Volatility of Some Selected Currencies Against the Naira Using Generalized Autoregressive Score Models

Babatunde, Oluwagbenga T.; Oranye, Henrietta Ebele; Nwafor, Cynthia Ndidiamaka

doi:10.11648/j.ijsd.20200603.11

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Among the empirical studies, Erer and Erer (2018) estimated the volatility of the BIST 100 and Dow Jones Indexes by using the GAS model to obtain time-varying dynamic conditional variance. Babatunde et al (2020) used the GAS model with its variants for estimating the volatility of the US/Naira, Pound sterling/Naira and Euro/Naira exchange rates, with GAS-T, EGAS-T, and EGAS-SKT being selected as the best model, respectively. Lazar and Xue (2020) compared the GARCH model with the GAS model by employing intraday data the S&P 500, Dow Jones Industrial Average, Nikkei 225, and FTSE 100.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of News Related Covid-19 on Exchange Rate Volatility: A New Evidence From Generalized Autoregressive Score Model

Erer¹

2023

ekoist

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The COVID-19 pandemic causes serious problems for the economy. When considering the significant impact the COVID-19 pandemic had on capital flows and global trade, it can be stated that the outbreak of this virus has caused sharp fluctuations in exchange rate markets. From this point of view, this study examines the effect of the news regarding the COVID-19 pandemic on exchange rate volatility for 12 emerging and developed countries that were most affected by the outbreak. The data covers the period between January 1, 2019 and August 31, 2022. For this purpose, we use the Generalized Autoregressive Score (GAS) model with student-t distribution, which is a new approach to measure the volatility of a financial series and to obtain the volatility clustering and fat-tail properties of a financial series. The findings of this study show that panic and fake news about the COVID-19 pandemic has increased the volatilites of exchange rates, while media hype news decreases the volatilities. These results indicate that the negative and speculative news regarding COVID-19 adversely affects exchange rate volatility through increasing the uncertainty of financial markets.

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Section: Literature Reviewmentioning

confidence: 99%

The Impact of News Related Covid-19 on Exchange Rate Volatility: A New Evidence From Generalized Autoregressive Score Model

Erer¹

2023

ekoist

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“…On the other hand, Babatunde et al (2020) investigates the volatility of exchange rates in Nigeria and the authors uses the United States (U.S) dollar, Pound Sterling, and Euro against the Naira. Variants of the GAS model are applied by these authors and studies exchange rate volatility by assuming three different probability distributions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In financial markets, risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of this probability distribution Babatunde et al (2020). There are various types of measures seeking to estimate risk and uncertainty: (1) realized and derivatives-implied distributions of returns across assets, (2) news-based measures of policy and political uncertainty, (3) survey-based indicators, (4) econometric measures, and (5) ambiguity indices.…”

Section: Introductionmentioning

confidence: 99%

Bootstrapping Time-Varying Uncertainty Intervals for Extreme Daily Return Periods

Makatjane

2022

IJFS

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This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and quantile of a continuously ranked probability score, are developed. Developed backtesting procedures revealed that an estimated Seasonal autoregressive integrated moving average-generalized autoregressive score-generalized extreme value distribution (SARIMA–GAS–GEVD) with a skewed student-t distribution had the best prediction performance in forecasting and bootstrapping VaR and ES. Extension of this non-stationary distribution in literature is quite complicated since it requires specifications not only on how the usual Bayesian parameters change over time but also those with bulk distribution components. This implies that the combination of a stochastic econometric model with extreme value theory (EVT) procedures provides a robust basis necessary for the statistical backtesting and bootstrapping density predictions for VaR and ES.

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“…Therefore, we urgently need an innovative method to generate simulated user characteristics data to make up for the deficiency of real data. General methods of data generation model include: (1) simulation sampling, represented by probability distribution model; (2) approximation methods, including random walk model [4] , autoregressive model [5] and variational autoencoder [6] ; (3) Implicit method, the representative model is generative adversarial network (GAN) [7] . This paper will use genetic algorithm to distinguish from the above three categories, which is an optimization method that mimics the process of natural selection and evolution [8] .…”

Section: Introductionmentioning

confidence: 99%

Library User Feature Data Generation Based on Genetic Algorithm

2024

AJCIS

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With the advent of the era of big data, libraries are faced with the need to make more use of user characteristic data to provide personalized services. However, due to the difficulty of acquiring real user characteristics data, it is difficult to carry out effective analysis and application. This paper proposes a genetic algorithm-based approach to generate simulated user feature data to enhance library service quality. Through genetic algorithms, we can simulate users' borrowing behavior, search behavior and participation behavior. The experimental results show that the generated simulated data is basically consistent with the real data in the distribution of behavior characteristics, which can provide accurate user characteristics data for the library, so as to better meet the needs of users and provide personalized services.

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Volatility of Some Selected Currencies Against the Naira Using Generalized Autoregressive Score Models

Cited by 4 publications

References 13 publications

The Impact of News Related Covid-19 on Exchange Rate Volatility: A New Evidence From Generalized Autoregressive Score Model

The Impact of News Related Covid-19 on Exchange Rate Volatility: A New Evidence From Generalized Autoregressive Score Model

Bootstrapping Time-Varying Uncertainty Intervals for Extreme Daily Return Periods

Library User Feature Data Generation Based on Genetic Algorithm

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