| DC 欄位 |
值 |
語言 |
| DC.contributor | 統計研究所 | zh_TW |
| DC.creator | 鄭靖諴 | zh_TW |
| DC.creator | Jing-Sian Zheng | en_US |
| dc.date.accessioned | 2011-7-6T07:39:07Z | |
| dc.date.available | 2011-7-6T07:39:07Z | |
| dc.date.issued | 2011 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=982205001 | |
| dc.contributor.department | 統計研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在過去二十幾年當中，世界各地電力市場紛紛走上自由化的道路，在自由競爭的環境下，市場生產者與消費者所承擔的風險相對增加。也因此電力現價在波動上產生了幾個模式化事實，尤其是尖峰現象，指的是電價在短時間內顯著地上漲或下跌，隨後又回歸，這特性增加模型解釋的難度。本文也將拿股價做為比較，股價也有類似的特性，但其落下速率比起電價來得慢。
本文將採用兩種模型：均值回歸跳躍擴散模型、馬可夫狀態轉換模型，去評估其解釋電價和股價的能力。使用實際資料來檢驗，電價資料來自歐洲電力交易市場 (EEX) 的平日現價，而股價資料則是蘋果公司 (Apple Inc.) 的歷史收盤價。在配適之前，電價資料必須先經過排除季節性因素的步驟，本文使用混合過往幾位學者的方法來表示季節性因素。經過參數估計和模擬的步驟後，我們使用三種指標檢定模擬準確度。經過實例分析後，得到均值回歸跳躍擴散模型在解釋股價上較好，而馬可夫狀態轉換模型在解釋電價上較佳的結論。然而，這個結果是否適用在世界各地其他電價市場，建議需要更多實例來驗證。
| zh_TW |
| dc.description.abstract | Over the past two decades, many electricity markets around the world have decided to take the path of market liberalization. Since then, both consumers as producers are exposed to significantly higher risk. And some stylized facts of electricity spot prices have been found, especially the price spike which is a behavior that the prices increase or decrease significantly and return afterwards in short time intervals. This fact enhances the difficulty for modeling. For the purpose of comparison, we also apply the historical stock closing prices which have the similar behavior, but the return rate is not as high as the stock prices.
In this paper, we use two models which include mean-reverting jump diffusion model and Markov regime-switching model to assess their ability to explain the electricity prices from the European Energy Exchange (EEX) and the stock prices from the Apple Inc. Before fitting the models, electricity prices need to be deseasonalized. After parameter estimating and simulating, we use three ways to measure the errors between the simulated values and the true values. We conclude that mean-reverting jump diffusion model is better modeling the stock prices and Markov regime-switching model has better ability to explain the electricity prices. However, if the result is the same for other market data, it suggests to further investigation.
| en_US |
| DC.subject | 電力現價 | zh_TW |
| DC.subject | 股票現價 | zh_TW |
| DC.subject | 均值回歸跳躍擴散模型 | zh_TW |
| DC.subject | 馬可夫狀態轉換模型 | zh_TW |
| DC.subject | 尖峰現象 | zh_TW |
| DC.subject | 排除季節性因素 | zh_TW |
| DC.subject | electricity spot prices | en_US |
| DC.subject | stock prices | en_US |
| DC.subject | mean-reverting jump diffusion model | en_US |
| DC.subject | Markov regime-switching model | en_US |
| DC.subject | spikes | en_US |
| DC.subject | deseasonalized | en_US |
| DC.title | 電價與股票市場的實例分析 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Empirical Analysis for Electricity Prices and Stock Prices | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |