Results 141 to 150 of about 2,269 (199)

Modelling stock market data in China: Crisis and Coronavirus. [PDF]

Financ Res Lett, 2021
Cristofaro L, Gil-Alana LA, Chen Z, Wanke P.   +3 more
europepmc   +1 more source

Commonality in the LME aluminium and copper volatility processes through a Figarch lens [PDF]

We consider dynamic representation of spot and three month aluminium and copper volatilities. These are the two most important metals traded in the London Metal Exchange (LME).
Christopher L. Gilbert, Isabel Figuerola-Ferretti   +1 more
core  

What Exactly is "Bad News" in Foreign Exchange Markets? Evidence from Latin American Markets [PDF]

This paper asks whether the ‘leverage effect’ –as defined by Black (1976) for stock markets– is also a characteristic of foreign exchange markets. The study focuses on five Latin American emerging markets which have adopted a floating exchange regime ...
Cecilia Maya, Karoll Gómez
core  

Long Memory in LME Volatility through the ARFIMA and FIGARCH Model

Korean Journal of Financial Engineering, 2016
null Jaehwan Park, null 김현숙
openaire   +1 more source

Stock Market Volatility and Return Analysis: A Systematic Literature Review. [PDF]

Entropy (Basel), 2020
Bhowmik R, Wang S.
europepmc   +1 more source

The Use of Weather Variables in the Modeling of Demand for Electricity in One of the Regions in the Southern Poland [PDF]

The main objective of the paper is the verification of usefulness of the ARFIMA-FIGARCH class models in the description of tendencies in the energy consumption in a selected region of the southern Poland taking into consideration weather variables ...
Aneta Wlodarczyk, Marcin Zawada
core  

Market-crash forecasting based on the dynamics of the alpha-stable distribution. [PDF]

Physica A, 2020
Molina-Muñoz J, Mora-Valencia A, Perote J.   +2 more
europepmc   +1 more source

"Modelling Long Memory Volatility in Agricultural Commodity Futures Returns" [PDF]

This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton ...
Chia-Lin Chang, Michael McAleer, Roengchai Tansuchat   +2 more
core  

DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions. [PDF]

Quant Finance
Moreno-Pino F, Zohren S.
europepmc   +1 more source

Cointegration and causality relationship of Indian stock market with selected world markets. [PDF]

F1000Res, 2022
Ali F, Suri P, Kaur T, Bisht D.
europepmc   +1 more source