一、講座時(shí)間

2024年10月16日（周三）13:30

二、講座地點(diǎn)

浙江工業(yè)大學(xué)屏峰校區(qū)?博易樓B303

三、講座主題

OnFourierMethodsandMachineLearningTechniquesinComputationalFinance

四、主講人簡(jiǎn)介

Cornelis(Kees)Oosterlee教授是烏特勒支大學(xué)擔(dān)任數(shù)學(xué)學(xué)院院長(zhǎng)和金融數(shù)學(xué)系系主任。自2000年以來，他一直致力于金融數(shù)學(xué)領(lǐng)域的計(jì)算問題研究。他是兩本英文教科書的合著者：2001年出版的《多重網(wǎng)格》2019年出版的《金融數(shù)學(xué)模型與計(jì)算》，并發(fā)表了眾多學(xué)術(shù)論文。上述第二本書最近已被翻譯成中文出版。

Oosterlee教授共同開發(fā)的金融衍生品定價(jià)和風(fēng)險(xiǎn)管理方法包括基于傅里葉余弦展開的COS方法、Shannon?Wavelet?Inverse?Fourier?Transform?method?(香農(nóng)小波逆傅里葉變換方法)、Stochastic?Grid?Bundling?Method?(隨機(jī)網(wǎng)格捆綁方法)、Stochastic?Collocation?Monte?Carlo?Method?(隨機(jī)配置蒙特卡羅方法)和Seven-League格式(7L)。金融機(jī)器學(xué)習(xí)是Oosterlee研究組的另一個(gè)研究興趣，他在這一領(lǐng)域致力于最優(yōu)投資組合選擇、時(shí)間序列和異常檢測(cè)方法的研究。Oosterlee教授曾領(lǐng)導(dǎo)兩個(gè)與業(yè)界合作的歐盟金融和保險(xiǎn)風(fēng)險(xiǎn)管理項(xiàng)目，他還支持了幾個(gè)荷蘭國(guó)家級(jí)項(xiàng)目。他曾在英國(guó)牛津大學(xué)、日本一橋大學(xué)、西班牙科魯尼亞大學(xué)等多所大學(xué)擔(dān)任客座講師。

五：講座摘要

In?this?presentation,?we?explore?the?integration?of?Fourier?methods?and?machine?learning?techniques?in?computational?finance,?focusing?on?the?pricing?of?financial?derivatives.?We?begin?by?revisiting?the?COS?method,?a?Fourier-cosine?expansion?technique,?as?a?fast?and?accurate?method?for?option?valuation,?leveraging?characteristic?functions?of?asset?price?processes.?We?demonstrate?the?computational?efficiency?of?the?COS?method,?particularly?when?applied?to?models?like?the?Heston?stochastic?volatility?model.?The?challenges?of?implied?volatility?computation?and?model?inversion?are?addressed.?To?overcome?these?challenges,?we?employ?neural?networks?to?approximate?the?pricing?function?and?its?inverse,?introducing?techniques?such?as?gradient?squashing?to?handle?steep?gradients?effectively.?The?Calibration?Neural?Network?(CaNN)?framework?is?presented,?combining?the?COS?method?with?neural?networks?to?accelerate?option?pricing?and?model?calibration.?We?showcase?how?the?CaNN?approach?significantly?reduces?computational?time?while?maintaining?high?accuracy,?making?it?suitable?for?real-world?financial?engineering?tasks.?This?collaborative?work?with?Shuaiqiang?Liu,?Sander?Bohte,?Anastasia?Borovykh,?demonstrates?the?synergy?between?advanced?numerical?methods?and?machine?learning?in?addressing?computational?challenges?in?finance.