Results 61 to 70 of about 318 (87)

Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models [PDF]

, 2012
Melike Bildirici, Özgür Ömer Ersin
openalex  

Univariate GARCH models: a survey (in Russian) [PDF]

This article presents a survey of the developments of univariate GARCH models. ARCH, GARCH, EGARCH and other possible nonlinear extensions are examined. Conditions for stationarity (weak and strong) are presented.
Eduardo Rossi
core  

An empirical evaluation of fuzzy bidirectional long short-term memory with soft computing based decision-making model for predicting volatility of cryptocurrencies. [PDF]

Sci Rep
Ragab M.
europepmc   +1 more source

Source tracing and contagion measurement of carbon emission trading price fluctuation in China from the perspective of major emergencies. [PDF]

PLoS One
Wu B, Wang H, Xie B, Xie Z.
europepmc   +1 more source

CDS Getirilerinin APGARCH Modellemesi

, 2015
Mert Ural, Erhan Demi̇reli̇
openalex   +1 more source

Stata 10 (Time Series and Forecasting) [PDF]

Robert Alan Yaffee
core   +1 more source

日経225コール・プットオプションの市場価格と理論価格の乖離の実証分析 [PDF]

, 2011
Kano Satoru, Takeuchi-Nogimori Asuka
core   +1 more source

A Measuring Approach of Portfolio's VaR Based on APGARCH-EWMA Model

2010 Third International Symposium on Information Processing, 2010
Value at Risk (VaR) is a commonly statistical tool to measure market risk. In this paper, a mixture method of APGARCH-M model and EWMA algorithm is applied to measure VaR of a portfolio. Empirical study using three stock index of shanghai stock market shows the mixture method is advantageous and accurate to calculate VaR of a portfolio.
Ping Wang
exaly   +3 more sources

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Using apgarch/avgarch models Gaussian and non-Gaussian for modeling volatility exchange rate

2022
Abdulla, Suhail, Dhaher Alwan, Heba
openaire   +1 more source

Modeling the symmetric relation between Baltic Exchange indexes

Maritime Business Review, 2023
Kasra Pourkermani
exaly