Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach DOI
Sudarshan Kumar, Sobhesh Kumar Agarwalla, Jayanth R. Varma

et al.

Journal of Futures Markets, Journal Year: 2023, Volume and Issue: 43(11), P. 1615 - 1644

Published: July 14, 2023

Abstract While there is a large literature on modeling volatility smile in options markets, most such studies are eventually focused the forecasting performance of model parameters and not applicability models trading environment. Drawing analogy like term structure context interest rates fixed‐income we evaluate Dynamic Nelson–Siegel (DNS) approach to dynamics environment against competing alternatives. Using model‐based mispricing as our sorting criterion, deploying strategy going long upper deciles short lower deciles, show that dynamic consistently outperform their static counterparts, with worst outperforming best terms percentage mean returns from portfolios Sharpe ratio. Specifically, find DNS outperforms all other specifications selected criteria.

Language: Английский

Macro and micro factors on global copper pricing: a historical data analysis DOI

Ernanto Ernanto,

Sudarso Kaderi Wiryono, Taufik Faturohman

et al.

Mineral Economics, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

Citations

0

High inflation during Russia–Ukraine war and financial market interaction: Evidence from C-Vine Copula and SETAR models DOI
Taher Hamza, Hayet Ben Haj Hamida, Mehdi Mili

et al.

Research in International Business and Finance, Journal Year: 2024, Volume and Issue: 70, P. 102384 - 102384

Published: May 6, 2024

Language: Английский

Citations

3

Artificial intelligence-driven financial innovation: A robo-advisor system for robust returns across diversified markets DOI
Qing Zhu, Chenyu Han,

Shan Liu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126881 - 126881

Published: Feb. 1, 2025

Language: Английский

Citations

0

Crude oil Price forecasting: Leveraging machine learning for global economic stability DOI

Amar Rao,

Gagan Deep Sharma, Aviral Kumar Tiwari

et al.

Technological Forecasting and Social Change, Journal Year: 2025, Volume and Issue: 216, P. 124133 - 124133

Published: April 12, 2025

Language: Английский

Citations

0

Relevance of hybrid artificial intelligence for improving the forecasting accuracy of natural resource prices DOI Creative Commons
Mei Li, Rida Waheed, Derviş Kırıkkaleli

et al.

Geoscience Frontiers, Journal Year: 2023, Volume and Issue: 15(3), P. 101670 - 101670

Published: July 8, 2023

The prediction performance of traditional forecasting methods is low due to the high level complexity in a series energy prices. present study attempts compare regression, machine learning tools and hybrid models conclude outperforming model. first step propose effective denoising technique for Tadawul index, which has confirmed superiority CSD based denoising. However, we use CSD-ARIMA, CSD-ANN, CSD-RNN as models. As result, outperforms both other terms MSE, MAPE, RMSE Dstat. findings are useful policy makers, investors portfolio managers forecast trends, hedge risk accordingly.

Language: Английский

Citations

6

Forecasting Crude Oil Prices: A Machine Learning Perspective DOI
Sourav Kumar Purohit, Sibarama Panigrahi

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 15 - 26

Published: Jan. 1, 2024

Language: Английский

Citations

2

Sustainable Development Through Energy Transition: The Role of Natural Resources and Gross Fixed Capital in China DOI Open Access
Yu Kang

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 83 - 83

Published: Dec. 26, 2024

Governments and politicians are very concerned about the environmental sustainability of energy sector, particularly with regard to oil gas. To assist in achieving global climate objectives, clean transition involves moving away from a fossil-fuel-based economy toward one that is dominated by clean, renewable energy. This reduces carbon emissions. Here, we consider moderating effects natural resources, urbanization, consumption between 1990 2022 as analyze impact China’s external balance goods services on country’s quality. We used ARDL econometric techniques present thorough empirical investigation. Overall, findings indicate ecological footprint adversely correlated transition, resource use, services, usage. Urbanization use non-renewable energy, however, positively associated footprint. The sources, richness all contribute sustainability. environment weakened urbanization It recommended policymakers facilitate acceleration utilizing promoting policies create favorable conditions for widespread adoption renewables, balancing nation’s urban structure way enhances self-sufficient development ensures sustainable future. Limitations this study future directions research outlined.

Language: Английский

Citations

1

Quantile on Quantile Analysis of Natural resources-growth and geopolitical risk trilemma DOI

Yan Kong,

Chuntong Dong, Yingyu Zhang

et al.

Resources Policy, Journal Year: 2023, Volume and Issue: 85, P. 103935 - 103935

Published: July 18, 2023

Language: Английский

Citations

3

Appropriateness of Reduced Modified Three-Parameter Weibull Distribution Function for Predicting Gold Production in Ghana DOI Open Access

Samuel Kwaku Obeng,

Christiana C. Nyarko,

Lewis Brew

et al.

Open Journal of Statistics, Journal Year: 2023, Volume and Issue: 13(04), P. 534 - 567

Published: Jan. 1, 2023

Forecasting mine production is pertinent to gold mining as it serves goals for investors. It therefore important identify the exact distribution that a response variable naturally follows. even more appropriate have model(s) with few predictor variables. This paper seeks statistical functions fitting in Ghana. The empirical relied mainly on quarterly secondary datasets between years 2009 and 2022 secured from Minerals Commission of Ghana, Accra. Several known distributions including Weibull, Log-Normal, Generalized Extreme Value (GEV) were explored Maximum Likelihood Estimation (MLE) evaluated using model selection criteria AIC, AICc BIC. Goodness Fits Kolmogorov-Smirnov Test (K-S), Cramer-Von Mises Statistic Anderson-Darling Statistic. Based analysis conducted, reduced modified 3-parameter Weibull provided best fit Though function proposed, however recognize other external factors can influence levels. Also, average fitted 1000334.8918 ± 75,327.080 (±7.5%) [i.e., 925,007.812 – 1,075,661.972]. indicates annually lies 3700031.248 4302647.888 ounces at 99.9% confidence level. Therefore, predicted year 3.7million

Language: Английский

Citations

2

An integration of oil price volatility, green energy consumption, and economic performance: assessing the mediating role of trade DOI
Ting Dai,

Mengchen Yu

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(26), P. 68792 - 68808

Published: May 2, 2023

Language: Английский

Citations

1