The thesis evaluates the benefit of allowing for long memory volatility dynamics in forecasts of the variance-covariance matrix for asset allocation. First, I compare the forecast performance of multivariate long memory conditional volatility models (the long memory EWMA, long memory EWMA-DCC, FIGARCH-DCC and Component GARCH-DCC models) with that of short memory conditional volatility models (the short memory EWMA and GARCH-DCC models), using the asset allocation framework of Engle and Colacito (2006). The research reports two main findings. First, for longer horizon forecasts, long memory volatility models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory volatility models. Second, the two parsimonious long memory EWMA models outperform the other models – both short memory and long memory – in a majority of cases across all forecast horizons. These results apply to both low and high dimensional covariance matrices with both low and high correlation assets, and are robust to the choice of estimation window. The research then evaluates the application of multivariate long memory conditional volatility models in dynamic asset allocation, applying the volatility timing procedure of Fleming et al. (2001). The research consistently identifies the economic gains from incorporating long memory volatility dynamics in investment decisions. Investors are willing to pay to switch from the static to the dynamic strategies, and especially from the short memory volatility timing to the long memory volatility timing strategies across both short and long investment horizons. Among the long memory conditional volatility models, the two parsimonious long memory EWMA models, again, generally produce the most superior portfolios. When transaction costs are taken into account, the gains from the daily rebalanced dynamic portfolios deteriorate; however, it is still worth implementing the dynamic strategies at lower rebalancing frequencies. The results are robust to estimation error in expected returns, the choice of risk aversion coefficients and the use of a long-only constraint. To control for estimation error in forecasts of the long memory high dimensional covariance matrix, the research develops a dynamic long memory factor (the Orthogonal Factor Long Memory, or OFLM) model by embedding the univariate long memory EWMA model of Zumbach (2006) into an orthogonal factor structure. The factor-structured OFLM model is evaluated against the six above multivariate conditional volatility models in terms of forecast performance and economic benefits. The results suggest that the OFLM model generally produces impressive forecasts over both short and long forecast horizons. In the volatility timing framework, portfolios constructed with the OFLM model consistently dominate the static and other dynamic volatility timing portfolios in all rebalancing frequencies. Particularly, the outperformance of the factor-structured OFLM model to the fully estimated LM-EWMA model confirms the advantage of the factor structure in reducing estimation error. The factor structure also significantly reduces transaction costs, making the dynamic strategies more feasible in practice. The dynamic factor long memory volatility model also consistently produces more superior portfolios than those produced by the traditional unconditional factor and the dynamic factor short memory volatility models.

This paper explores the relationship between Chinese rare earth metals (REMs) and the industries in the U.S and Europe that heavily rely on them. The study uses the EGARCH(1,1)-ARMA(1,0) process for conditional volatility and incorporates it into VAR(8) framework for forecast error variance decomposition to evaluate the static and dynamic volatility spillovers using daily data from the 2nd of January 2018 to the 3rd of March 2023. The liaison of risk management is also consolidated through the incorporation of Value at Risk and Event Study. Our findings indicate that the volatility interconnectedness between the Chinese REMs market and computer and electronics, electric vehicle, and wind energy industries exhibits relatively low volatility spillover to and from each other. Value at Risk measures suggests complexity in assessing the potential short-term losses for REM equity, leading to difficulties in risk management. Establishing and utilizing a derivatives market could be beneficial for future notice. However, the study also highlights that severe geopolitical risk or conflict could enable extreme levels of financial risk due to the global supply dominance of the Chinese quasi-monopolistic construct and the elements' overall criticality in the sustainable energy transition. The study also highlights the infeasibility of Western nations decoupling themselves from the Chinese REM supply. Various factors such as the pace of advancement in sourcing alternatives, technological advancements, and recycling technology are the main drivers of ineligibility. The forecasted global demand for REMs is also expected to increase significantly, primarily driven by the renewable and sustainable energy transition worldwide, further straining the possibility of independence. Therefore, the pace of advancement of these factors must collectively supersede that of the forecasted demand to mitigate the risk. Keywords: Rare Earth Metals, Interconnectedness, Conditional Volatility, Risk Management, Value at Risk, Event Study.

