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Bezirgen Veliyev - One of the best experts on this subject based on the ideXlab platform.

  • utility maximization in a Binomial Model with transaction costs a duality approach based on the shadow price process
    International Journal of Theoretical and Applied Finance, 2014
    Co-Authors: Christian Bayer, Bezirgen Veliyev
    Abstract:

    We consider the problem of optimizing the expected logarithmic utility of the value of a portfolio in a Binomial Model with proportional transaction costs with a long time horizon. By duality methods, we can find expressions for the boundaries of the no-trade-region and the asymptotic optimal growth rate, which can be made explicit for small transaction costs (in the sense of an asymptotic expansion). Here we find that, contrary to the classical results in continuous time, see Janecek and Shreve (2004), Finance and Stochastics8, 181–206, the size of the no-trade-region as well as the asymptotic growth rate depend analytically on the level λ of transaction costs, implying a linear first-order effect of perturbations of (small) transaction costs, in contrast to effects of orders λ1/3 and λ2/3, respectively, as in continuous time Models. Following the recent study by Gerhold et al. (2013), Finance and Stochastics17, 325–354, we obtain the asymptotic expansion by an almost explicit construction of the shadow price process.

  • utility maximization in a Binomial Model with transaction costs a duality approach based on the shadow price process
    2012
    Co-Authors: Christian Bayer, Bezirgen Veliyev
    Abstract:

    We consider the problem of optimizing the expected logarithmic utility of the value of a portfolio in a Binomial Model with proportional transaction costs with a long time horizon. By duality methods, we can find expressions for the boundaries of the no-trade-region and the asymptotic optimal growth rate, which can be made explicit for small transaction costs. Here we find that, contrary to the classical results in continuous time, the size of the no-trade-region as well as the asymptotic growth rate depend analytically on the level of transaction costs, implying a linear first order effect of perturbations of (small) transaction costs. We obtain the asymptotic expansion by an almost explicit construction of the shadow price process.

Yang Ning - One of the best experts on this subject based on the ideXlab platform.

  • meta analysis of studies with bivariate binary outcomes a marginal beta Binomial Model approach
    Statistics in Medicine, 2016
    Co-Authors: Yong Chen, Chuan Hong, Yang Ning, Xiao Su
    Abstract:

    When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-Binomial Model for the meta-analysis of studies with binary outcomes. This Model is based on the composite likelihood approach and has several attractive features compared with the existing Models such as bivariate generalized linear mixed Model (Chu and Cole, 2006) and Sarmanov beta-Binomial Model (Chen et al., 2012). The advantages of the proposed marginal Model include Modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-Binomial Model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-Binomial Model with the bivariate generalized linear mixed Model and the Sarmanov beta-Binomial Model by simulation studies. Interestingly, the results show that the marginal beta-Binomial Model performs better than the Sarmanov beta-Binomial Model, whether or not the true Model is Sarmanov beta-Binomial, and the marginal beta-Binomial Model is more robust than the bivariate generalized linear mixed Model under Model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case–control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

  • meta analysis of studies with bivariate binary outcomes a marginal beta Binomial Model approach
    Statistics in Medicine, 2016
    Co-Authors: Yong Chen, Chuan Hong, Yang Ning
    Abstract:

    When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-Binomial Model for the meta-analysis of studies with binary outcomes. This Model is based on the composite likelihood approach and has several attractive features compared with the existing Models such as bivariate generalized linear mixed Model (Chu and Cole, 2006) and Sarmanov beta-Binomial Model (Chen et al., 2012). The advantages of the proposed marginal Model include Modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-Binomial Model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-Binomial Model with the bivariate generalized linear mixed Model and the Sarmanov beta-Binomial Model by simulation studies. Interestingly, the results show that the marginal beta-Binomial Model performs better than the Sarmanov beta-Binomial Model, whether or not the true Model is Sarmanov beta-Binomial, and the marginal beta-Binomial Model is more robust than the bivariate generalized linear mixed Model under Model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration.

