Results 151 to 160 of about 482,276 (284)

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini   +2 more
wiley   +1 more source

Efficient Numerical Framework for Geothermal Energy Production Optimization in Fracture‐Controlled Reservoirs

open access: yesInternational Journal for Numerical Methods in Fluids, EarlyView.
We introduce an efficient open‐source numerical framework for the automated search for the placements of injection and production wells in hot fracture‐controlled reservoirs that sustainably optimize geothermal energy production. We model the reservoirs as discrete fracture networks in 3D. The fluid flow and heat transport in the reservoirs are modeled
Ondřej Pártl, Ernesto Meneses Rioseco
wiley   +1 more source

Coherent Forecasting of Realized Volatility

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley   +1 more source

Quadratic Hedging of American Options Under GARCH Models

open access: yesJournal of Futures Markets, EarlyView.
ABSTRACT American options are widely traded in financial markets, yet there is a scarcity of literature on hedging in incomplete markets. In this paper, we derive optimal hedging ratios and option values using Local Risk Minimization (LRM) and Global Risk Minimization (GRM) hedging strategies through dynamic programming.
Junmei Ma, Chen Wang, Wei Xu
wiley   +1 more source

Improving Implied Volatility Forecasts for American Options Using Neural Networks

open access: yesJournal of Futures Markets, EarlyView.
ABSTRACT This paper explores the application of neural networks to improve pricing of American options. Focusing on both American and European options on the S&P 100 index from January 2016 to August 2023, we integrate neural networks to model the difference between market‐implied and model‐implied volatilities derived from the Black‐Scholes and Heston
Haitong Jiang, Emese Lazar, Miriam Marra
wiley   +1 more source

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