Focusing on Environmental, Social and Governance (ESG) responsible investments, this study examines the historical and forecasted volatility and dynamic correlations between Emerging Markets in Europe, Asia and Latin America. By complementing the previous studies that provide evidence for how high ESG-ratings can reduce volatility in stock prices, regardless of which market, we seek to find if this is true in Emerging Markets. We additionally incorporate an analysis of dynamic correlations between Emerging Markets to see potential diversification benefits, which can be crucial in risk management. Data selection is based on daily closing prices of six different Emerging Markets indices. Three indices capturing the traditional Emerging Markets and three more only consisting of firms with a high ESG-rating, considered to be ESG Leaders. The sampled period is between January 2020 to January 2024. Data was processed through the DCC- GARCH(1,1) model to measure historical and forecasted volatility and dynamic correlations. The model uses past information to predict future values, meaning that past volatility and correlations influence forecasted volatility and correlations. This allows for a nuanced understanding of how the volatility and correlations have evolved and how they are forecast to change between these Emerging Markets. Key findings suggest that Asia can work as the diversification benefactor, as it is the least volatile Emerging Market and the ESG Leaders in Asia are showing a lower dynamic correlation with the ESG Leaders in the other Emerging Markets. Further results indicate that Europe is the most volatile Emerging Market, including the ESG Leaders. Furthermore, ESG Leaders in Europe and Latin America were seen to have the best DCC-GARCH filtered daily returns, while also having the highest dynamic correlation. This means that a portfolio with these two assets tends to be more volatile as shocks in daily returns move in tandem.

We examined the influence of news, related to the main central banks, on the conditional volatility of the stock returns of eighteen major European banks. We model their conditional volatility with GARCH, EGARCH and TGARCH models plugging in variables representing news. As a practical application we evaluate whether applying the news into the volatility modeling improves the performance of the Value-at-Risk (VaR) measure for given banks. The two types of news variables we use are constructed from the press releases of main central banks and from the search query at Factiva Dow Jones news database. The information contained in news is proxied by daily news counts. Using the EGARCH setup we are able to model individual volatility reaction functions of the banks' stock returns to different news variables. We show that the content, origin of the news and also the amount of news (news count) matter to the conditional volatility behavior. The results confirm that increase in the amount of media coverage causes increase in volatility. Certain news types have calming effect (speeches of the central banks' representatives) on volatility while others stir it (monetary news). Finally, we conclude that adding the news into the modeling only slightly improves the VaR out-of-sample performance.

The impact of the introduction of derivatives on the underlying stock is a debatable topic among the researchers. The issue is quite controversial as contradictory results have been obtained by researchers in various stock markets. The purpose of this study is to examine the volatility and the liquidity effect on the underlying stock after the introduction of index options. We have investigated volatility and liquidity effect by collecting sample data from the stock markets of India, Korea, Taiwan, Hong Kong, Japan, Thailand, Malaysia and Singapore, only markets which are offering index options in Asia.   Applying the generalized autoregressive conditional heteroscedasticity (GARCH) model, we have examined the conditional volatility of intraday (high frequency) returns for each stock market, before and after the introduction of index options. We have also examined the liquidity effect through t-test and Wilcoxon Signed Rank Test. We used t-test to determine the mean differences between the trading volume of pre-index and post-index options periods.    By comparing the estimated parameters and the coefficient of conditional volatility in pre and post period of index options introductions, we have examined that the derivatives trading dramatically increases the persistence of the conditional volatility for all the selected stock markets. We also observed mixed evidence in context to liquidity effect. In the stock exchanges of Hong Kong, Japan, Korea, Taiwan and Thailand, we found that the respective markets become more liquid in the post index options periods in contrast to pre index options period. In these markets trading volume increased significantly after the introduction of index options.  On the other hand, India, Malaysia and Singapore stock markets show no liquidity effect in the post-index option period.   Finally, the empirical results of our study conclude that the introduction of index options on the selected Asian stock markets have increased in stock return volatility and liquidity on the underlying stocks.

