開始日期: 2023-12-16

　　課時安排: 7周在線小組科研學習+5周不限時論文指導學習

　　適合人群

　　適合年級 (Grade): 高中生/大學生

　　適合專業(yè) (Major): 應用數學、金融經濟學、宏觀經濟學、計量經濟學、金融數據分析、股票投資、商業(yè)分析等專業(yè)或希望修讀相關專業(yè)的學生;學生需具備隨機變量、概率論等相關知識并熟練掌握R語言。

　　導師介紹

　　Peter

　　麻省理工學院 (MIT)終身教職

　　Peter 導師以優(yōu)異的成績獲得哈佛大學(Harvard University)應用數學學士學位，并當選為Phi Beta Kappa Alpha Chapter的成員。后續(xù)他攻讀統(tǒng)計學，獲得了帝國理工學院(Imperial College London)的碩士學位以及加州大學伯克利分校(University of California Berkeley)的博士學位。Peter 曾任哈佛大學統(tǒng)計系教授，任教期間獲得了美國國家科學基金會的博士后數學科學研究獎學金。隨后成為麻省理工學院Sloan管理學院終身教授兼首席研究科學家，在經濟和管理科學計算研究中心(CCREMS)和國際金融服務研究中心(IFSRC)進行研究。他是風險管理項目組的積極成員，并開發(fā)了納入行業(yè)標準RiskMetrics方法論的分析方法?，F為麻省理工學院數學系金融數學與統(tǒng)計講師。2014年在北京交通大學全球暑期學校任教期間，被聘為計算機與信息技術學院特聘教授。

　　自1992年以來，Peter 一直通過他的公司Kempthorne Analytics, Inc. 為各種機構提供金融和統(tǒng)計分析咨詢服務?？蛻舭ɑㄆ煦y行(Citibank)、Colonial/Liberty Funds、美國運通(American Express)、巴黎國家銀行(Banque Nationale de Paris)、佳能(Canon)等。主要項目包括：股票市場的資產選擇建模、風險管理的統(tǒng)計分析、風險管理軟件的設計和實現、衍生品定價的金融分析、災難性風險分析--風險暴露建模和保險定價方案、股票市場以數據微觀結構建模以及交易系統(tǒng)的設計、開發(fā)、實現。自1995年以來，Peter一直擔任投資經理，利用先進的統(tǒng)計分析來管理各種投資項目，他在一家完全系統(tǒng)化的量化對沖基金IKOS CIF, LTD，擔任投資組合經理和高級研究員。管理和增強投資組合的構建，alpha模型的評估、開發(fā)、執(zhí)行分析和投資組合優(yōu)化，以及期貨和貨幣投資組合的風險建模和管理。作為高級研究員，他擔任研究指導委員會主，管理和指導研究人員，并協(xié)調IKOS/牛津大學博士實習生計劃。他于1995年聯合創(chuàng)立了Chronos Asset Management，于1996年聯合創(chuàng)立了Summa Capital Management。作為這兩家投資管理公司的負責人，他運用自己專有的分析方法開發(fā)統(tǒng)計交易模型和交易系統(tǒng)，并監(jiān)督交易操作。Peter導師持有Series 3和Series 65許可證，并在the National Futures Association是注冊商品交易顧問。他活躍于John Bertram House Inc.(1998-2010)和Lynn Home for Young Women, Inc.(2005-2010)的董事會，曾擔任兩家非營利公司的財務主管，并擔任監(jiān)督信托資產管理的財務委員會主席。

　　Professor Peter received his B.S. in Applied Mathematics from Harvard University with honors and was elected a member of the Phi Beta Kappa Alpha Chapter. He pursued graduate studies in Statistics receiving M.Sc degree from Imperial College London and Ph.D from University of California Berkeley. Peter was awarded a National Science Foundation Postdoctoral Research Fellowship in Mathematical Sciences while a professor in the Department of Statistics at Harvard University. He then became an Associate Professor and Principal Research Scientist at MIT Sloan School of Management, conducting research at the Center for Computational Research in Economic and Management Sciences (CCREMS) and the International Financial Services Research Center (IFSRC). He is a member of the Risk Management Project team and has developed analytical methods of Risk Metrics methodology which is incorporated into industry standard. He is currently a Lecturer in MIT Mathematics Department on Financial Mathematics and Statistics. In 2014, while teaching at the Global Summer School of Beijing Jiaotong University, he was appointed as a special professor of the School of Computer and Information Technology.

