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

и другие.

Journal of Futures Markets, Год журнала: 2023, Номер 43(11), С. 1615 - 1644

Опубликована: Июль 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.

Язык: Английский

An explainable deep learning approach for stock market trend prediction DOI Creative Commons
Dost Muhammad, Iftikhar Ahmed, Khwaja Naveed

и другие.

Heliyon, Год журнала: 2024, Номер 10(21), С. e40095 - e40095

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

4

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

Shan Liu

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126881 - 126881

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

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

Ernanto Ernanto,

Sudarso Kaderi Wiryono, Taufik Faturohman

и другие.

Mineral Economics, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0

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

Amar Rao,

Gagan Deep Sharma, Aviral Kumar Tiwari

и другие.

Technological Forecasting and Social Change, Год журнала: 2025, Номер 216, С. 124133 - 124133

Опубликована: Апрель 12, 2025

Язык: Английский

Процитировано

0

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

и другие.

Geoscience Frontiers, Год журнала: 2023, Номер 15(3), С. 101670 - 101670

Опубликована: Июль 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.

Язык: Английский

Процитировано

6

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

Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 15 - 26

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

2

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

Sustainability, Год журнала: 2024, Номер 17(1), С. 83 - 83

Опубликована: Дек. 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.

Язык: Английский

Процитировано

1

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

Yan Kong,

Chuntong Dong, Yingyu Zhang

и другие.

Resources Policy, Год журнала: 2023, Номер 85, С. 103935 - 103935

Опубликована: Июль 18, 2023

Язык: Английский

Процитировано

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

и другие.

Open Journal of Statistics, Год журнала: 2023, Номер 13(04), С. 534 - 567

Опубликована: Янв. 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

Язык: Английский

Процитировано

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, Год журнала: 2023, Номер 30(26), С. 68792 - 68808

Опубликована: Май 2, 2023

Язык: Английский

Процитировано

1