arXiv subjects

Kyong-Hui Kim

Publications and source records attributed to Kyong-Hui Kim.

3 recordsLinked to original sources

arXiv2014

A goodness-of-fit test of the errors in nonlinear autoregressive time series models with stationary $α$-mixing error terms

In this work we deal with the problem of fitting an error density to the goodness-of-fit test of the errors in nonlinear autoregressive time series models with stationary $α$-mixing error terms. The test statistic is based on the integrated squared error of the nonparametric error density estimate and the null error density. By deriving the asymptotic normality of test statistics in these models, we extend the result of Cheng and Sun (Statist. Probab. Lett. \textbf{78}, 1(2008), 50-59) in the model with i.i.d error terms to the more general case.

math.ST↗

arXiv2014

Efficient hedging in general Black-Scholes model

An investor faced with a contingent claim may eliminate risk by perfect hedging, but as it is often quite expensive, he seeks partial hedging (quantile hedging or efficient hedging) that requires less capital and reduces the risk. Efficient hedging for European call option was considered in the standard Black-Scholes model with constant drift and volatility coefficients. In this paper we considered the efficient hedging for European call option in general Black-Scholes model $dX_t=X_t(m(t)dt+σ(t)dw(t))$ with time-varying drift and volatility coefficients and in fractional Black-Scholes model $dX_t=X_t(σB_H(t)+mdt)$ with constant coefficients.

q-fin.PR↗

arXiv2014

Asymptotic Normality of Estimates in Flexible Seasonal Time Series Model with Weak Dependent Error Terms

In this article, we consider flexible seasonal time series models which consist of a common trend function over periods and additive individual trend (seasonal effect) functions. The consistency and asymptotic normality of the local linear estimators were obtained under the $α$-mixing conditions and without specifying the error distribution. We develop these results to consistency and asymptotic normality of local linear estimates by using central limit theorems for flexible seasonal time series model, which error terms are $k$-weak dependent and $λ$-weak dependent random variables.

math-ph↗