| DC 欄位 |
值 |
語言 |
| DC.contributor | 財務金融學系 | zh_TW |
| DC.creator | 高敏淳 | zh_TW |
| DC.creator | Min-chun Kao | en_US |
| dc.date.accessioned | 2011-7-18T07:39:07Z | |
| dc.date.available | 2011-7-18T07:39:07Z | |
| dc.date.issued | 2011 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=984208014 | |
| dc.contributor.department | 財務金融學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 電力市場在早期主要由政府高度控制，不存在價格風險，近年來各國政府開放政策後，使越來越多的民間廠商進入市場，消費者也可自由洽談購買電力。而電力與其他商品相比，具有不可存儲的特性，這使得電力價格決定於每個時間點的供需平衡上，波動性很大，像是電力需求大增，或是電廠產能突然下降，就會造成價格跳躍，也使電力的生產者與消費者皆面臨很大的風險。1990年開始有電力交易所的出現，而後幾年更推出電力衍生性商品，目的就是規避掉價格變動的風險。像對需要長期大量使用電力的工廠來說，可以透過購買電力選擇權，將成本鎖定在一定水準。但電力的特色使模型建構不易，很難做衍生性商品的定價，本文先沿用過去文獻做法將電力價格模型拆成季節趨勢部分與隨機變動部分，在隨機變動過程使用OU-type的均值回歸Lévy模型：OU-VG與OU-NIG模型建構，發現OU-VG配適結果較佳，接著模型經由Conditional Esscher Transform轉換到風險中立測度下，利用蒙地卡羅法模擬算出電力選擇權的買權價格。
| zh_TW |
| dc.description.abstract | In the past, the electricity market was controlled by the government so there was no price risk. Recently, governments have taken open policies. More and more private firms entered the market and consumers also could purchase power freely. Compared with other commodities, the price of electricity is determined by supply and demand at each point of time due to non-storability of electricity. If electricity demand rises or power production drops suddenly would cause the price to jump. The power producers and consumers face great risk because of high volatility of electricity price. Power exchanges began in 1990 and electricity derivatives whose purposes were to avoid the risk of price volatility were introduced in few years later. Firms which need huge amount of electricity can buy electricity options and control the cost. However, modeling electricity prices is not easy owing to the characteristics of electricity; it is also difficult to price electricity derivatives. Hence, this study splits electricity price model into seasonal trend and random change parts based on the past literatures. In random process, we use OU-type process of mean-reversion Lévy Model, OU-VG and OU-NIG model and find that OU-VG fits better. Subsequently, the model via Conditional Esscher Transform switches to risk-neutral measure and use Monte Carlo simulation to calculate the call price of power option.
| en_US |
| DC.subject | 電力選擇權 | zh_TW |
| DC.subject | 均值回歸Lévy | zh_TW |
| DC.subject | Mean Reversion Lévy | en_US |
| DC.subject | Electricity Option | en_US |
| DC.title | 電力選擇權之定價-以均值回歸Lévy模型 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Pricing Electricity Option under a Mean Reversion Lévy Model | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |