期望波動度在投資組合的選擇、避險策略、資產管理，以及金融資產的評價上是關鍵性因素，因此，在波動度變化甚巨的金融市場中，找出具有良好預測波動度能力的模型，是絕對必要的。過去從事資產價格行為的相關研究都假設資產的價格過程是隨機的，且呈對數常態分配、變異數固定。然而實證結果一再顯示：變異數是隨時間而變動的（如 Mandelbrot(1963)、 Fama(1965)）。為預測波動度（或變異數），Eagle(1982)首先提出了 ARCH 模型，允許預期條件變異數作為過去殘差的函數，因此變異數能隨時間而改變。此後 Bollerslve(1986)提出 GARCH 模型，修正ARCH 模型線性遞減遞延結構，將過去的殘差及變異數同時納入條件變異數方程式中。 Nelson(1991)則提出 EGARCH 模型以改進 GARCH 模型的三大缺點，此模型對具有高度波動性的金融資產提供更成功的另一估計模式。除上列之 ARCH-type 模型外，Hull and White(1987)提出連續型隨機波動模型(continuous time stochastic volatility model)，用以評價股價選擇權，此模型不僅將過去的變異數納入條件變異數的方程式中，同時該條件變異數也會因隨機噪音(random noise)而變動。近年來，上述模型均被廣泛運用在模擬金融資產的波動性，均是相當實用的模型。 本文以隨機漫步(random walk)、GARCH(1,1)、EGARCH(1,1)及隨機波動模型(stochastic volatility)進行不同期間下，股價指數與外匯波動度之預測，並以實證結果判斷上述四種模型在預測外匯及股價指數波動度的能力表現。實證結果顯示：隨機波動模型不論在股價指數或外匯、長期或短期的波動度預測上，都是最佳的波動度預測模型，因此建議各大金融機構可採隨機波動模型預測金融資產未來的波動度。 / Volatility forecast is extremely important factor in portfolio chice, hedging strategies, asset management, asset pricing and option pricing. Identifying a good forecast model of volatility is absolutely necessary, especially for the highly volatile Taiwan stock marek. Due to increasing attention to the impact of marke risk on asset returns, academic researchers and practicians have developed ways to control risk and methodologies to forecast return volatility. Past researches on asset price behavior usually assumed that asset price behavior follows random walk, and its probability distribution is a log-normal distribution with a constant variance (or constant volatility). This assumption is in fact in violation of empirical evidence showing that volatility tends to vary over time (e.g., Mandelbrot﹝1963﹞ and Fama﹝1965﹞). To forecast volatility (or variance), Engle(1982) is the first scholar to propose a forecast model, now well-known as ARCH, whose conditional variance is a funtion of past squared returns residuals. Accordingly, the forecast variance(or volatility) varies over time. Bollerslev(1986) proposed a generalized model, called GARCH, which allows the current conditional variance depends not only on past squared residuals, but also on past conditional variances. However, Nelson(1991) has recently proposed a new model, called EGARCH, which attempts to remove the weakness of the GARCH model. The EGARCH model has been shown to be successful to forecast volatility and to describe successful stock price behavior. In addition, Hull and white(1987) employed a continuous-time stochastic volatility model to develop in option pricing model. Their stochastic volatility model not only admits the past variance, but also depends on random noise of volatility. The above-mentioned models have been widely implemented in practice to simulate and to forecast asset return volatility. This thesis investigates whether random walk, GARCH(1,1), EGARCH(1,1) and stochastic volatility model differ in their ability to predict the volatility of stock index and currency returns over short-term and long-term horizons. The results strongly support that the best volatility predictions are generated by the stochasic volatility model. Therefore, it is recommended that financial institutions may adopt stochastic volatility model to predict asset return volatility.

本研究之目的在探討討強制性財務預測下,公司管理當局對於不準確度程度過高之公司,其可能之修正行為, 準確度係預計值與實際值之間的差異從會計理論之觀點而言管理當局可用盈餘管理(隱含方式),或修正預測(外在方式)為之達到預測準確性之目的,本研究首先要了解之問題便是財務預測之更新與否與盈餘管理之間的關連,其次由於新上市公司於國內外實證研究中發現其於上市前後存有盈餘管理之現象故本研究推論由上市目的所編製之財務預測亦有較現金增資者有較樂觀之傾向存在另外在我國企業之歷史性財務報表之查核會計師通常亦為該公司之財務預測核閱會計師故本研究亦推論會計師之聲譽對於準確度有影響以上三項問題均以WILCOXONTEST 及T TEST 來回答另外本研究欲建立一可以解釋預測準確性之迴歸式而以預測更新與否,會計師聲譽,財務預測發布目的及盈餘管理幅度為自變數研究結果顯示管理當局傾向於以更新預測之方式達成其準確度之目的會計師聲譽並非決定準確度之決定性因素上市目的所發佈者較現金增資所發布者保守推論其可能之原因與承銷商之連坐記點有關故閱表者可從財務預測之更新與否及發布目的兩者綜合判斷財務預測之準確性

