“…For example, implied correlation estimates can be extracted from traded spread options [46], best-of basket options [19], and quanto options [4]. Implied correlation estimates based on various multiasset products are discussed in Austing [2].…”
Section: Definition 1 (Implied Lévy Correlation) Consider the One-facmentioning
confidence: 99%
“…This model was generalized in Semeraro [44], Luciano and Semeraro [33], and Guillaume [21]. A stochastic correlation model was considered in Fonseca et al [19]. A framework for modeling dependence in finance using copulas was described in Cherubini et al [14].…”
Section: Introductionmentioning
confidence: 99%
“…Several papers consider the problem of measuring implied correlation between stock prices; see e.g. Fonseca et al [19], Tavin [46], Ballotta et al [4], and Austing [2]. Our approach is different in that we determine implied correlation estimates in the onefactor Lévy model using multi-asset derivatives consisting of many assets (30 assets for the Dow Jones).…”
In this paper we employ a one-factor Lévy model to determine basket option prices. More precisely, basket option prices are determined by replacing the distribution of the real basket with an appropriate approximation. For the approximate basket we determine the underlying characteristic function and hence we can derive the related basket option prices by using the Carr-Madan formula. We consider a three-moments-matching method. Numerical examples illustrate the accuracy of our approximations; several Lévy models are calibrated to market data and basket option prices are determined. In the last part we show how our newly designed basket option pricing formula can be used to define implied Lévy correlation by matching model and market prices for basket options. Our main finding is that the implied Lévy correlation smile is flatter than its Gaussian counterpart. Furthermore, if (near) atthe-money option prices are used, the corresponding implied Gaussian correlation estimate is a good proxy for the implied Lévy correlation.
“…For example, implied correlation estimates can be extracted from traded spread options [46], best-of basket options [19], and quanto options [4]. Implied correlation estimates based on various multiasset products are discussed in Austing [2].…”
Section: Definition 1 (Implied Lévy Correlation) Consider the One-facmentioning
confidence: 99%
“…This model was generalized in Semeraro [44], Luciano and Semeraro [33], and Guillaume [21]. A stochastic correlation model was considered in Fonseca et al [19]. A framework for modeling dependence in finance using copulas was described in Cherubini et al [14].…”
Section: Introductionmentioning
confidence: 99%
“…Several papers consider the problem of measuring implied correlation between stock prices; see e.g. Fonseca et al [19], Tavin [46], Ballotta et al [4], and Austing [2]. Our approach is different in that we determine implied correlation estimates in the onefactor Lévy model using multi-asset derivatives consisting of many assets (30 assets for the Dow Jones).…”
In this paper we employ a one-factor Lévy model to determine basket option prices. More precisely, basket option prices are determined by replacing the distribution of the real basket with an appropriate approximation. For the approximate basket we determine the underlying characteristic function and hence we can derive the related basket option prices by using the Carr-Madan formula. We consider a three-moments-matching method. Numerical examples illustrate the accuracy of our approximations; several Lévy models are calibrated to market data and basket option prices are determined. In the last part we show how our newly designed basket option pricing formula can be used to define implied Lévy correlation by matching model and market prices for basket options. Our main finding is that the implied Lévy correlation smile is flatter than its Gaussian counterpart. Furthermore, if (near) atthe-money option prices are used, the corresponding implied Gaussian correlation estimate is a good proxy for the implied Lévy correlation.
“…As suggested by [29], we annualize the daily volatility by multiplying by the square root of 250 ≈ 5998 24 , which is the number of trading days instead of the number of calendar days. In the next step, we compute the needed parameters for the explicit model (6), as well as for the implicit model (2). Hence, we use a moving window approach to compute the parameters c, ρ and λ based on different non-overlapping samples of a window size k. Always starting from 31 December 2013 and going backwards in time, this approach results in a sample of size n = 5998 k .…”
Section: Empirical Analysis Of Historical Datamentioning
confidence: 99%
“…The best solution to this challenge is a stochastic covariance model, as those stochastic matrices tackle both features, volatility and correlation, simultaneously (see [1][2][3]). In such a context, when it comes to the actual pricing of products, practitioners either determine model parameters from option prices (usually called calibration) or obtain the parameters from historical observations of the underlying (estimation methodology).…”
This paper presents a comprehensive extension of pricing two-dimensional derivatives depending on two barrier constraints. We assume randomness on the covariance matrix as a way of generalizing. We analyse common barrier derivatives, enabling us to study parameter uncertainty and the risk related to the estimation procedure (estimation risk). In particular, we use the distribution of empirical parameters from IBM and EURO STOXX50. The evidence suggests that estimation risk should not be neglected in the context of multidimensional barrier derivatives, as it could cause price differences of up to 70%.
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