Results 51 to 60 of about 2,269 (199)

Enhanced state-space estimation of long-memory commodity volatility using the Unscented Kalman Filter and variational Bayes method for non-linear modeling

Frontiers in Applied Mathematics and Statistics
This study addresses the limitations of the Kalman Filter (KF) by extending the application of the Unscented Kalman Filter (UKF) and the variational Bayes method (VBM) for estimating long-memory (LM) volatility models.
Kisswell Basira, Lawrence Dhliwayo, Florance Matarise   +2 more
doaj   +1 more source

On the integrated behaviour of non-stationary volatility in stock markets [PDF]

, 2006
This paper analyses the behaviour of volatility for several international stock market indexes, namely the SP 500 (USA), the Nikkei (Japan), the PSI 20 (Portugal), the CAC 40 (France), the DAX 30 (Germany), the FTSE 100 (UK), the IBEX 35 (Spain) and the ...
Andreia Dionisio, Baillie, Barton, Bollerslev, Bollerslev, Darrat, Diana A. Mendes, Engle, Engle, Engle, Johansen, Johansen, Lamoureux, Meddahi, Nelson, Phylaktis, Rui Menezes, Vilasuso   +17 more
core   +5 more sources

Does the Oil Market Volatility have Long Run Memory? [PDF]

Pizhūhishnāmah-i Iqtiṣād-i Inirzhī-i Īrān, 2011
This paper has examined the long memory of oil market volatility. For this purpose, the paper has employed different types of long run ARCH models including FIGARCH-BBM, FIGARCH-chung, FIEGARCH, FIAPARCH-BBM and FIAPARCH-chung and short run ones ...
Seed Rasekhi, Amir Khanalipour
doaj  

Market Consistent Valuation for Bitcoin Options With Long Memory in Conditional Volatility and Conditional Non‐Normality

Journal of Futures Markets, Volume 45, Issue 8, Page 917-945, August 2025.
ABSTRACT This paper investigates the economic consequences for Bitcoin options' prices of a long memory in conditional volatility and conditional non‐normality of Bitcoin returns. The arbitrage‐free prices of Bitcoin options are determined by market consistent valuation and the conditional Esscher transform. Monte Carlo estimates for option prices from
Tak Kuen Siu
wiley   +1 more source

Spatial and spatiotemporal volatility models: A review

Journal of Economic Surveys, Volume 39, Issue 3, Page 1037-1091, July 2025.
Abstract Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, in the case of positive spatial dependence, if two locations are in close proximity ...
Philipp Otto, Osman Doğan, Süleyman Taşpınar, Wolfgang Schmid, Anil K. Bera   +4 more
wiley   +1 more source

Memories of the Gold Foreign Exchange Market Based on a Moving V‐Statistic and Wavelet‐Based Multiresolution Analysis

Discrete Dynamics in Nature and Society, Volume 2018, Issue 1, 2018., 2018
Memory in finance is the foundation of a well‐established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving V‐statistic on the basis of a modified R/S
Peng Zheng, Bin Liu, Zhongli Zhou, Francisco R. Villatoro   +3 more
wiley   +1 more source

A new multivariate nonlinear model to handle the volatility transmission

South African Journal of Industrial Engineering, 2014
Price volatility of stocks is an important issue in stock markets. It should also be taken into account that the stochastic nature of volatility affects decision-makers’ minds to a great extent. Therefore, predicting price volatility could help them make
Ebrahimi, Seyed Babak, Rouhiolyaee, Fahime   +1 more
doaj   +1 more source

Modelling Volatility Cycles: The MF2‐GARCH Model

Journal of Applied Econometrics, Volume 40, Issue 4, Page 438-454, June/July 2025.
ABSTRACT We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors.
Christian Conrad, Robert F. Engle
wiley   +1 more source

Improving Volatility Risk Forecasting Accuracy in Industry Sector

International Journal of Mathematics and Mathematical Sciences, Volume 2017, Issue 1, 2017., 2017
Recently, the volatility of financial markets has contributed a necessary part to risk management. Volatility risk is characterized as the standard deviation of the constantly compound return per day. This paper presents forecasting of volatility for the Jordanian industry sector after the crisis in 2009.
S. Al Wadi, Niansheng Tang
wiley   +1 more source

Forecasting West Texas Intermediate Crude Oil Price: Stochastic Differential Approach [PDF]

مدلسازی اقتصادسنجی, 2018
Uncertainty in oil markets has led economic researchers to the use of stochastic processes. The purpose of this paper, is the use of stochastic differential models to predict the crude oil price of West Texas Intermediate (WTI) and compare the ...
ramin khochiani, younes nademi
doaj   +1 more source