Analysis of volatility feedback and leverage effects on the ISE30 index using high frequency data

Date

2014-03-15

Author

Inkaya, A.
Okur, Y. Yolcu

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

234
views

0
downloads

In this study, we employ the techniques of Malliavin calculus to analyze the volatility feedback and leverage effects for a better understanding of financial market dynamics. We estimate both effects for a general semimartingale model applying Fourier analysis developed in Malliavin and Mancino (2002) [10]. We further investigate their joint behaviour using 5 min data of the ISE30 index. On the basis of these estimations, we look for the evidence that volatility feedback effect rate can be employed in the stability analysis of financial markets.

Subject Keywords

Applied Mathematics, Computational Mathematics

URI

https://hdl.handle.net/11511/65793

Journal

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

DOI

https://doi.org/10.1016/j.cam.2013.06.024

Collections

Graduate School of Applied Mathematics, Article

Suggestions

OpenMETU
Core

RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty Ozmen, Ayse; Weber, Gerhard Wilhelm (Elsevier BV, 2014-03-15) Since, with increased volatility and further uncertainties, financial crises translated a high "noise" within data from financial markets and economies into the related models, recent years' events in the financial world have led to radically untrustworthy representations of the future. Hence, robustification started to attract more attention in finance. The presence of noise and data uncertainty raises critical problems to be dealt with on the theoretical and computational side. For immunizing against para...
On the correlation of the supremum and the infimum and of maximum gain and maximum loss of Brownian motion with drift Vardar Acar, Ceren; Szekely, Gabor J. (Elsevier BV, 2013-08-15) Investors are naturally interested in the supremum and the infimum of stock prices, also in the maximum gain and the maximum loss over a time period. To shed light on these relatively complicated aspects of sample paths, we consider Brownian motion with and without drift. We provide explicit calculations of the correlation between the supremum and the infimum of Brownian motion with drift. We establish a number of results concerning the distributions of maximum gain and maximum loss. We present simulation s...
Dynamic programming for a Markov-switching jump-diffusion Azevedo, N.; Pinheiro, D.; Weber, Gerhard Wilhelm (Elsevier BV, 2014-09-01) We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump-diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman's optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton-Jacobi-Belman equation, which turns out to be a partial in...
Stochastic volatility and stochastic interest rate model with jump and its application on General Electric data Celep, Betül; Hayfavi, Azize; Department of Financial Mathematics (2011) In this thesis, we present two different approaches for the stochastic volatility and stochastic interest rate model with jump and analyze the performance of four alternative models. In the first approach, suggested by Scott, the closed form solution for prices on European call stock options are developed by deriving characteristic functions with the help of martingale methods. Here, we study the asset price process and give in detail the derivation of the European call option price process. The second appr...
Analysis of Model Variance for Ensemble Based Turbulence Modeling Jiang, Nan; Kaya Merdan, Songül; Layton, William (Walter de Gruyter GmbH, 2015-04-01) This report develops an ensemble or statistical eddy viscosity model. The model is parameterized by an ensemble of solutions of an ensemble-Leray regularization. The combined approach of ensemble time stepping and ensemble eddy viscosity modeling allows direct parametrization of the turbulent viscosity co-efficient. We prove unconditional stability and that the model's solution approaches statistical equilibrium as t -> infinity; the model's variance converges to zero as t -> infinity. The ensemble method i...

Citation Formats

A. Inkaya and Y. Y. Okur, “Analysis of volatility feedback and leverage effects on the ISE30 index using high frequency data,” JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, pp. 377–384, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65793.