本研究藉由探討公司盈餘的可預測性與財務分析師預測偏差性的關係，俾瞭解財務分析師獲取私有資訊的動機與預測偏差性的關係，本研究並擬進一步探討，當財務分析師預測資訊成為已公開資訊之時，市場上之投資者對於財務分析師此策略性的預測偏差性行為，是否具有辨識性，並能進一步的在股價中加以反應調整。經由實證結果，本研究獲致如下的結論: 1.財務分析師在短期的各年度預測值中，呈現不同程度的預測偏差性，但以整個樣本期間的長期平均而言，財務分析師並未呈顯著的預測偏差性。 2.公司盈餘可預測性愈低，財務分析師樂觀性預測偏差愈高。 3.公司規模愈大，財務分析師樂觀性預測偏差愈低。 4.公司產業別、內部持股比率、上市期間長短、市場風險與股票交易週轉率對於財務分析師預測偏差性不具有影響性。 5.就市場平均性而言，可完全反應調整財務分析師的預測偏差性。 6.市場上無法反應調整財務分析師對於低可預測性公司具較高程度的預測偏差性。

本論文的目的，在探討我國自願性財務預測公告與證券信用交易之間的關係。信用交易的增減代表使用信用交易的投資者對某特定資訊的瞭解與使用，因此實證檢視財務預測的修正行為與信用交易增減的關係，可以敏銳地瞭解，是種特定投資者在哪個時點對財務預測修正進行理性預期，並予使用且做了較實際的交易行為。因此，本研究的測試可以瞭解使用信用交易的投資者如何使用財務預測等相關資訊。據此，本研究的結果有助於了解使用信用交易的投資者如何運用自願性財務預測資訊來做投資決策。 　　研究期問是以民國八十四年至八十六年的資料為分析的對象，研究的結果顯示： 　　一、在季報（半年報、年報）公告前公佈的財務預測，好消息會引起融資顯著增加，融券增加幅度雖不如融資大，但結果亦為顯著；壞消息會使融資及融券同樣顯著增加，但融資增加幅度亦較融券顯著。 　　二、在季報（半年報、年報）公告後公佈的財務預測，好消息會引起融資顯著增加，融券增加幅度雖不如融黃大，但結果亦為顯著；壞消息會使融資及融券同樣顯著增加，但融資增加幅度亦較融券顯著。 / This study aims to examine the relationship between an announcement of voluntary forecasts and credit transactions, including margin and short transactions. In general, an announcement of good news would attract investor to employ margin for a long position, and vice versa. Since only noisy trader can employ credit transaction in Taiwan, this study hypothesizes that investors would follow the announcement for making rational expectation. The results of this study could help understand how noisy traders use a financial forecast. This study selects the samples occurred between 1995 and 1997 to test the established hypotheses. 　　The empirical results can be summarized as follows. 　　●If the announcement of voluntary forecast occurred prior to the release of quarterly, semiannual, and annual reports, both good and bad news simultaneously cause an increase of margin and short transactions during this period. However, the magnitude of margin transactions is significantly higher than that of short transactions. 　　●If the announcement of voluntary forecast occurred subsequent to the release of quarterly, semiannual, and annual reports, both good and bad news simultaneously cause an increase of margin and short transactions during this period; however, the magnitude of margin transaction is significantly higher than that of short transaction. 　　Since noisy traders are essentially information followers, their judgement significantly relates to functional efficiency of informational intermediaries. These empirical results imply the function of informational intermediaries requires further improvement.

<p>Background: An evaluation of analysts´ forecasting ability is interesting since their estimates constitute an important part in stock valuation and investment decisions. The recent years´ development in the stock market has lead to criticism of analysts’ deficient forecasts. </p><p>Purpose: The purpose of this thesis is to evaluate analysts´ forecasting ability concerning companies quoted at Stockholmsbörsen between 1987 and 2002. We also intend to discuss possible explanations for analysts’ behavior in case of deficient accuracy. </p><p>Method: Regression analysis is used to compare consensus estimates of earnings per share to actual earnings per share. We attempt to investigate the existence of a relation between forecasting ability and forecast horizon, the volatility at Stockholmsbörsen and the industry in which the firm operates. Behavioral finance and economic incentives is used to discuss the most convincing explanations to analysts´ behavior in cases of deficient accuracy. </p><p>Result: The study indicates over optimistic forecasts and overreaction to earnings information. Analysts tend to give more accurate forecasts closer to earnings announcement. We believe that herding, economic incentives and the fact that analysts get information from the company explains a significant part of analysts’ behavior. In addition, the study shows a possible relation between more accurate forecasts and lower volatility. Concerning industries we find stronger overreaction in healthcare and heavy industry. The study shows the most exceptional optimism in consumer goods/services and IT/telecom.</p>

