Results 251 to 260 of about 507,052 (301)

Forecasting volatility

, 2007
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Enhancing Prediction by Incorporating Entropy Loss in Volatility Forecasting. [PDF]

Entropy (Basel)
Urniezius R, Petrauskas R, Vaitkus V, Karimov J, Brazauskas K, Repsyte J, Kacerauskiene E, Harms T, Dargiene J, Ezerskis D.   +9 more
europepmc   +1 more source

Deep learning-enabled cherry price forecasting and real-time system deployment across multi-market supply chains in India. [PDF]

Sci Rep
Shaheen FA, Gul A, Ganai N, Majid M, Rashid M, Sultan A.   +5 more
europepmc   +1 more source

Carbon price fluctuation forecasting using an adaptive dual-channel residual attention neural network optimized with white shark optimizer and blockchain-based data provenance. [PDF]

Sci Rep
Biswal S, Kotecha K, Munjal N.
europepmc   +1 more source

Enhancing the forecast accuracy of the daily number of patients arrivals in emergency department by hybrid ARIMAX-ANN algorithm. [PDF]

PLoS One
Tabesh H, AbbaszadehMozaffari A, Ebnehoseini Z, Saki A.   +3 more
europepmc   +1 more source

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Functional volatility forecasting

Journal of Forecasting, 2023
AbstractWidely used volatility forecasting methods are usually based on low‐frequency time series models. Although some of them employ high‐frequency observations, these intraday data are often summarized into low‐frequency point statistics, for example, daily realized measures, before being incorporated into a forecasting model. This paper contributes
Yingwen Tan, Zhensi Tan, Yinfen Tang, Zhiyuan Zhang   +3 more
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THE ROLE OF IMPLIED VOLATILITY IN VOLATILITY COMBINING FORECASTS

International Journal of Economics and Business Research, 2023
This study explores the role of implied volatility (IV) in volatility combining forecasts for S&P 500 and DAX markets. A range of GARCH models, ad hoc models and STES models were developed to identify the best performing model that served as a base model for subsequent combining process, of which GJRGARCH model appeared to be the superior model among ...
Ho, Jen Sim, Choo, Wei Chong, Boon, Shui Hooi, Wan, Cheong Kin, Zhang, Yuruixian   +4 more
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Forecasting volatility

Statistics & Probability Letters, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thavaneswaran, A., Appadoo, S. S., Peiris, S.   +2 more
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GARCH, Outliers, and Forecasting Volatility

2011
The issue of detecting and handling outliers in GARCH processes has received considerable attention recently. In this chapter, we put forwardan iterative outlier detection procedure, which is appropriate given that in practice both the number of outliers as well as their timing is unknown. Our procedure aims to test for the presence of a single outlier
Franses, Philip Hans, van Dijk, Dick
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Automated Volatility Forecasting

Management Science
We develop an automated system to forecast volatility by leveraging more than 100 features and five machine learning algorithms. Considering the universe of S&P 100 stocks, our system results in superior out-of-sample volatility forecasts compared with existing risk models across forecast horizons.
Sophia Zhengzi Li, Yushan Tang
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