Results 251 to 260 of about 583,999 (299)

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +3 more sources

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
openaire   +1 more source

Volatility Processes and Volatility Forecast with Long Memory

Quantitative Finance, 2002
We introduce a new family of processes that include the long memory (power law) in the volatility correlation. This is achieved by measuring the historical volatility on a set of increasing time horizons and by computing the resulting effective volatility by a sum with power law weights.
openaire   +1 more source

Sentiment-aware volatility forecasting

Knowledge-Based Systems, 2019
Abstract Recent advances in the integration of deep recurrent neural networks and statistical inferences have paved new avenues for joint modeling of moments of random variables, which is highly useful for signal processing, time series analysis, and financial forecasting.
Frank Z. Xing, Erik Cambria, Yue Zhang 0004   +2 more
openaire   +2 more sources

Neural network volatility forecasts

Intelligent Systems in Accounting, Finance and Management, 2007
AbstractWe analyse whether the use of neural networks can improve ‘traditional’ volatility forecasts from time‐series models, as well as implied volatilities obtained from options on futures on the Spanish stock market index, the IBEX‐35.One of our main contributions is to explore the predictive ability of neural networks that incorporate both implied ...
José R. Aragonés, Carlos Blanco 0003, Pablo García Estévez   +2 more
openaire   +2 more sources

Practical Issues in Forecasting Volatility

CFA Digest, 2005
A comparison is presented of 93 studies that conducted tests of volatility-forecasting methods on a wide range of financial asset returns. The survey found that option-implied volatility provides more accurate forecasts than time-series models.
Poon, Ser Huang; id_orcid 0000-0002-7297-9401, Granger, Clive   +1 more
openaire   +1 more source