The Experts below are selected from a list of 36336 Experts worldwide ranked by ideXlab platform

Jaya P N Bishwal - One of the best experts on this subject based on the ideXlab platform.

Michael Mcaleer - One of the best experts on this subject based on the ideXlab platform.

  • testing for the box cox parameter for an integrated process
    Mathematics and Computers in Simulation, 2012
    Co-Authors: Jian Huang, Masahito Kobayashi, Michael Mcaleer
    Abstract:

    This paper analyses the constant elasticity of volatility (CEV) model suggested by Chan et al. [K.C. Chan, G.A. Karolyi, F.A. Longstaff, A.B. Sanders, An empirical comparison of alternative models of the short-term interest rate, Journal of Finance 47 (1992) 1209-1227]. The CEV model without mean reversion is shown to be the inverse Box-Cox transformation of integrated processes asymptotically. It is demonstrated that the maximum likelihood estimator of the power parameter has a nonStandard asymptotic Distribution, which is expressed as an integral of Brownian motions, when the data generating process is not mean reverting. However, it is shown that the t-ratio follows a Standard Normal Distribution asymptotically, so that the use of the conventional t-test in analyzing the power parameter of the CEV model is justified even if there is no mean reversion, as is often the case in empirical research. The model may applied to ultra high frequency data.

  • testing the box cox parameter for an integrated process
    Research Papers in Economics, 2010
    Co-Authors: Jian Huang, Masahito Kobayashi, Michael Mcaleer
    Abstract:

    This paper analyses the constant elasticity of volatility (CEV) model suggested by Chan et al. (1992). The CEV model without mean reversion is shown to be the inverse Box-Cox transformation of integrated processes asymptotically. It is demonstrated that the maximum likelihood estimator of the power parameter has a nonStandard asymptotic Distribution, which is expressed as an integral of Brownian motions, when the data generating process is not mean reverting. However, it is shown that the t-ratio follows a Standard Normal Distribution asymptotically, so that the use of the conventional t-test in analyzing the power parameter of the CEV model is justified even if there is no mean reversion, as is often the case in empirical research. The model may applied to ultra high frequency data.

  • testing the box cox parameter in an integrated process
    Social Science Research Network, 2009
    Co-Authors: Jian Huang, Masahito Kobayashi, Michael Mcaleer
    Abstract:

    This paper analyses the constant elasticity of volatility (CEV) model suggested by [6]. The CEV model without mean reversion is shown to be the inverse Box-Cox transformation of integrated processes asymptotically. It is demonstrated that the maximum likelihood estimator of the power parameter has a nonStandard asymptotic Distribution, which is expressed as an integral of Brownian motions, when the data generating process is not mean reverting. However, it is shown that the t-ratio follows a Standard Normal Distribution asymptotically, so that the use of the conventional t-test in analyzing the power parameter of the CEV model is justified even if there is no mean reversion, as is often the case in empirical research. The model may applied to ultra high frequency data.

Amit Chandramohan Kulkarni - One of the best experts on this subject based on the ideXlab platform.

  • monte carlo simulation of economic capital requirement default protection premium
    Social Science Research Network, 2008
    Co-Authors: Amit Chandramohan Kulkarni
    Abstract:

    The paper presents a simulation framework for measuring and managing the default risk of a loan portfolio. Through the dependency of counterparty default on a systematic risk factor, we explore the economic capital requirement for a hypothetical credit portfolio. The study employs bivariate Standard Normal Distribution for mapping asset return correlations into default correlations. Monte Carlo simulations are employed to approximate the loss Distribution and estimate various risk measures. The analysis performed shows that the Asymptotic Single Risk Factor (ASRF) model is a fast way for generating heavy tailed credit loss Distributions. Furthermore, we report complete analytic derivation of Basel II-IRB risk weight functions. The paper also comments on the pricing of single-period Portfolio Default Swaps.

Frederic Vrins - One of the best experts on this subject based on the ideXlab platform.

