In late 1994 when the Mexican financial crisis occurred, many emerging economies experienced negative returns. For instance, Latin American markets, Hong Kong, Singapore, Korea and Thailand all showed stock price declines of 15% to 30%. Similarly, the Asian Crisis of 1997, the Russian Crisis of 1998, and the Brazilian Crisis of 1999 have all brought about significant negative returns in many seemingly unrelated emerging markets. Based on these figures, it was found worthwhile to study the spread of financial crises among international stock exchanges, in a quest to reach clues on &lsquo / contagious volatility&rsquo / . This study aims to analyze the nature and spread of international financial crises. Remaining within its scope, the study on contagion of volatility studied 72 positive and negative events and reached the conclusion that markets move together in times of crisis, with outstanding increases in their correlations. This finding shows that the benefits of international diversification are reduced because of increasing correlations among markets during events. Another striking finding was that, though at a lesser extent, the same co-movement was observed with upturns in markets. It was also observed that volatility tends to be higher within periods of negative and positive events analyzed in the study. As country groups that move together in crisis periods were analyzed, it seemed that economic and trade linkages were an influencing factor in their behavior. In the light of these findings, the possible mechanisms of the spread of contagion and policies that could be implemented to withstand it were discussed.

In the original Vasicek model interest rates are calculated assuming that volatility remains constant over the period of analysis. In this study, we constructed a stochastic volatility model for interest rates. In our model we assumed not only that interest rate process but also the volatility process for interest rates follows the mean-reverting Vasicek model. We derived the density function for the stochastic element of the interest rate process and reduced this density function to a series form. The parameters of our model were estimated by using the method of moments. Finally, we tested the performance of our model using the data of interest rates in Turkey.

被廣泛應用在選擇權定價的Black-Scholes 模型[3] 時常在深價內與深價外 的選擇權價格有錯價的現象，也就是理論價格估計實際市場價格的偏差。藉由 Black-Scholes 評價公式所反推出的隱含波動度往往不像我們所期待的在不同履約價格具有一致性，這種現象被稱為波動度的微笑曲線。在這份論文裡，我們參考Jarrow and Rudd [13] 提出的方法，將Edgeworth展開式套用在Black-Scholes模型作延伸應用，進而推導出偏態峰態修正後的的評價公式，再利用台指選擇權的市場資料作實證分析並與Filho and Rosenfeld [1] 的研究作比較。我們發現從台指選擇權的實證結果得到非常態分配的隱含偏態和隱含峰態。此外，理論價格的估計偏誤比例顯著的被新的模型改善且隱含波動度的微笑曲線也變的較為平坦，這個方法提供我們一個有效的方法，利用標的資產的偏態峰態得到該資產的近似分配。 / The Black-Scholes [3] option pricing model widely applied in option contracts frequently misprices deep-in-the-money and deep-out-of-the-money options. The implied volatilities computed by the Black-Scholes formula are not identical on each strike price as we expect. This phenomenon is called the volatility smile or skew. In this thesis, we derived a skewness- and kurtosis-adjusted option pricing model using an Edgeworth expansion constructed by Jarrow and Rudd [13] to an investigation of TAIEX option prices and compare the results with those in Filho and Rosenfeld [1]. We found that non-normal skewness and kurtosis are implied by TAIEX option returns. Moreover, the magnitude of price deviations were signicantly corrected and the volatility skew is attened. This approach provides an useful way to derive an approximate distribution of a underlying security with its skewness and kurtosis.

本文主要探討基因演算法(genetic algorithms)與S&P500指數選擇權為研究對象，利用基因演算法的模型來估測選擇權的隱含波動度後，進而求出選擇權的最適價值，用此來比較過去文獻中利用Jump-Diffusion Model、Stochastic Volatility Model與Local Volatility Model來估算選擇權的隱含波動度，使原始BS model中隱含波動度之估測更趨完善。在此篇論文中，以基因演算法求估的選擇權波動度以0.052的平均誤差值優於以Jump-Diffusion Model、Stochastic Volatility Model與Local Volatility Model求出之平均誤差值0.308，因此基因演算法確實可應用於選擇權波動度之求估。 / In this paper a different approach to the BS Model is proposed, by using genetic algorithms a non-parametric procedure for capturing the volatility smile and assess the stability of it. Applying genetic algorithm to this important issue in option pricing illustrates the strengths of our approach. Volatility forecasting is an appropriate task in which to highlight the characteristics of genetic algorithms as it is an important problem with well-accepted benchmark solutions, the models mention in the previous literatures mentioned above. Genetic algorithms have the ability to detect patterns in the conditional mean on both time and stock depend volatility. In addition, the stability test of the genetic algorithm approach will also be accessed. We evaluate the stability of the new approach by examining how well it predicts future option prices. We estimate the volatility function based on the cross-section of reported option prices one week, and then we examine the price deviations from theoretical values one week later.

