scholarly journals Parsimonious Heterogeneous ARCH Models for High Frequency Modeling

2020 ◽  
Vol 13 (2) ◽  
pp. 38
Author(s):  
Juan Carlos Ruilova ◽  
Pedro Alberto Morettin
Keyword(s):  
Financial Market ◽  
Long Memory ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Arch Models ◽  
Heterogeneous Information ◽  
Modeling Process ◽  
Proposed Model ◽  
The Garch Model

In this work we study a variant of the GARCH model when we consider the arrival of heterogeneous information in high-frequency data. This model is known as HARCH(n). We modify the HARCH(n) model when taking into consideration some market components that we consider important to the modeling process. This model, called parsimonious HARCH(m,p), takes into account the heterogeneous information present in the financial market and the long memory of volatility. Some theoretical properties of this model are studied. We used maximum likelihood and Griddy-Gibbs sampling to estimate the parameters of the proposed model and apply it to model the Euro-Dollar exchange rate series.

scholarly journals Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

2006 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Marcelo C. Carvalho ◽  
Marco Aurélio S. Freire ◽  
Marcelo Cunha Medeiros ◽  
Leonardo R. Souza
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Value At Risk ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Normality Assumption ◽  
Out Of Sample ◽  
Management Area

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

scholarly journals Inference for time-varying lead–lag relationships from ultra-high-frequency data

2021 ◽  
Author(s):  
Yuta Koike
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Data ◽  
Time Varying ◽  
Frequency Data ◽  
Statistical Methodology ◽  
Microstructure Noise ◽  
New Approach ◽  
Proposed Model ◽  
Derivative Products

AbstractA new approach for modeling lead–lag relationships in high-frequency financial markets is proposed. The model accommodates non-synchronous trading and market microstructure noise as well as intraday variations of lead–lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented, as well. The methodology is illustrated by an empirical study to detect lead–lag relationships between the S&P 500 index and its two derivative products.

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