PERAMALAN DATA RUNTUN WAKTU DENGAN PEMODELAN ARFIMA-FIGARCH [PDF]
, 2009 Pemodelan Autoregressive Fractional Integrated Moving Average – Fractional Integrated Generalized Autoregressive Conditional Heteroskedastic (ARFIMA-FIGARCH) adalah salah satu model peramalan dimana data time series mempunyai memori jangka panjang dengan
Selvi Affriani, -
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Exchange rate volatility modeling: ARIMAX-FIGARCH with wavelet-based outlier detection for USD/PEN
Frontiers in Applied Mathematics and StatisticsIntroductionExchange rate volatility in partially dollarized emerging economies poses persistent challenges for financial risk management and monetary policy.
Wilfredo Bazán-Ramírez +6 more
doaj +1 more source
Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures [PDF]
In this paper we extend the univariate FIGARCH and FIAPARCH models to a bivariate framework. We estimate bivariate error correction FIGARCH and FIAPARCH models between the All Ordinaries Index and its SPI futures using constant correlation and diagonal ...
Jonathan Dark
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Volatility dynamics of stock returns, liquidity and exchange rates in ASEAN Countries [PDF]
AccountingIn this study, we examined the volatility trend of stock return in eight ASEAN stock markets. These includes the Singapore Exchange (SGX), Bursa Malaysia Stock Exchange (YSX), the Stock Exchange of Thailand (SET), Indonesia stock exchange, the Vietnam ...
David Umoru +3 more
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Estimating Value-at-Risk for the Turkish Stock Index Futures in the Presence of Long Memory Volatility [PDF]
This paper examines the long memory properties for closing prices of the Turkish stock index futures market using the FIGARCH(1,d,1) model with three different distributions : Normal, Student-t, and skewed Student-t.
Adnan Kasman
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Long memory of volatility measures in time series [PDF]
The authors analyse relations between the long memory parameter of conditional variance and estimates of the long memory in squared residuals in FIGARCH models. The investigations are performed by means of simulations FIGARCH(0, d, 0) and FIGARCH(1, d, 1)
Henryk Gurgul, Tomasz Wojtowicz
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Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach [PDF]
This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH , or A-FIGARCH , which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing
Claudio Morana, Richard T. Baillie
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Long memory behavior in the returns of Pakistan stock market: Arfima-Figarch models [PDF]
International Journal of Economics and Financial Issues, 2014 This study examines the weak-form market efficiency of Pakistan Stock Market namely Karachi Stock Exchange for the period 2010-2013. The efficiency of stock market has tested by using ARFIMA-FIGARCH models estimated under different distribution assumptions as Normal, Student-t, Skewed Student-t and GED distribution.
TURKYILMAZ, Serpil, BALIBEY, Mesut
openaire +2 more sources
Volatility Modeling and Spillover: The Turkish and Russian Stock Markets
Istanbul Business ResearchThis study investigates the internal and external (spillover) characteristics of the volatility of the Turkish and Russian stock market indices. To this end, generalized autoregressive conditional heteroskedasticity models that are classified as short ...
Ahmet Galip Gençyürek
doaj +1 more source
Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models
RisksModelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial.
Kisswell Basira +4 more
doaj +1 more source