A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

Leandro Maciel

Chapter 11 in Advances in Financial Risk Management, 2013, pp 253-283 from Palgrave Macmillan

Abstract: Abstract Accurately measuring and forecasting stock market volatility plays a crucial role for asset and derivative pricing, hedge strategies, portfolio allocation and risk management. Since the 1987 stock market crash, academics, practitioners and regulators have investigated the development of financial time series models with changing variance over time in order to avoid huge investment losses due to their exposure to unexpected market movements (Allen and Morzuch, 2006, Carvalho et al., 2005, Lin et al., 2012). Indeed, volatility, as a measure of financial security prices fluctuation around its expected value, is one of the primary inputs in decision making processes under uncertainty, justifying its growing interest in the financial and economic literature (Kapetanios et al., 2006, Lux and Kaizoji, 2007).

Keywords: Particle Swarm Optimization; Stock Market; Differential Evolution; Particle Swarm Optimization Algorithm; Differential Evolution Algorithm (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palchp:978-1-137-02509-8_11

Ordering information: This item can be ordered from
http://www.palgrave.com/9781137025098

DOI: 10.1057/9781137025098_11

Access Statistics for this chapter

More chapters in Palgrave Macmillan Books from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().