Affiliation:
1. Firat University, Turkey
Abstract
This chapter delves into market volatility and its forecasting models in the dynamic financial landscape. It examines factors driving volatility, quantification approaches, and diverse models. From traditional to advanced models and deep learning techniques like RNNs, LSTMs, BiLSTMs, and GRUs, it enriches our understanding of market dynamics. These models are vital for risk management, strategic investment, and informed decisions, offering insights into volatile asset price fluctuations. By embracing data-driven solutions and predictive analytics, the authors navigate market unpredictability, led by models serving as custodians of comprehension and stability, guiding towards enlightened, strategic, and prosperous financial decisions.
Reference80 articles.
1. Adlığ, G. Ş. (2009). Finansal piyasalarda ardışık bağlanımlı koşullu varyans etkileri, oynaklık tahmini ve Türkiye üzerine bir uygulama [Conditional variance effects in financial markets, volatility forecasting and an application on Turkey] [Unpublished master thesis]. Istanbul University Institute of Social Sciences, Istanbul.
2. Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model
3. The leverage effect puzzle: Disentangling sources of bias at high frequency
4. Borsa İstanbul ve Küresel Piyasa Göstergeleri Arasındaki Volatilite Etkileşiminin DCC-GARCH Yöntemi İle Analizi
5. AkelV. (2011). Kriz dönemlerinde finansal piyasalar arasındaki volatilite yayılma etkisi [Volatility spillover effect across financial markets during crisis periods] (1st ed.). Detay Publishing.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献