空间时间分数衍生品期权定价模型的最佳计算与随机流动性风险和波动性使用组合神经网络
Lina Song1, Yangcheng Luo1, Xueting Yan1
1School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian 116025, China.
Chaos (Woodbury, N.Y.)
|May 2, 2025
概括
本研究介绍了期权定价的新型分数模型,包括流动性风险和波动性. 结合神经网络有效地解决了这些复杂的模型,显示了市场数据的高准确性.
科学领域:
- 量化金融 量化金融
- 数学建模的数学建模
- 计算金融是指计算金融.
背景情况:
- 期权定价模型往往简化了市场动态,忽视了流动性风险和非静止性等因素.
- 随机波动和分数计算提供了先进的方法来捕捉复杂的金融行为.
研究的目的:
- 开发和验证欧洲期权定价的新型空间时间分数混合模型.
- 将随机流动性风险和波动性纳入小数衍生品模型.
- 实现一个组合神经网络算法来解决这些复杂的模型.
主要方法:
- 使用卡普托类型的分数导数开发两个时空分数混合模型.
- 结合神经网络算法的应用,以解决带有混合边界条件的分数导数方程.
- 将模型结果与经典赫斯顿模型的分析解决方案以及市场数据进行比较.
主要成果:
- 提出的模型准确地捕捉了价格演变中的非线性和非静止性.
- 当流动性风险不存在时,减少模型与时空分数的赫斯顿模型保持一致.
- 纳入流动性风险的模型显示了小的预测错误和高的市场数据匹配.
结论:
- 开发的动态模型和组合神经网络为在随机流动性风险和波动性下进行期权定价提供了有效的工具.
- 分数衍生品模型在捕捉复杂的金融市场动态方面提供了卓越的性能.
- 该研究证明了拟议的计算方法的实际适用性和准确性.
相关概念视频
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
25
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
25
Pharmacokinetic Models: Comparison and Selection Criterion
20
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
20
Linear Approximation in Time Domain
53
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
53
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
221
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
221
Multicompartment Models: Overview
60
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
60
Calculating Standard Deviation
7.1K
The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
7.1K


