An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics. Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics
2023-03-12 23:23:41 29.27MB Monte Carlo Simulation
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柳树 willowtree是Michael Curran的同名衍生产品定价模型的开源Python实现。 Curran,M.(2001年): 什么是柳树? 柳树是一种高效的重组格子,旨在快速,准确地对衍生合约进行定价。 它通过离散时间马尔可夫链直接对标准布朗运动进行建模,所得的估计值可作为更复杂过程(例如几何布朗运动)的基础。 晶格具有两个鲜明的特征: 它根据布朗运动作为时间的平方根扩展,并且与二项式模型不同,后者随着时间线性增长。 它在一开始就非常快地打开,覆盖了被标准树忽略的高概率区域,后来又慢慢地被限制在正态分布的置信度范围内。 这方面既避免浪费时间和计算资源浪费在分布的尾部,又对几乎不影响当前证券价格的定义的区域,并且避免了修剪树的任意做法,即无视分支,以及他们的后代,位于低概率区域; 它在每个时间步中具有恒定数量的节点。 随着时间的推移,该数字线性增长,而不是二项式模型中
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英文的,配得上——用monte carlo模拟方法对金融衍生品定价的经典著作称号。
2022-05-12 01:19:12 13.22MB monte_carlo _simulation financial_engineering
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非常好的量化基础参考书,复习面试好帮手。 注意是第二版哦!
2021-11-30 11:59:18 13.39MB FE Data Stat
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这本书适合本科生,低年级博士生学习,是用数学研究金融领域的一本很好的教材。
2021-08-12 20:15:32 3.48MB 蒙特卡洛
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Mathematics for Finance An introduction to Financial Engineering Second Edition Springer
2021-04-12 16:00:32 11.79MB Mathematics Finance Financial Engineering
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portfolio management and risk management, financial engineer must have
2021-03-25 15:26:44 37.6MB financial en
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Publication Date: November 17, 2010 | ISBN-10: 1441977864 | ISBN-13: 978-1441977861 | Edition: 2011 Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.
2019-12-21 20:25:59 11.4MB Portfolio Management
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