In this dissertation, several essays in the field of bio-security are presented. The estimation of the probability of an FMD outbreak by type and location of premises is important for decision making. In Essay I, we estimate and predict the probability/risk of an FMD outbreak spreading to the various premises in the study area. We first used a Poisson regression model with adjustment dispersion associated with random simulation results from the AusSpead model to estimate the parameters of the model. Our estimation and prediction show that large cattle loss could be concentrated in three counties-Deaf Smith, Parmer, and Castro. These results are based on approximately 70% of the feedlots with over 10,000 cattle located in the three counties previously mentioned. In Essay II, our objective is to determine the best mitigation strategies in minimizing animal loss based on AusSpead simulation model. We tested 15 mitigation strategies by using multiple comparison. The results show that the best mitigation strategies for all four scenarios are regular surveillance, slaughter of the infected animals, and early detection. We then used the Mixed Integer Programming to estimate costs of disposing of animal carcasses and transportation. Results show that the unit disposal cost will vary with carcass scale and the unit transportation cost also varies with the distribution of the infected premises and disposal locations. FMD seems to have varying impacts on equity markets. In Essay III, we studied returns at three different levels of the stock market. We determined results in a structural break, and then estimated the impact of the announcement of confirmed cases of FMD disease on the volatility of stock market returns by using a GARCH-Mean model. Our results show that the structure break occurs on the day with the largest number of confirmed cases for meat product firms rather than the day of the first confirmed case. We found that the conditional volatilities over the FMD period are higher than those over the sample period. The announcement of confirmed cases had the largest marginal impact on meat products. Investors may always consider maintaining a portfolio consisting of index funds or hedge funds.

Most of the prior research in the area of monthly regularities has been based on the Gregorian calendar; by contrast, little attention has been given to other calendars based on different religions or cultures. This thesis examines monthly calendar anomalies in the Pakistani stock market for both the Gregorian calendar and its Islamic counterpart. This is one of the first studies to investigate both calendars for monthly seasonality in one investigation on the same dataset. Empirical studies of the Pakistani stock market that have examined monthly calendar anomalies are relatively sparse when compared with investigations from other emerging markets throughout the world. Even the findings from the small number of Pakistani investigations that have examined for the presence of monthly calendar anomalies have arrived at different conclusions about the predictability of equity returns at different times within a year. Since the conclusions of these findings have been mixed, the current study undertakes further work on this topic to offer some clarity in this area; this thesis arrives at a firm conclusion about the monthly calendar anomaly. For the purpose of this thesis, both qualitative and quantitative research methods were employed. Firstly, 19 face-to-face interviews were conducted with brokers, regulators and individual investors to ascertain their views about share price regularities with regards to monthly calendar anomalies and to gain some insights about the role of investor sentiment in the Pakistani stock markets. Secondly, share returns for a sample of 106 companies listed on the KSE over the 17 year period from 1995 to 2011 were analysed to determine whether Pakistani stock markets are weak-form efficient or whether security price changes can be predicted from knowledge of the month when the return is earned; it also investigates whether there is a change in the risk (volatility) of shares in different months which might explain any pattern in returns. To answer these questions various research methods were employed. The results of the interviews suggest that most respondents believed that share prices exhibit patterns in certain months of the year. The most common pattern highlighted by the interviewees related to the month of January for the Gregorian calendar and Ramadan for the Islamic calendar. Interviewees also argued that volatility declined during the religious month of Ramadan; they attributed these changes to investor sentiment and religious duties. Overall, the results suggested that monthly calendar anomalies may be present in the market and that these are studied by investors in an attempt to earn profit. The results from the quantitative analyses supported the findings from the interviews. Initial analyses suggested that returns varied significantly during certain months which indicate that the market might not be efficient. Further, investigations for seasonality in both the mean and volatility of returns offered conflicting evidence; very little statistical evidence of monthly seasonal anomalies was identified in average returns. However, monthly patterns were present in the variance of equity price changes in Pakistan. Overall, the results confirm that whatever monthly seasonality may be present in the equity prices of Pakistani companies, it is more pronounced in the volatility data than in the mean return numbers. These findings may have useful implications for trading strategies and investment decisions; investors may look to gain from managing the risk of their portfolios due to time varying volatility documented in the findings of this thesis. Further, the results of this thesis have interesting implications for our understanding of the dynamics of equity volatility in the Pakistani stock market.

