长记忆随机波动率模型下的带交易费与红利欧式期权定价

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本文标题"European option pricing with transaction costs and dividends under the long memory stochastic volatility model"聚焦于金融领域中的一个关键问题,即在长记忆随机波动率模型中考虑交易费用和股息对欧式期权定价的影响。作者刘倩和王晓天,分别来自中国科学技术大学数学系,他们针对这个复杂且具有挑战性的金融定价问题进行了深入探讨。 在传统的金融模型中,欧式期权定价通常假设市场是线性的,而实际金融市场往往表现出非线性和复杂性。长记忆随机波动率模型引入了时间序列的自相似性和长期依赖性,这使得价格变动不再遵循简单的随机漫步,而是呈现出更为动态和不确定的特性。在这种背景下,研究者考虑了现实世界中的两个重要因素:交易费用和股息支付。 交易费用是投资者在买入或卖出期权时不可避免的成本,它会影响投资者的实际回报和策略选择。在长记忆模型中,由于市场的非平稳性,交易费用的影响可能更加显著。同时,股息的存在会改变股票价值,从而间接影响期权的价值,特别是在含有分红的资产上。 刘倩和王晓天利用“锚定与调整”(anchoring and adjustment)的理论方法,这是一种创新的定价策略,它能够捕捉到长记忆模型下波动率的长期依赖性,并将交易费用和股息的影响纳入考量。他们的工作旨在提供一个准确反映这些现实因素的欧式看涨期权定价公式,这对于理解和预测金融市场的定价行为具有重要的实践意义。 关键词包括"锚定调整"、"股息"、"delta-hedging"(Delta对冲策略)、"缩放"以及"交易成本",这些都是理解文章核心内容的关键术语。这项首发论文深入剖析了长记忆随机波动率模型下的期权定价问题,对于金融工程和实证金融学的研究者来说,提供了有价值的理论支持和实操指导。

Here are the detail information provided in PPTs:The option is an exotic partial barrier option written on an FX rate. The current value of underlying FX rate S0 = 1.5 (i.e. 1.5 units of domestic buys 1 unit of foreign). It matures in one year, i.e. T = 1. The option knocks out, if the FX rate:1 is greater than an upper level U in the period between between 1 month’s time and 6 month’s time; or,2 is less than a lower level L in the period between 8th month and 11th month; or,3 lies outside the interval [1.3, 1.8] in the final month up to the end of year.If it has not been knocked out at the end of year, the owner has the option to buy 1 unit of foreign for X units of domestic, say X = 1.4, then, the payoff is max{0, ST − X }.We assume that, FX rate follows a geometric Brownian motion dSt = μSt dt + σSt dWt , (20) where under risk-neutrality μ = r − rf = 0.03 and σ = 0.12.To simulate path, we divide the time period [0, T ] into N small intervals of length ∆t = T /N, and discretize the SDE above by Euler approximation St +∆t − St = μSt ∆t + σSt √∆tZt , Zt ∼ N (0, 1). (21) The algorithm for pricing this barrier option by Monte Carlo simulation is as described as follows:1 Initialize S0;2 Take Si∆t as known, calculate S(i+1)∆t using equation the discretized SDE as above;3 If Si+1 hits any barrier, then set payoff to be 0 and stop iteration, otherwise, set payoff at time T to max{0, ST − X };4 Repeat the above steps for M times and get M payoffs;5 Calculate the average of M payoffs and discount at rate μ;6 Calculate the standard deviation of M payoffs.

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