<p>Since the 1950s, oil has been the dominant source of energy in the world. The cheap supply of oil has been the engine for economic growth in the western world. Since future oil demand is expected to increase, the question to what extent future production will be available is important. </p><p>The belief in a soon peak production of oil is fueled by increasing oil prices. However, the reliability of the oil price as a single parameter can be questioned, as earlier times of high prices have occurred without having anything to do with a lack of oil. Instead, giant oil fields, the largest oil fields in the world, can be used as a parameter.</p><p>A giant oil field contains at least 500 million barrels of recoverable oil. Only 507, or 1 % of the total number of fields, are giants. Their contribution is striking: over 60 % of the 2005 production and about 65 % of the global ultimate recoverable reserve (URR). </p><p>However, giant fields are something of the past since a majority of the largest giant fields are over 50 years old and the discovery trend of less giant fields with smaller volumes is clear. A large number of the largest giant fields are found in the countries surrounding the Persian Gulf. </p><p>The domination of giant fields in global oil production confirms a concept where they govern future production. A model, based on past annual production and URR, has been developed to forecast future production from giant fields. The results, in combination with forecasts on new field developments, heavy oil and oil sand, are used to predict future oil production.</p><p>In all scenarios, peak oil occurs at about the same time as the giant fields peak. The worst-case scenario sees a peak in 2008 and the best-case scenario, following a 1.4 % demand growth, peaks in 2018.</p>

Background: An evaluation of analysts´ forecasting ability is interesting since their estimates constitute an important part in stock valuation and investment decisions. The recent years´ development in the stock market has lead to criticism of analysts’ deficient forecasts. Purpose: The purpose of this thesis is to evaluate analysts´ forecasting ability concerning companies quoted at Stockholmsbörsen between 1987 and 2002. We also intend to discuss possible explanations for analysts’ behavior in case of deficient accuracy. Method: Regression analysis is used to compare consensus estimates of earnings per share to actual earnings per share. We attempt to investigate the existence of a relation between forecasting ability and forecast horizon, the volatility at Stockholmsbörsen and the industry in which the firm operates. Behavioral finance and economic incentives is used to discuss the most convincing explanations to analysts´ behavior in cases of deficient accuracy. Result: The study indicates over optimistic forecasts and overreaction to earnings information. Analysts tend to give more accurate forecasts closer to earnings announcement. We believe that herding, economic incentives and the fact that analysts get information from the company explains a significant part of analysts’ behavior. In addition, the study shows a possible relation between more accurate forecasts and lower volatility. Concerning industries we find stronger overreaction in healthcare and heavy industry. The study shows the most exceptional optimism in consumer goods/services and IT/telecom.

Since the 1950s, oil has been the dominant source of energy in the world. The cheap supply of oil has been the engine for economic growth in the western world. Since future oil demand is expected to increase, the question to what extent future production will be available is important. The belief in a soon peak production of oil is fueled by increasing oil prices. However, the reliability of the oil price as a single parameter can be questioned, as earlier times of high prices have occurred without having anything to do with a lack of oil. Instead, giant oil fields, the largest oil fields in the world, can be used as a parameter. A giant oil field contains at least 500 million barrels of recoverable oil. Only 507, or 1 % of the total number of fields, are giants. Their contribution is striking: over 60 % of the 2005 production and about 65 % of the global ultimate recoverable reserve (URR). However, giant fields are something of the past since a majority of the largest giant fields are over 50 years old and the discovery trend of less giant fields with smaller volumes is clear. A large number of the largest giant fields are found in the countries surrounding the Persian Gulf. The domination of giant fields in global oil production confirms a concept where they govern future production. A model, based on past annual production and URR, has been developed to forecast future production from giant fields. The results, in combination with forecasts on new field developments, heavy oil and oil sand, are used to predict future oil production. In all scenarios, peak oil occurs at about the same time as the giant fields peak. The worst-case scenario sees a peak in 2008 and the best-case scenario, following a 1.4 % demand growth, peaks in 2018.

This paper uses co-integration method and error-correction model to re-examine the relationship between real exchange rate and expected interest rate differentials, including cumulated current account balance, over floating exchange rate periods. As indicated by the dynamic model, I find that there is a long run relationship among the variables using Johansen co-integration method. Final conclusion is that the empirical evidence is provided to show that our error-correction model leads to a good real exchange rate forecast.

Inflation is one of the most important macroeconomic variables. It is vital that policy makers receive accurate forecasts of inflation so that they can adjust their monetary policy to attain stability in the economy which has been shown to lead to economic growth. The purpose of this study is to model inflation and evaluate if applying the Kalman filter to SARIMA models lead to higher forecast accuracy compared to just using the SARIMA model. The Box-Jenkins approach to SARIMA modelling is used to obtain well-fitted SARIMA models and then to use a subset of observations to estimate a SARIMA model on which the Kalman filter is applied for the rest of the observations. These models are identified and then estimated with the use of monthly inflation for Luxembourg, Mexico, Portugal and Switzerland with the target to use them for forecasting. The accuracy of the forecasts are then evaluated with the error measures mean squared error (MSE), mean average deviation (MAD), mean average percentage error (MAPE) and the statistic Theil's U. For all countries these measures indicate that the Kalman filtered model yield more accurate forecasts. The significance of these differences are then evaluated with the Diebold-Mariano test for which only the difference in forecast accuracy of Swiss inflation is proven significant. Thus, applying the Kalman filter to SARIMA models with the target to obtain forecasts of monthly inflation seem to lead to higher or at least not lower predictive accuracy for the monthly inflation of these countries.