Výsledky vyhledávání - "GARCH family models"

Akademický článek

Forecasting Volatility Spillovers Using Advanced GARCH Models: Empirical Evidence for Developed Stock Markets from Austria and USA

Autor: Bharat Kumar Meher, Puja Kumari, Ramona Birau, Cristi Spulbar, Abhishek Anand, Ion Florescu

Publikováno v: Annals of Dunarea de Jos University. Fascicle I : Economics and Applied Informatics, Vol 30, Iss 1, Pp 16-29 (2024)

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research emba

Externí odkaz: https://doaj.org/article/0c18956740c94692abd0e3ef9312ab43

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Akademický článek

Foreign Exchange Futures Trading and Spot Market Volatility in Thailand

Autor: Woradee Jongadsayakul

Publikováno v: Risks, Vol 12, Iss 7, p 107 (2024)

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trad

Externí odkaz: https://doaj.org/article/3eb44df0a47847439ce2278be11b388d

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Akademický článek

Assessing Volatility Patterns using GARCH Family Models: A Comparative Analysis Between the Developed Stock Markets in Italy and Poland

Autor: Cristi SPULBAR, Ramona BIRAU, Jatin TRIVEDI, Mircea Laurentiu SIMION, Rachana BAID

Publikováno v: Annals of Dunarea de Jos University. Fascicle I : Economics and Applied Informatics, Vol 29, Iss 1, Pp 5-11 (2023)

The main aim of this research paper is to conduct a comparative empirical study on the behavior of the stock markets in Italy and Poland. In this sense, it is examined the presence of volatility patterns using GARCH family models for the sample perio

Externí odkaz: https://doaj.org/article/6086393963e2424da10fb7c78fd837bc

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Akademický článek

Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies

Autor: Bahareh Amirshahi, Salim Lahmiri

Publikováno v: Machine Learning with Applications, Vol 12, Iss , Pp 100465- (2023)

The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis for crypto

Externí odkaz: https://doaj.org/article/302323c211364492bcb8cb66529be3bb

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Akademický článek

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Autor: Huibin Zeng, Bilin Shao, Genqing Bian, Hongbin Dai, Fangyu Zhou

Publikováno v: Energy Science & Engineering, Vol 10, Iss 7, Pp 1998-2021 (2022)

Abstract Natural gas load forecasting provides decision‐making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas loa

Externí odkaz: https://doaj.org/article/569eaa3cd8184acebb0c7bba276000ea

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Akademický článek

Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

Autor: Cristi Spulbar, Ramona Birau, Jatin Trivedi, Iqbal Thonse Hawaldar, Elena Loredana Minea

Publikováno v: Investment Management & Financial Innovations, Vol 19, Iss 1, Pp 262-273 (2022)

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from

Externí odkaz: https://doaj.org/article/b2cc932fb054447e82c4aacb336a9bfd

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