We examine how investor sentiment affects the changes in implied volatility, and discover investor sentiment has impact on the size of the changes in implied volatility through returns, especially when returns are negative. We examine the short-tern relation between the S&P 500 index returns and the changes of VIX from January 1990 to January 2011, and between the NASDAQ-100 index returns and the changes of VXN from February 2001 to January 2011 with proxy for beginning-of-period investor sentiment at both the daily and weekly level. We find that during high sentiment periods, the negative and asymmetric relation of return to changes in implied volatility can be mitigated significantly. When returns are segregated into positive and negative returns, investor sentiment has different impact on the size of changes in implied volatility. In negative returns, investors are more panic than in positive returns, but the panic can be mitigated significantly when investors are in high sentiment. Thus, sentiment can alter the risk attitude of investors and reduce their panic in the future, especially when market has negative performance.

This thesis characterizes properties of ambient secondary organic aerosol (SOA), an important and abundant component of particulate matter. The findings presented in this thesis are significant because they represent the results from ambient measurements, which are relatively scarce, and because they report on properties of SOA that, until now, were highly uncertain. The analyses utilized the fraction of particulate organic carbon that was soluble in water (WSOCp) to approximate SOA concentrations in two largely different urban environments, Mexico City and Atlanta. In Mexico City, measurements of atmospheric gases and fine particle chemistry were made at a site ~ 30 km down wind of the city center. Using box model analyses and a comparison to ammonium nitrate aerosol, a species whose thermodynamic properties are generally understood, the morning formation and mid-day evaporation of SOA are investigated. In Atlanta, simultaneous measurements of WSOCp and water-soluble organic carbon in the gas phase (WSOCg) were carried out for an entire summer to investigate the sources and partitioning of WSOC. The results suggest that both WSOCp and WSOCg were secondary and biogenic, except possibly in several strong biomass burning events. The gas/particle partitioning of WSOC in Atlanta was investigated through the parameter, Fp, which represented the fraction of WSOC in the particle phase. Factors that appear to influence WSOC partitioning in Atlanta include ambient relative humidity and the WSOCp mass concentration. There was also a relationship between the NOx concentration and Fp, though this was not likely related to the partitioning process. Temperature did not appear to impact Fp, though this may have been due to positive relationships WSOCp and WSOCg each exhibited with temperature. Neither the total Organic Carbon aerosol mass concentration nor the ozone concentration impacted WSOC partitioning.

The corporate scandals in the beginning of the 21st century caused distrust in the market and a pressure for more disclosure to increase transparency. To broaden the traditional reporting, companies started to voluntarily disclose information regarding soft measures like Corporate Social Responsibility (CSR). Due to the fast development and popularity of CSR, more companies started to disclose a separate sustainability report to communicate information about these activities. The aim of the report is to provide stakeholders with accurate and transparent information regarding the companies CSR activities, but also to legitimize the business. The main purpose of this research is to investigate if the quantity of information disclosed in the sustainability report affects the financial performance of companies listed on NASDAQ OMX Stockholm. We also aim to investigate whether the existence of a report affects the financial performance. With companies spending an increasing amount of resources on disclosing voluntary information it is important to extend the research regarding CSR and the benefits to financial performance. This research ontological and epistemological positions are objectivism and positivism with a deductive approach. A quantitative method was used to gather sufficient data from existing databases and reports. For the first research question our population is all companies listed on NASDAQ OMX Stockholm on April 12th 2013, and for the second research question our population is the companies with a separate sustainability report in English from the accounting year of 2011. The financial performance data was gathered from the period 2012-04-01-2013-03.31. To answer our research questions and sub- questions, six hypotheses were formulated based on relevant theories and previous studies. Several multiple linear regression analyses were performed to examine the relationship between the existence of the reports, and the quantity of information in them, to the company’s financial performance. Other regressions were performed to establish if the quantity disclosed was affected by industry classification or market capitalization size. Our results show that the neither the existence of a separate sustainability report nor the quantity of information disclosed in it has an effect on stock return. However, both having a separate sustainability and the quantity of information disclosed have a positive effect on stock volatility. Conclusively, companies do not benefit financially from disclosing their CSR activities through a separate sustainability report.