  • Meta‐analysis of studies with bivariate binary outcomes: a marginal beta‐Binomial Model approach
    Statistics in medicine, 2015
    Co-Authors: Yong Chen, Chuan Hong, Yang Ning
    Abstract:

    When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-Binomial Model for the meta-analysis of studies with binary outcomes. This Model is based on the composite likelihood approach and has several attractive features compared with the existing Models such as bivariate generalized linear mixed Model (Chu and Cole, 2006) and Sarmanov beta-Binomial Model (Chen et al., 2012). The advantages of the proposed marginal Model include Modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-Binomial Model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-Binomial Model with the bivariate generalized linear mixed Model and the Sarmanov beta-Binomial Model by simulation studies. Interestingly, the results show that the marginal beta-Binomial Model performs better than the Sarmanov beta-Binomial Model, whether or not the true Model is Sarmanov beta-Binomial, and the marginal beta-Binomial Model is more robust than the bivariate generalized linear mixed Model under Model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration.

Yong Chen - One of the best experts on this subject based on the ideXlab platform.

  • meta analysis of studies with bivariate binary outcomes a marginal beta Binomial Model approach
    Statistics in Medicine, 2016
    Co-Authors: Yong Chen, Chuan Hong, Yang Ning, Xiao Su
    Abstract:

    When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-Binomial Model for the meta-analysis of studies with binary outcomes. This Model is based on the composite likelihood approach and has several attractive features compared with the existing Models such as bivariate generalized linear mixed Model (Chu and Cole, 2006) and Sarmanov beta-Binomial Model (Chen et al., 2012). The advantages of the proposed marginal Model include Modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-Binomial Model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-Binomial Model with the bivariate generalized linear mixed Model and the Sarmanov beta-Binomial Model by simulation studies. Interestingly, the results show that the marginal beta-Binomial Model performs better than the Sarmanov beta-Binomial Model, whether or not the true Model is Sarmanov beta-Binomial, and the marginal beta-Binomial Model is more robust than the bivariate generalized linear mixed Model under Model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case–control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

  • meta analysis of studies with bivariate binary outcomes a marginal beta Binomial Model approach
    Statistics in Medicine, 2016
    Co-Authors: Yong Chen, Chuan Hong, Yang Ning
    Abstract:

    When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-Binomial Model for the meta-analysis of studies with binary outcomes. This Model is based on the composite likelihood approach and has several attractive features compared with the existing Models such as bivariate generalized linear mixed Model (Chu and Cole, 2006) and Sarmanov beta-Binomial Model (Chen et al., 2012). The advantages of the proposed marginal Model include Modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-Binomial Model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-Binomial Model with the bivariate generalized linear mixed Model and the Sarmanov beta-Binomial Model by simulation studies. Interestingly, the results show that the marginal beta-Binomial Model performs better than the Sarmanov beta-Binomial Model, whether or not the true Model is Sarmanov beta-Binomial, and the marginal beta-Binomial Model is more robust than the bivariate generalized linear mixed Model under Model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration.

  • Meta‐analysis of studies with bivariate binary outcomes: a marginal beta‐Binomial Model approach
    Statistics in medicine, 2015
    Co-Authors: Yong Chen, Chuan Hong, Yang Ning
    Abstract:

    When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-Binomial Model for the meta-analysis of studies with binary outcomes. This Model is based on the composite likelihood approach and has several attractive features compared with the existing Models such as bivariate generalized linear mixed Model (Chu and Cole, 2006) and Sarmanov beta-Binomial Model (Chen et al., 2012). The advantages of the proposed marginal Model include Modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-Binomial Model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-Binomial Model with the bivariate generalized linear mixed Model and the Sarmanov beta-Binomial Model by simulation studies. Interestingly, the results show that the marginal beta-Binomial Model performs better than the Sarmanov beta-Binomial Model, whether or not the true Model is Sarmanov beta-Binomial, and the marginal beta-Binomial Model is more robust than the bivariate generalized linear mixed Model under Model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration.