nÃo hà / As economias emergentes que compÃem os BRIC, apesar de serem caracterizadas por heterogeneidades marcantes em termos econÃmicos, sociais e polÃticos, apresentam evidÃncias empÃricas sobre convergÃncia parcial e integraÃÃo financeira. Neste sentido, este trabalho agrega a discussÃo sobre gestÃo de risco dos principais Ãndices de mercado dos BRIC atravÃs do Value at Risk, em sua versÃo paramÃtrica gaussiana incondicional e extensÃes que acomodam as violaÃÃes sobre a nÃo normalidade e a heterocedasticidade dos retornos diÃrios. Corroborando estudos especÃficos para cada economia, Jianshe (2007) para o mercado chinÃs, Karmakar (2005) para o indiano e Thupayagale (2010) para o russo, evidencia-se ser necessÃrio adaptar o arcabouÃo visando modelar a idiossincrasia estatÃstica da sÃrie temporal dos Ãndices, recorrendo a valores crÃticos associados à distribuiÃÃo de probabilidade mais adequada, alÃm da modelagem da evoluÃÃo condicional do risco. O trabalho ainda oferece uma mÃtrica dinÃmica de performance risco-retorno dos Ãndices sob a Ãtica dos investidores locais. / Although the bloc labeled BRIC is composed of emerging economies characterized by heterogeneity in economic, social and political aspects, there are empirical evidences about the convergence and partial financial integration. In this sense, we address the risk management of most relevant BRIC market indices through Value at Risk approach, based on a parametric Gaussian and unconditional version, and also extending it intending to accommodate violations of heteroscedasticity and non-normality of daily returns. Corroborating previous and specific evidences, as Jianshe (2007) for the Chinese market, Karmakar (2005) for the Indian and Thupayagale (2010) for Russian, we are able to show that it is necessary to adapt the canonical framework, because of the statistical idiosyncrasies of time series, using the critical values related to the best fitting probability distribution, and modeling the evolution of the conditional risk. We also provide a dynamic measure of risk-return performance of theses indices from the perspective of local investors.

nÃo hà / O presente trabalho busca analisar, empiricamente, o comportamento do modelo de mensuraÃÃo de risco de mercado Value-at-Risk â VaR em sua interpretaÃÃo paramÃtrica gaussiana incondicional e extensÃes que regulam as violaÃÃes sobre a nÃo normalidade e a heterocedasticidade dos retornos diÃrios dos fundos de investimentos em AÃÃes, das treze maiores instituiÃÃes financeiras residentes no Brasil, durante o perÃodo de janeiro/06 a dezembro/12. Para uma melhor avaliaÃÃo dos dados, buscou-se, inicialmente, modelar a evoluÃÃo condicional do risco e ajustar a idiossincrasia estatÃstica das sÃries temporais das treze tesourarias, utilizando distribuiÃÃes de probabilidade que mais se adaptassem à anÃlise dos modelos. Os resultados obtidos com esses modelos sÃo analisados à luz do teste para proporÃÃo de falhas proposto por Kupiec (1995) e Chisttoffersen (1998). A pesquisa ainda apresenta, com exemplos grÃficos, uma anÃlise de desempenho Risco â Retorno dos treze bancos utilizando a metodologia proposta por Balzer. / This study aims to examine empirically the behavior of the model for measuring market risk Value at Risk - VaR in its parametric interpretation unconditional Gaussian and extensions that regulate violations on heteroscedasticity and non-normality of daily returns of investment funds Actions, of the thirteen largest financial institutions resident in Brazil, during the January/06 dezembro/12. For a better evaluation of the data, we sought to initially model the conditional evolution of risk and adjust the statistic al idiosyncrasy of temporal series of thirteen treasuries, using probability distributions that best adapt to the analysis of the models. The results obtained with the semodels are analyzed by the test failure rate proposed by Kupiec (1995) and Chisttoffersen (1998). The survey also shows, with graphic examples, a performance Risk - Return of the thirteen banks using the methodology proposed by Balzer.

The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting ac- curacy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index sepa- rately. Unlike other works in this eld of study, the thesis does not assume the log-returns to be normally distributed and does not explicitly select a partic- ular conditional volatility process. Moreover, the thesis takes advantage of a less known conditional coverage framework for the measurement of forecasting accuracy.