Forecasting volatility with outliers in GARCH models - Charles - 2008Charles, A. (2008). Forecasting volatility with outliers in GARCH models. Journal of Forecasting, 27, 551-565.Charles A. (2008). Forecasting volatility with outliers in GARCH models. Journal of Forecasting, 27, 551-565....
One class of models which have proved successful in forecasting volatility in many situations is the GARCH family of models. The objective of the present study is to analyze systematic mispricing of options derivatives. In order to perform the analysis, data was collected for a sample of NSE-...
This article examines the volatility forecasting abilities of three approaches: GARCH-type model that uses carbon futures prices, an implied volatility from carbon options prices, and the k-nearest neighbor model. Based on the results, we document that GARCH-type models perform better than an implie...
Forecasting China′s Stock Market Volatility Using Non-Linear GARCH Models Forecasting Volatility of Emerging Stock Markets: Linear versus Non-linear GARCH Models - Gokcan - 2000Franses P. H. - Van Dijk D. (1996), ... WEI Ei - 《Journal of Systems Science and Systems Engineering》 被引量...
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In this section, we investigate the out-of-sample forecast performance of the CARR-MIDAS model in forecasting the renminbi exchange rate volatility. We compare the performance of the range-based CARR-MIDAS model with that of the two popular return-based volatility models: the GARCH model of Boll...
Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility. Keywords: volatility; Bitcoin; machine learning; GARCH; recurrent neural networks1. Introduction The rapid development of technology has spurred changes in ...
448453 Forecasting China′Stock Market Volatility Using s Non-Linear GARCH Models W EI Wei-xian I nstitute of Finance , X iamen Univ ersity , X iam en 361005, China Abstract: T his paper s tu dies th e perf ormance of t h e G A RC H model and t w o of it s non-linear ...
Forecasting Volatility with Markov‐Switching GARCH Models―Comparison of Models Using Realized Volatility― In this paper we compare a set of different standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the petroleum futures markets...
The estimates of the GARCH-PARK-R model are derived using the Quasi-Maximum Likelihood Estimation (QMLE). The results suggest that the GARCH-PARK-R model is a good middle ground between intra-daily models, such as the Realized Volatility and inter-daily models, such as the ARCH class. The...