Forecasting stock market volatilities using a novel two-stage hybrid model by integrating the asymmetric CARR with LSTM models

Forecasting stock market volatilities using a novel two-stage hybrid model by integrating the asymmetric CARR with LSTM models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 February 2025 V1 Latest version Share on Forecasting stock market volatilities using a novel two-stage hybrid model by integrating the asymmetric CARR with LSTM models Authors : Lei Chen 0009-0002-7752-5254 , You Beng Koh 0000-0002-8468-0757 [email protected] , and Kok Haur Ng 0000-0002-8763-7586 Authors Info & Affiliations https://doi.org/10.22541/au.174046893.33479873/v1 331 views 174 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper proposes a novel two-stage hybrid model that integrates the asymmetric conditional autoregressive range (AsyCARR) with long short-term memory (LSTM) models to forecast volatility. In the first stage, the Parkinson (PK) volatility estimates are fitted using the AsyCARR model to obtain the predicted volatilities. The residuals, defined as the deviations between the average lagged of observed PK estimates and the predicted volatilities, are obtained. In the second stage, the LSTM model is employed to train and forecast the residuals. The ultimate forecasted volatilities are obtained by combining the predicted residuals with the average lagged of PK. The main feature of the two-stage hybrid model lies in its ability to capture the asymmetric response of shocks to the conditional expectation of PK volatility while effectively handling the nonlinear residuals components. Moreover, this study investigates the feasibility of incorporating five distinct forms of residuals as an input variable respectively into the LSTM model to enhance forecasting accuracy. Empirical analyses demonstrate that this family of hybrid AsyCARR-LSTM models delivers outstanding performance in both in-sample fitting and out-of-sample forecasting across a comprehensive set of loss functions. Furthermore, the superiority of the hybrid AsyCARR-LSTM model in out-of-sample forecasting is validated through Hansen’s model confidence set test, based on heteroskedastic mean squared error, which confirms its statistical advantage over the CARR and AsyCARR models. Lastly, the findings indicate that the forecasting performance of hybrid models are influenced by the choice of residual forms. Supplementary Material File (full manuscript reformat.docx) Download 385.73 KB Information & Authors Information Version history V1 Version 1 25 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords hybrid model lstm parkinson range-based volatility volatility forecasted Authors Affiliations Lei Chen 0009-0002-7752-5254 Universiti Malaya View all articles by this author You Beng Koh 0000-0002-8468-0757 [email protected] Universiti Malaya View all articles by this author Kok Haur Ng 0000-0002-8763-7586 Universiti Malaya View all articles by this author Metrics & Citations Metrics Article Usage 331 views 174 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lei Chen, You Beng Koh, Kok Haur Ng. Forecasting stock market volatilities using a novel two-stage hybrid model by integrating the asymmetric CARR with LSTM models. Authorea . 25 February 2025. DOI: https://doi.org/10.22541/au.174046893.33479873/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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