期权定价中的聚类与分类

IF 0.7 Q3 ECONOMICS Review of Economic Analysis Pub Date : 2011-09-30 DOI:10.15353/rea.v3i2.1458
N. Gradojevic, D. Kukolj, R. Gencay
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引用次数: 5

摘要

本文回顾了最近的期权定价文献,并研究了聚类和分类如何辅助期权定价模型。具体来说,我们考虑非参数模块化神经网络(MNN)模型来为标准普尔500欧洲看涨期权定价。重点是将期权数据分解并分类为多个跨金钱和期限范围的子模型,这些子模型分别进行处理。我们提出的模糊学习向量量化(FLVQ)算法生成由“智能”分类边界划分的决策区域(即选项类)。这种方法提高了MNN模型的泛化性能,从而提高了其定价精度。
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Clustering and Classification in Option Pricing
This paper reviews the recent option pricing literature and investigates how clustering and classification can assist option pricing models. Specifically, we consider non-parametric modular neural network (MNN) models to price the S&P-500 European call options. The focus is on decomposing and classifying options data into a number of sub-models across moneyness and maturity ranges that are processed individually. The fuzzy learning vector quantization (FLVQ) algorithm we propose generates decision regions (i.e., option classes) divided by ‘intelligent’ classification boundaries. Such an approach improves generalization properties of the MNN model and thereby increases its pricing accuracy.
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
10
审稿时长
26 weeks
期刊最新文献
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