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5.116  Articles
1 of 513 pages  |  10  records  |  more records»
The purpose of this paper is to model and forecast volatility of returns for corn futures prices using GARCH models. Non-linear models from the GARCH family, specifically TGARCH and EGARCH are employed to assess the role of asymmetries and to analyze the ... see more

The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptronand an ARCH model to predict the monthly conditional variance of stock prices.The results sho... see more

We examine the ability of three different GARCH-class models, with four innovation distributions, to capture the volatility properties of natural gas futures contracts traded on the New York Mercantile Exchange. We jointly estimate the long-memory process... see more

Volatility forecasting has been of great interest both in academic and professional fields all over the world. However, there is no agreement about the best model to estimatevolatility. New models include measures of skewness, changes of regimes and diffe... see more

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns st... see more

The stock market is continuously changing with uncertainties that can create risks. Prompt information dissemination and rapid capital flow will cause stock price fluctuations, causing volatility in stock prices. This research examines the behavior of vol... see more

This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock indice... see more

In this paper, we compare semiparametric additive models with GARCH models in terms of their capability to estimate and forecast volatility during crisis periods. Our Monte Carlo studies indicate a better performance for GARCH models when their functional... see more

Forecasting stock market returns volatility is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibo... see more

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