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    Öğe
    Analysis of asymmetric financial data with directional dependence measures
    (Hacettepe Univ, Fac Sci, 2023) Kara, Emel Kizilok; Kemaloglu, Sibel Acik; Evkaya, O. Ozan
    The increase of the product variety in the financial markets requires a clear understanding of the dependence between such instruments for the decision-makers. For a few decades, such dependence structures were often modeled with symmetric copula families. How-ever, financial data may reveal an asymmetric structure, which can be determined via directional dependence measures in the context of copulas. Previously, some asymmetric copula models were proposed in different ways using Khoudraji's device. But they are merely used for financial time series data in a broader sense. In this study, a new set of asymmetric copulas were defined by using one parameter of Archimedean copula families. For this aim, widely used copula families were studied and the corresponding directional dependence measures were analyzed. To illustrate the efficiency of the parameter estima-tion method, a small simulation scenario consisting of an asymmetric dependence pattern was carried out. Thereafter, the proposed asymmetric bi-variate copulas with directional dependence coefficients were investigated for two different stock market data. The study's primary findings suggested that the newly generated asymmetric models might be useful for directional dependence. Especially, the estimated directional dependence coefficients can serve as an indicator to explain the variability of one stock in terms of the other.
  • Küçük Resim
    Öğe
    Modeling Dependent Financial Assets By Dynamic Copula And Portfolio Optimization Based On Cvar
    (Ankara Univ, Fac Sci, 2015) Kemaloglu, Sibel Acik; Kara, Emel Kizilok
    This paper is concerned with the statistical modeling of the dependence structure of multivariate financial data using copula. Since financial data is greatly affected by the economic factors, it often varies according to the time. Therefore, dynamic copula model is used that takes into account the time-varying. In addition, portfolio optimization based on Mean-CVaR model is applied with Monte Carlo simulation. As an application, a portfolio with four different Indexes is constructed from the Turkish financial markets. The marginal distributions of assets in the portfolio are estimated and parameter estimates are given for the different copula models. The portfolio optimization based on CVaR is made for the portfolio created from the specified copula model.