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首页数据驱动的欧式期权定价法:基于随机漫步模型与最小熵鞅测度
本文档标题为《纯粹数据驱动的欧洲期权定价方法(2006)》,发表在《重庆大学工程教育》的"数学与应用"栏目上,卷5,第3期,9月刊。作者黄光湖和万建平来自华中科技大学数学系,他们提出了一个基于随机漫步市场模型的全新期权定价策略。 核心内容是建立在无套利机会的最小熵鞅度量基础上的方法。这个理论假设市场中不存在任何可利用的套利机会,因此得出的最小熵鞅度量成为了定价依据。这种方法摒弃了对基础资产价格过程分布的任何先验假设,采用蒙特卡洛模拟技术来评估欧式期权的价值,具有纯数据驱动的特点。 相比于传统的定价方法,如Black-Scholes模型中的经典估值方法和历史波动率为基础的定价,这种新的方法更加适应实际金融市场中的不确定性。通过在模拟的Black-Scholes世界中进行对比实验,研究结果表明,这个纯粹数据驱动的定价方法具有显著优势,对于金融工程实践具有很高的价值。 关键词包括:衍生证券、最小熵鞅度量、无套利定价、数据驱动、蒙特卡洛模拟以及欧洲期权。这篇论文为金融市场的实证分析提供了一种创新且实用的期权定价工具,有助于提升市场参与者对复杂金融产品的定价准确性,并可能推动金融理论与实践的进一步发展。
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J Chongqing Univ.-Eng. Ed.
Mathematics & Application
Vol. 5 No. 3
September 2006
Article ID: 1671-8224(2006)03- 0175-06
A purely data driven method for European option valuation
∗
HUANG Guang-hui
a
, WAN Jian-ping
Department of Mathematics, Huazhong University of Science &Technology, Wuhan 430074, P.R. China
Received 20 June 2006; revised 27 June 2006
Abstract: An alternative option pricing method is proposed based on a random walk market model. The minimal entropy
martingale measure which adopts no arbitrage opportunity in the market, is deduced for this market model and is used as the
pricing measure to evaluate European call options by a Monte Carlo simulation method. The proposed method is a purely data
driven valuation method without any distributional assumption about the price process of underlying asset. The performance of
the proposed method is compared with the canonical valuation method and the historical volatility-based Black-Scholes
method in an artificial Black-Scholes world. The simulation results show that the proposed method has merits, and is valuable
to financial engineering.
Keywords: derivative security; minimal entropy martingale measure; Monte Carlo simulation
CLC number: F224.7 Document code: A
1 Introduction
a
Basic research on how to formulate a theory or a
method that allows extracting general characteristics
of a system from partial and incomplete information
can be traced back to the 18th century; Jakob
Bernoulli (1713), Laplace (1744) and Bayes (1763)
all input great efforts on the basic problem of
calculating the state of a system based on a limited
number of expectation data [1]. Their efforts resulted
in a wide variety of theoretical and empirical
achievements. The classical maximum entropy is
designed to handle such questions and is commonly
used as a method of estimating a probability
distribution from an insufficient number of moments
representing the only available information.
Stutzer [2, 3] proposed an entropic option pricing
theory with an arbitrage constraint, which is known as
the canonical valuation method. In Stutzer's frame-
work, the unique pricing measure is the canonical
probability measure whose relative entropy to the
a
HUANG Guang-hui ( 黄光辉): Male; Born 1977; PhD
candidate; Research interests: derivative security valuation,
financial engineering; Monte Carlo simulation; E-mail:
guanghui77huang@gmail.com.
*
Funded by the Natural Science Foundation of China under
Grant No.10571065.
natural market measure is the minimum among
equivalent martingale measures under which the
discounted summation of the price and the cumulated
dividends is a martingale. Empirical investigations
have proved the advantages of this canonical
valuation method [3].
Our purpose was to develop another entropic option
pricing method in a different economy, and compare
the performance of the proposed method with the
canonical valuation method and the Black-Scholes
formula in an artificial Black-Scholes world. Our
simulation results suggested that the proposed method
is better than the canonical valuation method when
the options are out-of-the-money and at-the-money.
However, when the options are in-the-money, our
method works very well while the canonical method
works even better. In comparison with the Black-
Scholes formula, we found that when the price
process of the underlying asset is a geometric
Brownian motion, the results of the proposed method
are accurate even without any knowledge, except for
the extremely deep out-of-the-money options. So, our
method turns out to be practicable, and is valuable to
the theory and practice of financial engineering.
Hereinafter, we deduced in Section 2 the minimal
entropy martingale measure basing on a random walk
market model called finite state multi-period market
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