Analysis of shares frequency components on daily value-at-risk in emerging and developed markets

Biage, Milton

doi:10.1016/j.physa.2019.121798

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…where 0 < d < 1 is known as the fractional differencing parameters. The FIGARCH model has been widely used thanks to its ability to capture the volatility's persistence and integrate it into its predictions (Cochran, Mansur, and Odusami 2012;Biage 2019). Threshold ARCH (TARCH) models (Rabemananjara and Zakoian 1993) are also used for benchmarking purposes.…”

Section: Baseline Modelsmentioning

confidence: 99%

Deepvol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Moreno-Pino

Zohren

2022

SSRN Journal

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Section: Baseline Modelsmentioning

confidence: 99%

Deepvol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Moreno-Pino

Zohren

2022

SSRN Journal

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“…It has been considered a better way to measure extreme value risk in financial markets, and a number of previous studies have used it to analyze financial markets, including Longin and Solnik [11], Longin and Pagliardi [12], Liu et al [13], and Sobreira and Louro [14]. For empirical analysis, previous studies generally adopt a Value-at-Risk (VaR) or conditional Value-at-Risk (CoVaR) to better estimate extreme value risk in financial markets [15][16][17]. Since market prices are affected by many factors, the introduction of these factors could result in a more comprehensive exploration of the degree of extreme value risk in the markets, caused by these risk factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets

Qiu

Yang

et al. 2020

Sustainability

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In this study, we analyze the risk of extreme value dependence in Chinese regional carbon emission markets. After filtering the daily return data of six carbon markets in China using a generalized autoregressive conditional heteroscedasticity (GARCH) model, we obtain the standardized residual series. Next, the dependence structures in the markets are captured by the Copula function and the Extreme Value theory (EVT). We report high peaks, heavy tails and fluctuation aggregation in the logarithm return series of the markets, as well as significant dependent structures. There are significant extreme value risks in Chinese regional carbon markets, but the risks can be mitigated through appropriate portfolio diversification.

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“…Among them, multiscale models such as the empirical mode decomposition (EMD) are the latest development and have attracted increasing levels of attention in the risk measurement field. For example, Biage [22] used the wavelet analysis to model the stock volatility at different frequency scales and construct more accurate VaR estimates. Cifter [23] proposed a wavelet-based extreme value theory to estimate more accurate VaR.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

Zou

2022

Mathematics

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In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy.

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Analysis of shares frequency components on daily value-at-risk in emerging and developed markets

Cited by 8 publications

References 32 publications

Deepvol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Deepvol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets

Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

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