Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH

Vortelinos, Dimitrios I.

doi:10.1016/j.ribaf.2015.01.004

Cited by 47 publications

(20 citation statements)

References 80 publications

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“…O artigo de Vortelinos (2017) aborda o seguinte problema demonstrando que os modelos lineares tenderam a apresentar melhores resultados em relação aos métodos não lineares estudados. O presente artigo busca verificar outros pontos não abordados como redes bayesianas e escolha do lag, além de avaliar o desempenho dos modelos quanto ao desempenho de trading.…”

Section: Considerações Finaisunclassified

Combinação da projeção da volatilidade percebida por redes neurais e HAR

2019

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<p>This paper examines a combination of HAR and neural networks methods to better predict perceived volatility and, consequently, to more efficiently manage risk. To carry out the projections, combinations and tests, the series of perceived volatility of Ibovespa was collected between 2000 and 2018, producing a sample of 4,530 observations. The main results show that the combination of both models better predict perceived volatility, which can be interpreted as an efficiency gain for risk management. In addition, this article also evaluates the performance of the models, considering the profitability of trading with options. For the case of profitability, combinations of linear and nonlinear models present better performance.</p>

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Section: Considerações Finaisunclassified

Combinação da projeção da volatilidade percebida por redes neurais e HAR

2019

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“…In addition to this, studies like Corra (2016) used Wavelet Auto-Regressive Integrated Moving Average with exogenous variables and Generalised Auto-Regressive Conditional heteroskedasticity (WARIMAX-GARCH) and ARIMA-GARCH method. While researchers like Vortelinos (2015) used HAR (Heterogeneous Auto-Regressive Model); Principal Components Combining; Neural networks; GARCH; Volatility studies in the study. GARCH models were used to test the efficiency of the markets (Narayan, 2016).…”

Section: Review Of Literaturementioning

confidence: 99%

Understanding volatility and leverage effects in bunker markets

Ranjan¹,

Raju

Jayaraj

et al. 2018

IJGEI

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The research analysed the volatility and the leverage effect in the bunker prices in IFO 380 and IFO 180 grades. Limited studies were found on bunker prices using EGARCH and bivariate analysis. The research investigated the causal relationships between IFO 380 and IFO 180 grades of bunkers. Serial correlations were found through using Lagrange multiplier (LM test) and heteroskedastic tests in the bunker price data. The analysis exhibited the model to be highly significant at 1%. Both the grades showed the short and long-run shocks and a positive leverage effect. The recommendations were made to the maritime industry to strategise during the long run and short-run shocks of bunker markets.

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“…Current literature emphasizes the potential of HAR modeling to deal with realized volatility forecasting with jumps against alternative approaches [23], [24], [25] and [26]. The papers examine whether nonlinear models, like principal components, neural networks, and GARCH are more accurate than HAR in realized volatility forecasting.…”

Section: A Realized Volatility With Jumps Modelsmentioning

confidence: 99%

Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps

Maciel

Ballini

Gomide

2017

IEEE Trans. Fuzzy Syst.

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Equity assets volatility modeling and forecasting provide key information for risk management, portfolio construction, financial decision making and derivatives pricing. Realized volatility models outperform autoregressive conditional heteroskedasticity and stochastic volatility models in out-ofsample forecasting. Gain in forecasting performance is achieved when models comprise volatility jump components. This paper suggests evolving possibilistic fuzzy modeling to forecast realized volatility with jumps. The modeling approach is based on an extension of the possibilistic fuzzy c-means clustering and on functional fuzzy rule-based models. It employs memberships and typicalities to recursively update cluster centers. The evolving nature of the model allows adding or removing clusters using statistical distance-like criteria to update the model as dictated by input data. The possibilistic model improves robustness to noisy data and outliers, an essential requirement in financial markets volatility modeling and forecasting. Computational experiments and statistical analysis are done using Value-at-Risk estimates to evaluate and to compare the performance of the evolving possibilistic fuzzy modeling with the Heterogeneous Autoregressive, neural networks models and current state of the art evolving fuzzy methods. The experiments use actual data from S&P 500 and Nasdaq (United States), FTSE (United Kingdom), DAX (Germany), IBEX (Spain) and Ibovespa (Brazil), major equity market indexes in global markets. The results show that the evolving possibilistic fuzzy model is highly efficient to model realized volatility with jumps in terms of forecasting accuracy.

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Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH

Cited by 47 publications

References 80 publications

Combinação da projeção da volatilidade percebida por redes neurais e HAR

Combinação da projeção da volatilidade percebida por redes neurais e HAR

Understanding volatility and leverage effects in bunker markets

Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps

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