Estimating the Value-at-Risk from High-frequency Data

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of models throughout our experiments: (i) the Susceptible-Infective-Removed (SIR) that predicts the infections’ peak on 2020-03-27; (ii) volatility (GARCH), correlation (DCC), and risk-management (Value-at-Risk (VaR)) models that relate how bears painted Wall Street red; and, (iii) data-science trees algorithms with forward prunning, mosaic plots, and Pythagorean forests that crunch the data on confirmed, deaths, and recovered Covid-19 cases and then tie them to high-frequency data for 31 stock markets.

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

Brazilian Review of Finance ◽

10.12660/rbfin.v4n1.2006.1155 ◽

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.

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A Comparative Study of VAR Measure Using Different Frequency Data of China’s Fuel Oil Futures Market

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.361-363.1887 ◽

2011 ◽

Vol 361-363 ◽

pp. 1887-1891

Author(s):

Feng Wang

Keyword(s):

High Frequency ◽

Value At Risk ◽

Low Frequency ◽

Futures Market ◽

Fuel Oil ◽

High Frequency Data ◽

Frequency Data ◽

Oil Futures ◽

Ultra High Frequency ◽

Fuel Oil Futures

By using datas of Chinese fuel oil futures market, this pater establishes VAR model based on low frequency, high frequency and ultra-high frequency data, to measure the value at risk, and compares the prediction accuracy of different frequency. The research results show that the high frequency and ultra-high frequency data have better accuracy in the VAR measuring, as they contain more intraday information and can reflect the futures market microstructure better.

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