Results 51 to 60 of about 13,792,513 (203)
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
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange [PDF]
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī, 2014 This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX ...
Mohammad Javad Mohagheghnia +3 more
doaj
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 +4 more
wiley +1 more source
Out-of-sample Forecasting Performance of Won/Dollar Exchange Rate Return Volatility Model
East Asian Economic Review, 2009 We compare the out-of-sample forecasting performance of volatility models using daily exchange rate for the KRW/USD during the period from 1992 to 2008.
Hojin Lee
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
Structural breaks and GARCH models of exchange rate volatility: Re‐examination and extension
Journal of Applied Econometrics, Volume 39, Issue 7, Page 1403-1407, November/December 2024.Summary We examine the empirical significance of structural changes concerning generalized autoregressive conditional heteroskedasticity (GARCH) models of exchange rate volatility using out‐of‐sample tests by replicating and carrying out robustness checks on the volatility forecasting study by Rapach and Strauss (Journal of Applied Econometrics, 2008 ...
Akram Shavkatovich Hasanov +3 more
wiley +1 more source
International Journal of Financial StudiesThe aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility
Apostolos Ampountolas
doaj +1 more source
East Asian Economic Review, 2007 This paper investigates the relevance of skewed Student-t distributions in capturing long memory volatility properties in the daily return series of Japanese financial data (Nikkei 225 Index and JPY-USD exchange rate).
Seong¡-Min Yoon , Sang-Hoon Kang
doaj +1 more source
Statistical modelling for forecasting volatility in potato prices using ARFIMA-FIGARCH model
The Indian Journal of Agricultural Sciences, 2018 This paper investigates the presence of long memory both in mean and volatility in the potato prices in Agra and Amritsar markets of India, using the Autoregressive fractionally integrated moving average (ARFIMA) and Fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models.
DIPANKAR MITRA +2 more
openaire +1 more source
Gestão & Produção, 2012 O presente estudo propõe uma análise comparativa de dez modelos de volatilidade para o cálculo do Value-at-Risk (VaR) para carteira teórica do Ibovespa, considerando a presença de memória longa na série temporal dos seus retornos diários.
Luiz Eduardo Gaio +1 more
doaj +1 more source