  • sampling the multivariate Standard Normal Distribution under a weighted sum constraint
    Risks, 2018
    Co-Authors: Frederic Vrins
    Abstract:

    Statistical modeling techniques—and factor models in particular—are extensively used in practice, especially in the insurance and finance industry, where many risks have to be accounted for. In risk management applications, it might be important to analyze the situation when fixing the value of a weighted sum of factors, for example to a given quantile. In this work, we derive the (n−1)-dimensional Distribution corresponding to a n-dimensional i.i.d. Standard Normal vector Z=(Z1,Z2,…,Zn)′ subject to the weighted sum constraint w′Z=c, where w=(w1,w2,…,wn)′ and wi≠0. This law is proven to be a Normal Distribution, whose mean vector μ and covariance matrix Σ are explicitly derived as a function of (w,c). The derivation of the density relies on the analytical inversion of a very specific positive definite matrix. We show that it does not correspond to naive sampling techniques one could think of. This result is then used to design algorithms for sampling Z under constraint that w′Z=c or w′Z≤c and is illustrated on two applications dealing with Value-at-Risk and Expected Shortfall.

  • closed form expression of the multivariate Standard Normal Distribution under a weighted sum constraint
    arXiv: Probability, 2018
    Co-Authors: Frederic Vrins
    Abstract:

    In this letter we derive the $(n-1)$-dimensional Distribution corresponding to a $n$-dimensional i.i.d. Normal Standard vector $Z=(Z_1,Z_2,\ldots,Z_n)$ subjected to the weighted sum constraint $\sum_{i=1}^n w_i Z_i=c$, $w_i\neq 0$. We first address the $n=2$ case before proceeding with the general $n\geq 2$ case. The resulting Distribution is a Normal Distribution whose mean vector $\mu$ and covariance matrix $\Sigma$ are explicitly derived as a function of $w_1,\ldots,w_n,c$. The derivation of the density relies on a very specific positive definite matrix for which the determinant and inverse can be computed analytically.

Jian Huang - One of the best experts on this subject based on the ideXlab platform.

  • testing for the box cox parameter for an integrated process
    Mathematics and Computers in Simulation, 2012
    Co-Authors: Jian Huang, Masahito Kobayashi, Michael Mcaleer
    Abstract:

    This paper analyses the constant elasticity of volatility (CEV) model suggested by Chan et al. [K.C. Chan, G.A. Karolyi, F.A. Longstaff, A.B. Sanders, An empirical comparison of alternative models of the short-term interest rate, Journal of Finance 47 (1992) 1209-1227]. The CEV model without mean reversion is shown to be the inverse Box-Cox transformation of integrated processes asymptotically. It is demonstrated that the maximum likelihood estimator of the power parameter has a nonStandard asymptotic Distribution, which is expressed as an integral of Brownian motions, when the data generating process is not mean reverting. However, it is shown that the t-ratio follows a Standard Normal Distribution asymptotically, so that the use of the conventional t-test in analyzing the power parameter of the CEV model is justified even if there is no mean reversion, as is often the case in empirical research. The model may applied to ultra high frequency data.

  • testing the box cox parameter for an integrated process
    Research Papers in Economics, 2010
    Co-Authors: Jian Huang, Masahito Kobayashi, Michael Mcaleer
    Abstract:

    This paper analyses the constant elasticity of volatility (CEV) model suggested by Chan et al. (1992). The CEV model without mean reversion is shown to be the inverse Box-Cox transformation of integrated processes asymptotically. It is demonstrated that the maximum likelihood estimator of the power parameter has a nonStandard asymptotic Distribution, which is expressed as an integral of Brownian motions, when the data generating process is not mean reverting. However, it is shown that the t-ratio follows a Standard Normal Distribution asymptotically, so that the use of the conventional t-test in analyzing the power parameter of the CEV model is justified even if there is no mean reversion, as is often the case in empirical research. The model may applied to ultra high frequency data.

  • testing the box cox parameter in an integrated process
    Social Science Research Network, 2009
    Co-Authors: Jian Huang, Masahito Kobayashi, Michael Mcaleer
    Abstract:

    This paper analyses the constant elasticity of volatility (CEV) model suggested by [6]. The CEV model without mean reversion is shown to be the inverse Box-Cox transformation of integrated processes asymptotically. It is demonstrated that the maximum likelihood estimator of the power parameter has a nonStandard asymptotic Distribution, which is expressed as an integral of Brownian motions, when the data generating process is not mean reverting. However, it is shown that the t-ratio follows a Standard Normal Distribution asymptotically, so that the use of the conventional t-test in analyzing the power parameter of the CEV model is justified even if there is no mean reversion, as is often the case in empirical research. The model may applied to ultra high frequency data.