Dietmar Leisen - One of the best experts on this subject based on the ideXlab platform.

  • The random-time Binomial Model
    Journal of Economic Dynamics and Control, 1999
    Co-Authors: Dietmar Leisen
    Abstract:

    Abstract In this paper we study a Binomial Model with random time steps and explain how to calculate values for European and American call and put options. We prove both weak convergence of the discrete processes to the Black–Scholes setup and convergence of the values for European and American put options. Computational experiments exhibit a smooth convergence structure and suggest that we can obtain a quadratic order of convergence via an extrapolation procedure. Approximations to jump-diffusions are straightforward.

  • The Random-Time Binomial Model
    1998
    Co-Authors: Dietmar Leisen
    Abstract:

    In this paper we study Binomial Models with random time steps. We explain, how calculating values for European and American Call and Put options is straightforward for the Random-Time Binomial Model. We present the conditions to ensure weak-convergence to the Black-Scholes setup and convergence of the values for European and American put options. Differently to the CRR-Model the convergence behaviour is extremely smooth in our Model. By using extrapolation we therefore achieve order of convergence two. This way it is an efficient tool for pricing purposes in the Black-Scholes setup, since the CRR Model and its extrapolations typically achieve order one. Moreover our Model allows in a straightforward manner to construct approximations to jump-diffusions. The simple valuation approaches and the convergence properties carry immediately over from the Black-Scholes case.

  • The Random-Time Binomial Model
    SSRN Electronic Journal, 1997
    Co-Authors: Dietmar Leisen
    Abstract:

    In this paper we study a Binomial Model with random time steps and explain how to calculate values for European and American call and put options. We prove weak convergence of the discrete processes to the Black{Scholes setup as well as convergence of the values for European and American put options. Computational experiments exhibit a smooth convergence structure and suggest that we can obtain an order of convergence of two via an extrapolation procedure. Approximations to jump {diffusions are straightforward.

Christian Bayer - One of the best experts on this subject based on the ideXlab platform.

  • utility maximization in a Binomial Model with transaction costs a duality approach based on the shadow price process
    International Journal of Theoretical and Applied Finance, 2014
    Co-Authors: Christian Bayer, Bezirgen Veliyev
    Abstract:

    We consider the problem of optimizing the expected logarithmic utility of the value of a portfolio in a Binomial Model with proportional transaction costs with a long time horizon. By duality methods, we can find expressions for the boundaries of the no-trade-region and the asymptotic optimal growth rate, which can be made explicit for small transaction costs (in the sense of an asymptotic expansion). Here we find that, contrary to the classical results in continuous time, see Janecek and Shreve (2004), Finance and Stochastics8, 181–206, the size of the no-trade-region as well as the asymptotic growth rate depend analytically on the level λ of transaction costs, implying a linear first-order effect of perturbations of (small) transaction costs, in contrast to effects of orders λ1/3 and λ2/3, respectively, as in continuous time Models. Following the recent study by Gerhold et al. (2013), Finance and Stochastics17, 325–354, we obtain the asymptotic expansion by an almost explicit construction of the shadow price process.

  • utility maximization in a Binomial Model with transaction costs a duality approach based on the shadow price process
    2012
    Co-Authors: Christian Bayer, Bezirgen Veliyev
    Abstract:

    We consider the problem of optimizing the expected logarithmic utility of the value of a portfolio in a Binomial Model with proportional transaction costs with a long time horizon. By duality methods, we can find expressions for the boundaries of the no-trade-region and the asymptotic optimal growth rate, which can be made explicit for small transaction costs. Here we find that, contrary to the classical results in continuous time, the size of the no-trade-region as well as the asymptotic growth rate depend analytically on the level of transaction costs, implying a linear first order effect of perturbations of (small) transaction costs. We obtain the asymptotic expansion by an almost explicit construction of the shadow price process.