed bivariate MS-GARCH (Lee and Yoder, 2007;Lee, 2009), MS-VECM-GARCH (Alizadeh et al, 2008) and bivariate FIGARCH (Dark, 2007), yet there has been no attempt to jointly capture cointegration, long memory, asymmetries and structural change.…”
Section: Applicationmentioning
confidence: 99%
“…Lien (1996) shows that failure to allow for cointegration generally results in a smaller than optimal hedge ratio, and this has a significant adverse effect on hedging. Failure to allow for regime switching (Alizadeh et al, 2008;Lee, 2011) and long memory in volatility (Dark, 2007) also results in a deterioration in hedging performance. The evidence with respect to volatility asymmetries though is mixed (Meneu and Torro, 2003;Lien and Yang, 2006).…”
Section: Introductionmentioning
confidence: 99%
“…Not only the return series itself but also its volatility is an important input for investment, derivatives pricing, and financial market regulation (Taylor (2000), Poon and Granger (2003) and Dark (2007)). Furthermore, volatility is used for the measurement of value-at-risk (VaR) in risk management (Jorion (2000)).…”
Section: Long Memory Properties Of Financial Time Seriesmentioning
confidence: 99%
“…Long memory in conditional volatility of exchange rate has long been recognized in the literature, see, for example, Cheung (1993),Baillie, Bollerslev, and Mikkelsen (1996), andTeyssiere (1997) Dark (2005). documented similar property in stock returns whereasBunetti and Gilbert (2000) found long memory conditional volatility in crude oil futures prices.…”
mentioning
confidence: 97%