Linear-Time Accurate Lattice Algorithms for Tail Conditional Expectation

IF 0.3 Q4 BUSINESS, FINANCE Algorithmic Finance Pub Date : 2014-05-26 DOI:10.3233/AF-140034
Bryant Chen, William W. Y. Hsu, Jan-Ming Ho, M. Kao
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引用次数: 5

Abstract

This paper proposes novel lattice algorithms to compute tail conditional expectation of European calls and puts in linear time. We incorporate the technique of prefix-sum into tilting, trinomial, and extrapolation algorithms as well as some syntheses of these algorithms. Furthermore, we introduce fractional-step lattices to help reduce interpolation error in the extrapolation algorithms. We demonstrate the efficiency and accuracy of these algorithms with numerical results. A key finding is that combining the techniques of tilting lattice, extrapolation, and fractional steps substantially increases speed and accuracy.
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尾部条件期望的线性时间精确点阵算法
本文提出了一种新的点阵算法来计算线性时间内欧式期权的尾条件期望。我们将前缀和技术纳入倾斜、三叉和外推算法以及这些算法的一些综合。此外,我们引入了分数阶格来帮助减少外推算法中的插值误差。用数值结果证明了这些算法的有效性和准确性。一个关键的发现是,将倾斜点阵、外推和分数步相结合,大大提高了速度和准确性。
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来源期刊
Algorithmic Finance
Algorithmic Finance BUSINESS, FINANCE-
CiteScore
0.40
自引率
0.00%
发文量
6
期刊介绍: Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.
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