Results 11 to 20 of about 318 (87)
Volatility Forecasts Jakarta Composite Index (JCI) and Index Stock Volatility Sector with Estimated Time Series [PDF]
Indonesian Capital Market Review, 2020 This study aims to explore the comparative ability of forecasting models and the time series volatility of capital markets in Indonesia using JCI daily index data and sectoral indices from January 2010 to December 2014. The use of ARCH-family ARCH model (
Bahtiar, Muhammad Rifki
exaly +3 more sources
COVID-19's disasters are perilous than Global Financial Crisis: A rumor or fact? [PDF]
Financ Res Lett, 2020 Shehzad K, Xiaoxing L, Kazouz H.
europepmc +2 more sources
Can black swans be tamed with a flexible mean‐variance specification?
International Journal of Finance &Economics, Volume 27, Issue 3, Page 3202-3227, July 2022., 2022 Abstract We examine the homogeneity of the highly improbable returns, what practitioners and the mainstream economic press also call black swan events. By setting up a simple framework and using the benchmark stock market indices of all OECD countries, we find that the frequency of black swans varies greatly over the last two decades often with ...
Vasiliki Chatzikonstanti +1 more
wiley +1 more source
Attention‐based novel neural network for mixed frequency data
CAAI Transactions on Intelligence Technology, Volume 6, Issue 3, Page 301-311, September 2021., 2021 Abstract It is a common fact that data (features, characteristics or variables) are collected at different sampling frequencies in some fields such as economic and industry. The existing methods usually either ignore the difference from the different sampling frequencies or hardly take notice of the inherent temporal characteristics in mixed frequency ...
Xiangpeng Li +3 more
wiley +1 more source
Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2019 Bu çalışmada, Borsa İstanbul (BIST) Mali (XUMAL)ve Sınai (XUSIN) Endekslerinin 07.01.2007-03.02.2019 dönemine ilişkin haftalıklogaritmik getirileri ele alınarak volatilite tahminlemesi yapılmasıamaçlanmıştır.
İlhan Ege, Tuğba Nur Topaloğlu
doaj +1 more source
Volatility in high-frequency intensive care mortality time series: application of univariate and multivariate GARCH models [PDF]
, 2017 Mortality time series display time-varying volatility. The utility of statistical estimators from the financial time-series paradigm, which account for this characteristic, has not been addressed for high-frequency mortality series.
Moran, J., Solomon, P.
core +1 more source
GENERALIZED ASYMMETRIC POWER ARCH MODELING OF NATIONAL STOCK MARKET RETURNS
Sosyal Ekonomik Araştırmalar Dergisi, 2009 Uygulamalı çalışmalar finansal varlık getirilerinin şişman kuyruk (leptokurtosis) özelliği sergilediklerini ve genellikle oynaklık kümelenmesi ve asimetrik yapı ile nitelendirildiklerini göstermiştir.
Mert Ural
doaj +4 more sources
Discrete Dynamics in Nature and Society, Volume 2019, Issue 1, 2019., 2019 This article aims to investigate the Value at Risk of basis for stock index futures hedging in China. Since the RS‐GARCH model can effectively describe the state transition of variance in VaR and the two‐state Markov process can significantly reduce the dimension, this paper constructs the parameter and semiparametric RS‐GARCH models based on two‐state
Liang Wang +4 more
wiley +1 more source
Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency [PDF]
, 2010 In this paper we model the adjustment process of European Union Allowance (EUA) prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence.
Conrad, Christian +2 more
core +4 more sources
Low‐Frequency Volatility in China’s Gold Futures Market and Its Macroeconomic Determinants
Mathematical Problems in Engineering, Volume 2015, Issue 1, 2015., 2015 We extract low‐ and high‐frequency volatility from China’s Shanghai gold futures market using an asymmetric Spline‐GARCH (ASP‐GARCH) model. We then regress monthly low‐frequency volatility on selected monthly macroeconomic indicators to study the impact of macroeconomy on gold futures market and to test for excess volatility.
Song Liu +4 more
wiley +1 more source