The aim of this thesis is to improve risk measurement estimation by incorporating extra information in the form of constraint into completely non-parametric smoothing techniques. A similar approach has been applied in empirical likelihood analysis. The method of constraints incorporates bootstrap resampling techniques, in particular, biased bootstrap. This thesis brings together formal estimation methods, empirical information use, and computationally intensive methods. In this thesis, the constraint approach is applied to non-parametric smoothing estimators to improve the estimation or modelling of risk measures. We consider estimation of Value-at-Risk, of intraday volatility for market risk, and of recovery rate densities for credit risk management. Firstly, we study Value-at-Risk (VaR) and Expected Shortfall (ES) estimation. VaR and ES estimation are strongly related to quantile estimation. Hence, tail estimation is of interest in its own right. We employ constrained and unconstrained kernel density estimators to estimate tail distributions, and we estimate quantiles from the fitted tail distribution. The constrained kernel density estimator is an application of the biased bootstrap technique proposed by Hall & Presnell (1998). The estimator that we use for the constrained kernel estimator is the Harrell-Davis (H-D) quantile estimator. We calibrate the performance of the constrained and unconstrained kernel density estimators by estimating tail densities based on samples from Normal and Student-t distributions. We find a significant improvement in fitting heavy tail distributions using the constrained kernel estimator, when used in conjunction with the H-D quantile estimator. We also present an empirical study demonstrating VaR and ES calculation. A credit event in financial markets is defined as the event that a party fails to pay an obligation to another, and credit risk is defined as the measure of uncertainty of such events. Recovery rate, in the credit risk context, is the rate of recuperation when a credit event occurs. It is defined as Recovery rate = 1 - LGD, where LGD is the rate of loss given default. From this point of view, the recovery rate is a key element both for credit risk management and for pricing credit derivatives. Only the credit risk management is considered in this thesis. To avoid strong assumptions about the form of the recovery rate density in current approaches, we propose a non-parametric technique incorporating a mode constraint, with the adjusted Beta kernel employed to estimate the recovery density function. An encouraging result for the constrained Beta kernel estimator is illustrated by a large number of simulations, as genuine data are very confidential and difficult to obtain. Modelling high frequency data is a popular topic in contemporary finance. The intraday volatility patterns of standard indices and market-traded assets have been well documented in the literature. They show that the volatility patterns reflect the different characteristics of different stock markets, such as double U-shaped volatility pattern reported in the Hang Seng Index (HSI). We aim to capture this intraday volatility pattern using a non-parametric regression model. In particular, we propose a constrained function approximation technique to formally test the structure of the pattern and to approximate the location of the anti-mode of the U-shape. We illustrate this methodology on the HSI as an empirical example.

This dissertation investigates the information considerations of volatility, pricediscovery and the relationship change in volume and volatility resulting from index derivatives transactions on financial markets in Australia. The impact of information on volatility was investigated in the essay one, as volatility is a key factor for accurately pricing derivative securities. I assessed the forecast accuracy, unbiasedness and information content of volatility forecasts, based on implied volatility and conditional volatility models for the S&P/ASX 200 Index Options market in Australia. The conditional volatility models produce the most accurate forecasts and are robust when forecasting into short time horizons. Essay two, investigates the information content of the index and option markets in the price-discovery process. Based on the above volatility results, the long-run equilibrium relationship between the share price index and the implied price of the share-price-index option was investigated. Causality was determined to show which market leads the other. Information share measures were used to gauge the contribution of the share price index and index option markets to the price-discovery process. Unambiguous evidence shows the index market leads the options market and the former contributes more to price-discovery than the latter. In essay three, I investigate the dynamic relationship between the future price volatility of the S&P/ASX 200 Index and the trading volume of the S&P/ASX 200 Index Options to explore the informational role of option volume in predicting price volatility. I found the contemporaneous call options volume have a significant strong positive feedback effect on the implied volatility, but the contemporaneous feedback effect of volume on the TARCH volatility is insignificant. The contemporaneous feedback effects from the implied volatility and the TARCH volatility to the call options volume are positive, significant and strong.

This thesis examines the relationship between equity valuation and four value drivers: revenue growth, volatility, profit margin and competitive advantage. It is motivated by evidence that the predominant valuation techniques of equity analysts are not associated with improved portfolio performance. Prior research suggests that equity analysts devote considerable resources into forecasting near-term earnings, but derive target prices from those earnings in an almost arbitrary fashion. In contrast, the valuation techniques in the commercial world are increasing in sophistication. Around 30 percent of large corporations in the United States and Australia use real options analysis for project evaluation, according to recent surveys. Thus, the research question is whether sophisticated equity valuation, based on rigorous economic assumptions, is useful for investment decision-making.