　　Peter has been providing financial and statistical analysis consulting services to various institutions through his own company Kempthorne Analytics, Inc. since 1992, clients include Citibank, Colonial/Liberty Funds, American Express, Banque Nationale de Paris, Canon. The main projects include: asset selection modeling of stock market, statistical analysis of risk management, design and implement risk management software, financial analysis of derivatives pricing, catastrophic risk analysis--risk exposure modelling and insurance pricing schemes, stock market based on data microstructure modeling and trading system design, development and implementation. Peter has been an investment manager since 1995, using advanced statistical analysis to manage various investment projects, where he is a portfolio manager and senior Research fellow at IKOS CIF, LTD. Manage and enhance portfolio construction, evaluation, development, performance analysis and portfolio optimization of alpha models, and risk modeling and management of futures and currency portfolios. As a Senior Research Fellow, he serves on the Research Steering Committee, manages and directs researchers, and coordinates the IKOS/ Oxford PhD Internship programme. He co-founded Chronos Asset Management in 1995 and Summa Capital Management in 1996. As head of these two investment management firms, he applied his proprietary analytical methods to develop statistical trading models and trading systems, and oversaw trading operations. Peter holds Series 3 and Series 65 licenses and is a registered Commodity Trading Advisor with the National Futures Association. He was active on the boards of John Bertram House Inc.(1998-2010) and Lynn Home for Young Women, Inc.(2005-2010) and served as Treasurer of two non-profit corporations, also chaired the Finance Committee, which oversaw the management of trust assets.

　　任職學校

　　麻省理工學院(MIT)創(chuàng)立于1861年，是世界著名私立研究型大學，在2023年U.S.News世界大學排名中綜排位列第二。學校孕育了97位諾貝爾獎得主、59位美國國家科學獎章獲得者，以及75位麥克阿瑟獎獲得者。

　　項目背景

　　時間序列是指將某種現象某一個統(tǒng)計指標在不同時間上的各個數值，按時間先后順序排列而形成的序列。時間序列法是一種定量預測方法，亦稱簡單外延方法，在統(tǒng)計學中作為一種常用的預測手段被廣泛應用。時間序列分析在第二次世界大戰(zhàn)前應用于經濟預測。二次大戰(zhàn)中和戰(zhàn)后，在軍事科學、空間科學、氣象預報和工業(yè)自動化等部門的應用更加廣泛。時間序列分析(Time series analysis)是一種動態(tài)數據處理的統(tǒng)計方法。該方法基于隨機過程理論和數理統(tǒng)計學方法，研究隨機數據序列所遵從的統(tǒng)計規(guī)律，以用于解決實際問題。時間序列構成要素是：現象所屬的時間，反映現象發(fā)展水平的指標數值。

　　項目介紹

　　本課程將重點介紹時間序列分析的基本方法和模型及其在經濟、金融數據分析中的應用。本課程將融合計算機編程的R語言輔助時間序列模型在金融經濟數據中的處理分析。目前，主流經濟數據分析往往會以圖形方法來進行呈現，這些可視化方法被用于大數據探索、分析模型的有效性驗證和數據預測結果的展現。在本課程中，導師開發(fā)并應用了趨勢和季節(jié)性的重要時間序列模型，包括經典分解和多級指數平滑模型。同時導師將利用真實世界的時間序列數據(包括美國聯邦儲備局、世界銀行和雅虎金融數據庫)對本課程中涵蓋的統(tǒng)計概率方法進行分析和實踐應用。Introduction to fundamental methods and models of time series analysis with applications in economics, finance, and public health. The course uses R to forecast time series. Graphical methods are emphasized for data exploration, analyzing the validity of models, and presenting forecast results. Important models of trend and seasonality are developed and applied, including classical decompositions and multi-stage exponential smoothing. Real-world time series data are collected from the internet and analyzed with the methods covered in the course.

　　項目大綱

　　時間序列分析導論 Introduction to Time Series Analysis

　　時間序列模型;金融時間序列 Simple Time Series Models; financial time series

　　預估噪聲序列的時間序列相關性檢驗固定的流程 Testing estimated noise sequences for time series dependence; stationary processes

　　回歸(AR)、移動平均(MA)和ARMA模型 ;模型選擇和預測 Auto-regression (AR), moving average (MA), and ARMA models;model selection and forecasting

　　學術研討1 Final Project Phase I

　　學術研討1 Final Project Phase II

　　項目回顧和成果展示 Program Review and Presentation

　　論文輔導Project Deliverables Tutoring

　　項目收獲

　　7周在線小組科研學習+5周不限時論文指導學習 共125課時

　　項目報告

　　優(yōu)秀學員獲主導師Reference Letter

　　EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級別索引國際會議全文投遞與發(fā)表指導(可用于申請)

　　結業(yè)證書

　　成績單

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