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研究生： 陳以婕
Chen, Yi-Jie
論文名稱： 以動態隨機關聯結構模型對大宗商品期貨之實證研究
An Empirical Study on Commodity Futures by Using Dynamic Stochastic Copula Models
指導教授： 張焯然
Chang, Jow-Ran
口試委員: 劉鋼
Liu, Kang
蔡璧徽
Tsai, Bi-Huei
學位類別： 碩士
Master
系所名稱： 科技管理學院 - 計量財務金融學系
Department of Quantitative Finance
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 31
中文關鍵詞： 大宗商品 、關聯結構模型 、狀態空間模型 、馬可夫鏈蒙地卡羅法
外文關鍵詞： Commodity, Copula models, State space models, Markov chain Monte Carlo methods
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    • 大宗商品期貨投資受到重視起源於20世紀末的網路泡沫，當投資者為了分散投資組合風險而尋求新的投資標的，大宗商品期貨的某些性質似乎正符合人們的需求，如Gorton and Rouwenhorst (2006) 驗證了大宗商品期貨報酬率與股票報酬率間為負相關。然而後續研究對於大宗商品是否具備分散風險能力的結論並不一致，也有文獻表示大宗商品的分散風險能力僅作用至2008年金融危機為止。
    本文收集自1990年5月31日至2019年12月31日，16種不同部門（包含能源、金屬、農業、畜牧業）的大宗商品期貨和S&P 500指數月資料，以關聯結構模型建構大宗商品期貨報酬率與股票報酬率之間的相依性質，並引入狀態空間模型描述其隨時而變的隨機過程，最後藉由馬可夫鏈蒙地卡羅法模擬參數估計值。實證結果表明大宗商品期貨報酬率與股票報酬率之間的相關性在市場動盪期間有明顯的增長，削弱了大宗商品作為分散風險用途資產的價值。

    Investing in commodity futures has gained importance since the dotcom bubble at the end of the 20th century. As investors looked for a new asset to diversify their portfolio risk, commodity futures looked attractive. For example, Gorton and Rouwenhorst (2006) find a negative correlation between commodity and stock returns. However, the conclusions of subsequent studies on whether commodity provides diversification benefits are not unanimous, and some literatures indicate that the diversification benefits of commodity work until 2008 financial crisis.
    In this paper, we collect 16 commodity futures monthly returns in different sectors (energy, metals, agricultural, and livestock) and S&P 500 index monthly returns from May 31, 1990 to December 31, 2019. We use a copula model to construct the dependence structure of commodity futures returns and stock returns, introduce a state space model to describe the time-varying process, and simulate the parameter estimates by Markov chain Monte Carlo (MCMC) methods. The empirical results show that the correlation between commodity and stock markets increases during the turbulent market period, which reduces the potential for commodity to diversify risk in the portfolio.

    Chapter 1　Introduction……………………………………………………………………………………………………………1 Chapter 2　Methodology………………………………………………………………………………………………………………4 2.1 Dynamic Stochastic Copula Models…………………………………………………………………4 2.1.1　Copula Function………………………………………………………………………………………………………………4 2.1.2　Copula-based State Space Model………………………………………………………………………5 2.1.3　Factor Structure of Copula-based State Space Model…………………8 2.2 MCMC Algorithms in Bayesian Estimation…………………………………………………9 2.2.1　Bayesian Inference……………………………………………………………………………………………………10 2.2.2　Markov Chain Monte Carlo Method…………………………………………………………………11 2.2.3　Gibbs Sampling………………………………………………………………………………………………………………13 Chapter 3　Data and Results………………………………………………………………………………………………14 3.1 Data…………………………………………………………………………………………………………………………………………14 3.2 Marginals Estimation………………………………………………………………………………………………16 3.3 Conditional Copula Estimation………………………………………………………………………18 3.4 Posterior Mean Correlation………………………………………………………………………………21 3.5 Posterior Correlation Matrix…………………………………………………………………………23 Chapter 4　Conclusion………………………………………………………………………………………………………………29 References……………………………………………………………………………………………………………………………………………30

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