학술행사
- 발표자 김형지 교수(Trinity University)
- 개최일시 2024-07-08 15:00-17:00
- 장소 국가수리과학연구소 산업수학혁신센터(판교)
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일시: 2024년 7월 8일(월), 15:00-17:00
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장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
- 무료주차는 2시간 지원됩니다.
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발표자: 김형지 교수(Trinity University)
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주요내용: Forecasting Agricultural Futures Contracts with Econometric Factors and Stochastic Differential Equations
In this talk, we will begin by exploring the fundamentals of probabilistic modeling using stochastic differential equations (SDEs). We will then examine how SDEs, in conjunction with econometric models, can be employed to forecast the prices of futures contracts for agricultural commodities, such as corn. Our approach models price fluctuations in these contracts using an autoregressive moving average (ARMA) model with Fourier terms, integrated with stochastic processes to capture the Samuelson effect. To achieve this, we introduce an additional volatility term that varies with time-to-execution, which is explained through the Ornstein-Uhlenbeck (OU) process, also known as the mean-reversion process. As we delve into more advanced topics, we will discuss quantitative functional inequalities for specific classes of SDEs with degenerate noise, known as hypoelliptic SDEs. These include the reverse log-Sobolev inequality and Wang-type Harnack inequality, which provide deeper insights into the solutions of SDEs.
유튜브 스트리밍 예정입니다.
-
일시: 2024년 7월 8일(월), 15:00-17:00
-
장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
- 무료주차는 2시간 지원됩니다.
-
발표자: 김형지 교수(Trinity University)
-
주요내용: Forecasting Agricultural Futures Contracts with Econometric Factors and Stochastic Differential Equations
In this talk, we will begin by exploring the fundamentals of probabilistic modeling using stochastic differential equations (SDEs). We will then examine how SDEs, in conjunction with econometric models, can be employed to forecast the prices of futures contracts for agricultural commodities, such as corn. Our approach models price fluctuations in these contracts using an autoregressive moving average (ARMA) model with Fourier terms, integrated with stochastic processes to capture the Samuelson effect. To achieve this, we introduce an additional volatility term that varies with time-to-execution, which is explained through the Ornstein-Uhlenbeck (OU) process, also known as the mean-reversion process. As we delve into more advanced topics, we will discuss quantitative functional inequalities for specific classes of SDEs with degenerate noise, known as hypoelliptic SDEs. These include the reverse log-Sobolev inequality and Wang-type Harnack inequality, which provide deeper insights into the solutions of SDEs.
