Conditional Dynamic Hedging with Interest Rate Futures PDF Download
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Author: Georgios D. Giaouris
Publisher:
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Category :
Languages : en
Pages :
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Author: Georgios D. Giaouris
Publisher:
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Category :
Languages : en
Pages :
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Author: Esteban Polidura Frohmader
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Category :
Languages : en
Pages :
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Author: Kenneth F. Kroner
Publisher:
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Category : Financial futures
Languages : en
Pages : 44
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Author: ANTHONY TUCK-KWAI CHAN
Publisher:
ISBN:
Category : Financial futures
Languages : en
Pages : 402
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Treasury bills futures market are chosen for the purpose of empirical study.
Author: Su Dai
Publisher: GRIN Verlag
ISBN: 3656539219
Category : Business & Economics
Languages : en
Pages : 80
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Master's Thesis from the year 2013 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1,7, University of Mannheim, language: English, abstract: The commodity futures contract is an agreement to deliver a specific amount of commodity at a future time . There are usually choices of deliverable grades, delivery locations and delivery dates. Hedging belongs to one of the fundamental functions of futures market. Futures can be used to help producers and buyers protect themselves from price risk arising from many factors. For instance, in crude oil commodities, price risk occurs due to disrupted oil supply as a consequence of political issues, increasing of demand in emerging markets, turnaround in energy policy from the fossil fuel to the solar and efficient energy, etc. By hedging with futures, producers and users can set the prices they will receive or pay within a fixed range. A hedger takes a short position if he/she sells futures contracts while owning the underlying commodity to be delivered; a long position if he/she purchases futures contracts. The commonly known basis is defined as the difference between the futures and spot prices, which is mostly time-varying and mean-reverting. Due to such basis risk, a naïve hedging (equal and opposite) is unlikely to be effective. With the popularity of commodity futures, how to determine and implement the optimal hedging strategy has become an important issue in the field of risk management. Hedging strategies have been intensively studied since the 1960s. One of the most popular approaches to hedging is to quantify risk as variance, known as minimum-variance (MV) hedging. This hedging strategy is based on Markowitz portfolio theory, resting on the result that “a weighted portfolio of two assets will have a variance lower than the weighted average variance of the two individual assets, as long as the two assets are not perfectly and positively correlated.” MV strategy is quite well accepted, however, it ignores the expected return of the hedged portfolio and the risk preference of investors. Other hedging models with different objective functions have been studied intensively in hedging literature. Due to the conceptual simplicity, the value at risk (VaR) and conditional value at risk (C)VaR have been adopted as the hedging risk objective function. [...]
Author: Chih-Chiang Hsu
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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In a number of prior studies it has been demonstrated that the traditional regression-based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies has emerged. In this paper we propose a class of new copula-based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. In regards to the reduction of variance in the returns of hedged portfolios, our empirical results show that in both the in-sample and out-of-sample tests, with full flexibility in the distribution specifications, the copula-based GARCH models perform more effectively than other dynamic hedging models.
Author: Akash Deep
Publisher:
ISBN:
Category : Futures
Languages : en
Pages : 40
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Author: Gregory Koutmos
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Category :
Languages : en
Pages :
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This article proposes a dynamic hedging model for Government National Association Mortgage-Backed Securities (GNMA MBSs) that is free of the drawbacks associated with the static hedging strategies currently used. The simultaneity bias of the regression approach is dealt with by modeling the joint distribution of price changes of GNMA MBSs and 10-year Treasury-note futures. Error correction (EC) terms from cointegrating relationships are included in the conditional mean equations to preserve the long-term equilibrium relationship of the two markets. The time-varying variance-covariance structure of the two markets is modeled via a version of the bivariate generalized autoregressive conditionally heteroskedastic model (bivariante GARCH), which assures that the time-varying variance-covariance matrix is positive semidefinite for all time periods. This dynamic error-correction GARCH model is estimated using daily data on six different coupon GNMA MBSs. Dynamic cross-hedge ratios are obtained from the time-varying variance-covariance matrix using the 10-year Treasury-note futures contract as the hedging instrument. These ratios are evaluated in terms of both overall risk reduction and expected utility maximization. There is overwhelming evidence that dynamic hedge ratios are superior to static ones even when transaction costs are incorporated into the analysis. This conclusion holds for all six different coupon GNMA MBSs under investigation.
Author:
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Category : Canada
Languages : en
Pages : 48
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Author: Gyu-Hyun Moon
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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This article examines the hedging performance of the conventional OLS model and a variety of dynamic hedging models for the in-sample and out-of-sample periods of Korean daily KOSDAQ STAR (KOSTAR) index futures. We employ the rolling OLS and various popular multivariate GARCH models to estimate and forecast the conditional covariances and variances of KOSTAR spot and futures returns. The paper finds that dynamic hedging methods outperform the conventional method for the out-of-sample period. However, the simple rolling OLS is superior to all the other popular multivariate GARCH models.
