Modeling Asymmetric Effects and Long Memory in Conditional Volatility of Dhaka Stock Exchange: New Evidence from Family of FIGARCH Models

Haque, Muhammad Enamul; Farzana, Nusrat

doi:10.13189/ujaf.2021.090520

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…We would like to provide a discussion about the possible reason. It is commonly observed in literature that the return and volatility in financial markets often exhibit considerable long-memory phenomenon [49][50][51][52][53]. As LSTM network contains memory cells (shown in Fig 3) that should be promising to 'memorize' some longterm features of the data [52]; while ANN is just a simple memory-free network.…”

Section: Plos Onementioning

confidence: 99%

Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach

Chen

2022

PLoS ONE

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This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. Inspired by the recent advancement of deep learning methodology, six models that combine a variety of artificial intelligence techniques are compared, including ANN, ANN(PCA), ANN(AE), LSTM, LSTM(PCA), and LSTM(AE). That is, in the design and comparison of the proposed AI models, we consider the combination of two dimensionality reduction methods (PCA and AE) and two typical neural networks (ANN and LSTM) in processing time series data. Besides, to further assess the prediction performance of the proposed models, two widely-applied statistical models (i.e. AR and EGARCH) on volatility prediction are used as benchmarks. In the empirical study, we collect financial trading data in both China and the US, and compare the performances of different models in predicting 5 days and 10 days ahead volatilities of stock index futures. In all, our analysis supports the use of LSTM(PCA) model to tackle those irregular and complex datasets.

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Section: Plos Onementioning

confidence: 99%

Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach

Chen

2022

PLoS ONE

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Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models

Khumalo,

Mashele,

Seitshiro

2023

DSFE

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<abstract><p>The South African financial market is developing with periods of high and low volatility. Employing an adequate volatility model is essential to manage market risk. This research study was designed to investigate the effectiveness of the fractionally integrated asymmetric power autoregressive conditional heteroskedasticity contrasted with long-memory GARCH-type models, such as the fractionally integrated generalized autoregressive conditional heteroskedasticity and the hyperbolic generalized autoregressive conditional heteroskedasticity for producing the measure of market risk known as the value at risk. These long-memory GARCH-type models assume that the distributions of the index returns follow normal, student-$ t $, skewed student-$ t $ and generalized error distributions. The historical closing price time series of the Johannesburg Stock Exchange all share, the mining and the banking indices are considered. The value at risk and its backtesting for short and long trading positions on the different confident levels are computed and they correspond to the right and left quantiles of the return distributions, respectively. The results reveal that FIAPARCH with a standard student-$ t $ distribution is an appropriate model for producing a robust value at risk in the context of mining and banking indices. Alternatively, FIGARCH with the assumed skewed student-$ t $ distribution model is a good fit to produce a value at risk for the Johannesburg Stock Exchange All Share Index.</p></abstract>

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Modeling Asymmetric Effects and Long Memory in Conditional Volatility of Dhaka Stock Exchange: New Evidence from Family of FIGARCH Models

Cited by 2 publications

References 34 publications

Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach

Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach

Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models

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