Multivariate tempered stable model with long-range dependence and time-varying volatility
نویسنده
چکیده
High-frequency financial return time series data have stylized facts such as the long-range dependence, fat-tails, asymmetric dependence, and volatility clustering. In this paper, a multivariate model which describes those stylized facts is presented. To construct the model, a multivariate ARMA-GARCH model is considered along with fractional Lévy process. The fractional Lévy process in this paper is defined by the stochastic integral with a tempered stable driving process. Parameters of the new model are fit to high-frequency returns for five U.S stocks. Approximated form of portfolio value-at-risk and average value-at-risk are provided and portfolio optimization is discussed under the model.
منابع مشابه
Long Memory Dynamics for Multivariate Dependence under Heavy Tails
We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the data. In the empirical study for four Dow Jones equities, we find that the degree of memory in the v...
متن کاملExtreme News Events, Long-memory Volatility, and Time Varying Risk Premia in Stock Market Returns
This paper proposes a new GARCH-jump in mean model to test the presence of time varying risk premia associated with normal and extreme news events. The model allows for a dynamic jump component with autoregressive jump intensity, long-range dependence in volatility dynamics, and a volatility in mean structure separately for the normal and extreme news events. The results show significant jump r...
متن کاملSpecification Testing for Multivariate Time Series Volatility Models
Volatility models have been playing an important role in economics and finance. Using a multivariate generalized spectral approach, we propose a new class of generally applicable omnibus tests for univariate and multivariate volatility models. Both GARCH models and stochastic volatility models are covered. Our tests have a convenient asymptotic null N(0,1) distribution, and can detect a wide ra...
متن کاملTempered Fractional Stable Motion
Tempered fractional stable motion adds an exponential tempering to the power-law kernel in a linear fractional stable motion, or a shift to the power-law filter in a harmonizable fractional stable motion. Increments from a stationary time series that can exhibit semi-long-range dependence. This paper develops the basic theory of tempered fractional stable processes, including dependence structu...
متن کاملCopula-based Multivariate GARCH Model with Uncorrelated Dependent Errors∗
Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our ne...
متن کامل