1,155 research outputs found

    Real Option Valuation of a Portfolio of Oil Projects

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    Various methodologies exist for valuing companies and their projects. We address the problem of valuing a portfolio of projects within companies that have infrequent, large and volatile cash flows. Examples of this type of company exist in oil exploration and development and we will use this example to illustrate our analysis throughout the thesis. The theoretical interest in this problem lies in modeling the sources of risk in the projects and their different interactions within each project. Initially we look at the advantages of real options analysis and compare this approach with more traditional valuation methods, highlighting strengths and weaknesses ofeach approach in the light ofthe thesis problem. We give the background to the stages in an oil exploration and development project and identify the main common sources of risk, for example commodity prices. We discuss the appropriate representation for oil prices; in short, do oil prices behave more like equities or more like interest rates? The appropriate representation is used to model oil price as a source ofrisk. A real option valuation model based on market uncertainty (in the form of oil price risk) and geological uncertainty (reserve volume uncertainty) is presented and tested for two different oil projects. Finally, a methodology to measure the inter-relationship between oil price and other sources of risk such as interest rates is proposed using copula methods.Imperial Users onl

    Price volatility forecasts for agricultural commodities:an application of volatility models,option implieds and composite approaches forfutures prices of corn and wheat

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    There has been substantial research effort aimed to forecast futures price return volatilities of financial and commodity assets. Some part of this research focuses on the performance of time-series models (in particular ARCH models) versus option implied volatility models. A significant part of the literature related to this topic shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of volatility forecast models for the case of corn and wheat futures price returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), an option implied and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. The results show that the option implied model is superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-square-errors. Given these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied. In addition, the results of this paper are consistent to that part of the literature that emphasizes the difficulty on being accurate about forecasting asset price return volatility. This is because the explanatory power (coefficient of determination) calculated in the forecast regressions were relatively low.Agricultural commodities, BEKK model, multivariate GARCH, Samuelson hypothesis, theory of storage.

    Systemic risk in the financial sector: what can we learn from option markets? : [version 12 july 2013]

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    In this paper, we propose a novel approach on how to estimate systemic risk and identify its key determinants. For all US financial companies with publicly traded equity options, we extract their option-implied value-at-risks (VaRs) and measure the spillover effects between individual company VaRs and the option-implied VaR of an US financial index. First, we study the spillover effect of increasing company risks on the financial sector. Second, we analyze which companies are most affected if the tail risk of the financial sector increases. We find that key accounting and market valuation metrics such as size, leverage, balance sheet composition, market-to-book ratio and earnings have a significant influence on the systemic risk profile of a financial institution. In contrast to earlier studies, the employed panel vector autoregression (PVAR) estimator allows for a causal interpretation of the results

    The Impact of Warrant Introduction Australian Experience

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    The impact that derivative trading has on the underlying security is essential to our understanding of security market behaviour, and important in the fields of market efficiency and pricing of such derivatives. This paper examines the impact that the introduction of exchange traded derivative warrants has on the underlying securities’ price, volume and volatility in the Australian market. The major findings of significant negative abnormal returns, reduction in skewness, no change in beta and small changes in variance are consistent with recent research findings in the US, UK and Hong Kong. However findings of derivative warrant listing resulting in decreased trading volume in contrast with most prior research in the field.Derivatives, Warrants, Market Efficiency, Event Study.

    Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets

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    Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar’s (2006) framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1), GARCH(3,1), and GARCH (1,1), respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.Volatility Forecasting, Capital Market, Risk Management

    Risk and Volatility: Econometric Models and Financial Practice

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    The advantage of knowing about risks is that we can change our behavior to avoid them. Of course, it is easily observed that to avoid all risks would be impossible; it might entail no flying, no driving, no walking, eating and drinking only healthy foods and never being touched by sunshine. Even a bath could be dangerous. I could not receive this prize if I sought to avoid all risks. There are some risks we choose to take because the benefits from taking them exceed the possible costs. Optimal behavior takes risks that are worthwhile. This is the central paradigm of finance; we must take risks to achieve rewards but not all risks are equally rewarded. Both the risks and the rewards are in the future, so it is the expectation of loss that is balanced against the expectation of reward. Thus we optimize our behavior, and in particular our portfolio, to maximize rewards and minimize risks.time series;

    UK Housing Market: Time Series Processes with Independent and Identically Distributed Residuals

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    The paper examines whether a univariate data generating process can be identified which explains the data by having residuals that are independent and identically distributed, as verified by the BDS test. The stationary first differenced natural log quarterly house price index is regressed, initially with a constant variance and then with a conditional variance. The only regression function that produces independent and identically distributed standardised residuals is a mean process based on a pure random walk format with Exponential GARCH in mean for the conditional variance. There is an indication of an asymmetric volatility feedback effect but higher frequency data is required to confirm this. There could be scope for forecasting the index but this is tempered by the reduction in the power of the BDS test if there is a non-linear conditional variance process

    Stock returns, volume and stock price volatility : An empirical firm-level analysis

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    This paper examines the relation between stock returns and stock market volatility in an autoregressive conditional heteroskedasticity model framework. Using a GARCH-M model, we examine the relation between stock returns, volume and stock price volatility. Using daily returns from January 1990 until December 1999 for a sample of 20 firms listed on the Tokyo Stock Exchange, first of all, we examine if there exists a risk premium for stock return volatility. Second, using daily volume and a new measure of daily stock price volatility as a proxy for the amount of daily arrival of information, we try to find out how contemporaneous and lagged trading volume and volatility explain conditional volatility. As a result we find that (1) stock returns are positively related to the conditional variance but the correlation is not always significant. Only when introducing contemporaneous volume in the variance equation, the GARCH parameter in the mean equation becomes significant; (2) contemporaneous trading volume is positively correlated to the conditional variance and highly statistically significant, while lagged trading volume has a mixed impact on the conditional variance; (3) we find evidence that our new measure of stock price volatility using the daily high, low and closing price can catch information in return volatility. Both contemporaneous and lagged stock price volatility are positively related with the conditional variance and are highly significant. Volatility models for daily returns are therefore improved by including information such as the daily high and low price. Together with volume our measure of stock price volatility can be very useful in explaining volatility clustering in daily returns; (4) introducing stock price volatility and volume in the GARCH variance equation reduces the persistence and significance of variance considerably but does not turn them insignificant. After controlling for the rate of information flow using volume and volatility, lagged squared residuals still contribute additional information about the variance of the stock return process. This is in contrast with the research of Lamoureux and Lastrapes (1990) who found empirical evidence that the ARCH effects vanish when volume is included as an explanatory variable in the conditional variance equation.松谷勉教授古稀記念特

    The informational content of over-the-counter currency options

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    Financial decision makers often consider the information in currency option valuations when making assessments about future exchange rates. The purpose of this paper is to systematically assess the quality of option based volatility, interval and density forecasts. We use a unique dataset consisting of over 10 years of daily data on over-the-counter currency option prices. We find that the OTC implied volatilities explain a much larger share of the variation in realized volatility than previously found using market-traded options. Finally, we find that wide-range interval and density forecasts are often misspecified whereas narrow-range interval forecasts are well specified. JEL Classification: G13, G14, C22, C53Density, forecasting, FX, Interval, Volatility
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