Risk Measuring and Forecasting-The Case of Crude Oil
| Autor: | Wan-Hsiu Cheng, 鄭婉秀 |
|---|---|
| Rok vydání: | 2006 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 94 Risk measuring and forecasting are important issues in finance, however most literature focuses on the financial assets, and fewer papers discuss energy assets. The Petroleum market is characterized as highly volatile, the imbalance of supply and demand, the strategies adopted by the Organization of Petroleum Exporting Countries (OPEC), the interference of politics and so on have all stimulated prices. The oil prices have climbed up steadily recently, and it has not only shocked the petroleum market traders, but also influenced the financial market as well, owing to the high volatility of the crude oil. Thus the investigation of the crude oil risk is an important issue. This thesis analyzes the value-at-risk, the jump volatilities, and the forecast problems in crude oil of West Texas Intermediate (WTI), which modifies the shortcomings of traditional models in measurements and forecasts. The first topic is discussing the Value-at-Risk (VaR). VaR is the most popular and attractive method of risk measuring. We estimate the VaR of the return on crude oil via RiskMetrics and the AR-GARCH model using the rolling bootstrapping methods. We adopt the bootstrapping method rather than using the Monte Carlo simulation or the historical simulation method because traditionally they are methods to estimate VaR. Even though they are traditional methods but they actually have the two severe problems of distribution assumption, Monte Carlo simulation, and a short observation period, historical simulation. The empirical results demonstrate that the bootstrapping method outperforms the no-bootstrapping method in the one-day-ahead VaR forecast but not in the ten-days-ahead forecasts. Furthermore, the performances of VaR forecasts are statistically indifferent in both the RiskMetrics and the AR-GARCH models. The second topic is estimating the volatility of crude oil and gasoline while considering jumps. Previous studies in the literature almost all assumed that time series data follows a smooth and continuous volatility process. However, the presence of abnormal events induce serious violate in price, and the diffusion models are misspecified statistically. Therefore, considering the jump model with discrete characteristics is necessary while facing the abnormal events like two Gulf Wars. We further employ a correlated bivariate Poisson GARCH model suggested by Chan (2003) to investigate the relationship between the volatility of crude oil and gasoline; especially during the period of the Gulf War. We find that greater jumps occurring in crude oil returns will appear in gasoline returns at the same time, but the magnitude of the co-movements in volatility falls. The covariance is relatively smaller in the Second Gulf War compared to the first conflict. The volatility of crude oil is more sensitive than gasoline during the periods of wars. Furthermore, the jump that occurred by the war did not lead both spot prices to a high persistent level for a long period, which fits the feature of the jump models. The third topic investigates an essential problem of how to determine the estimation period in forecasting. Until now, less attention has been given to the problem of determining the appropriate estimation periods. Using the forecast encompassing and accuracy test, this investigation discusses the importance of considering the overall useful information in the in-sample period. An excessively short sample period will increase the variance of the parameter estimation and bias the out-of-sample forecasts. This study further constructs a nested linear regression model, either with or without the structural change, depending on the existence of a break, and comparing the performance of the two versions of the model for each estimation period and forecast scheme. The empirical results demonstrate that forecasts under the correct model reduces both measurement loss and the mean square forecast as we increase the in-sample estimation period. For the forecast accuracy and encompassing tests, the use of fewer observations in making an estimate could easily lead to wrong decisions and the acceptance of the wrong model. Finally, these results are also consistent with the hedge effectiveness, namely that the effectiveness is better under the recursive scheme in terms of considering all useful information. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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