An effective risk management program requires reliable risk measurement. Failure to assess inherited risks in mortgage-backed securities in the U.S. market contributed to the financial crisis of 2007–2008, which has prompted government regulators to pay greater attention to controlling risk in banks, investment funds, credit unions, and other financial institutions to prevent bankruptcy and financial crisis in the future. In order to calculate risk in a reliable manner, this thesis has focused on the statistical modeling of expected return and volatility. The primary aim of this study is to propose a framework, based on the probabilistic reduction approach, to reliably quantify market risk using statistical models and historical data. Particular emphasis is placed on the importance of the validity of the probabilistic assumptions in risk measurement by demonstrating how a statistically misspecified model will lead the evaluation of risk astray. The concept of market risk is explained by discussing the narrow definition of risk in a financial context and its evaluation and implications for financial management. After highlighting empirical evidence and discussing the limitations of the ARCH-GARCH-type volatility models using exchange rate and stock market data, we proposed Student's t Autoregressive models to estimate expected return and volatility to measure risk, using Value at Risk (VaR) and Expected Shortfall (ES). The misspecification testing analysis shows that our proposed models can adequately capture the chance regularities in exchange rates and stock indexes data and give a reliable estimation of regression and skedastic functions used in risk measurement. According to empirical findings, the COVID-19 pandemic in the first quarter of 2020 posed an enormous risk to global financial markets. The risk in financial markets returned to levels prior to the COVID-19 pandemic in 2021, after COVID-19 vaccine distribution started in developed countries. / Doctor of Philosophy / Reliable risk measurement is necessary for any effective risk management program. Hence, the primary purpose of this dissertation was to propose a framework to quantify market risk using statistical models and historical data, with a particular emphasis placed on checking the validity of probabilistic assumptions underlying models. After discussing the concept of market risk and its evaluation methods in financial management, we explored the empirical evidence in financial data and highlighted some limitations of other well-known modeling approaches. In order to ameliorate limitations, this study proposed Student's t Autoregressive models to estimate the conditional mean and the conditional variance of the financial variables and use them to measure risk via two popular methods: Value at Risk (VaR) and Expected Shortfall (ES). Further investigation shows that our proposed models can adequately model exchange rates and stock indexes data and give reliable estimations to use in risk measurement. We used our model to quantify risk in global financial markets in recent years. The results show that the COVID-19 pandemic posed an enormous risk to global financial markets in the first quarter of 2020. In 2021, the level of risk in financial markets returned to levels before the COVID-19 pandemic, after COVID-19 vaccine distribution started in developed countries.

The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC. / A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.

This thesis contains three main parts. In the first part, we adapt a model developed for the US economy to the unique Chinese economic and institutional context. The uniqueness is mainly from two perspectives: the dual-channel housing financing system in China and the existence of the shadow banking sector (which differs from the shadow banking in developed economies) in China’s housing market. It would be difficult to obtain a clear picture of the Chinese housing market and macroeconomy without a thorough understanding of these two characteristics. This is due to the crucial role played by shadow banking and other informal finance institutions within the context of China in both the development and purchase of housing, in supporting productive economic activities in general, and that the housing market is in turn intricately connected to the health of the Chinese economy, being a key ‘barometer’. The second part of the research is the quantification of the determinants of the scale of shadow banking in China. The quantification is crucial since policy makers need to be aware of how sensitive shadow banking is to various factors. We develop a theoretical framework to explain the evolution of the scale of shadow banking in China. As part of this research, we investigate whether the real interest rate of household saving deposits, the required reserve ratio and bank loans to business and household are the main factors in explaining the evolution of China’s shadow banking. In the third part of research, we employ a credit risk and macroeconomic stress test to investigate the vulnerability of the commercial banks in China. Our originality here is the integration of both the role of shadow banking and housing market related loans in the commercial banks’ stress test scenarios at the macro level. Since a systematic analysis regarding the effect of changes in the macroeconomy and housing market on the credit risk of commercial banks in China is scarce, we use bank stress tests to analyse the credit risk in terms of the non-performing loans ratio of commercial banks in China; this is in response to changes in the macroeconomic factors and housing market. We address the role of the variation of the scale of shadow banking in China in terms of its contribution to the credit risk because of its uncertainty and close link with the commercial banks. Stress tests often focus on a single bank or financial institution yet we apply the same principles to examine the financial system as a whole in China, which would allow us to quantify the systemic risk in the entire Chinese financial system; and which variables, especially shadow banking contribute to the risks and by how much. This thesis contributes to the understanding of how China’s dual-channel housing finance system and shadow banking affect the evolution of house prices; and also, the main driving factors of the scale of China’s shadow banking and whether the housing market related loans and shadow banking pose risks to commercial banks. Possible research questions raised by the main findings of this thesis will enrich the debate on China’s housing market, shadow banking and regular banks, especially at a time when China is reforming its economic structure.

This study investigates the long-term and short-term dynamic relationships among the variables of stock returns and institutional investors' buy-sell difference in Taiwan's stock market for the sample periods from Jan., 2000 through May, 2009. Some econometrical methodologies are used in this study, such as unit test, vector autoregressive model, cointegration test, vector error correction model, impulse response function. The major empirical results are shown as follows: 1. Cointegration test For the sample periods, one long-term equilibrium relationship is found from the Johansen's cointegration test, significantly with 5% confidence level between stock year returns and the buy-sell difference for the foreign investment institutions, the domestic investment institutions, and the dealers. The long-term equilibrium relationship is Ry=1.65*QFII+4.28*FUND+35.22*DLR-1142.6. 2. VECM estimation (1)With the vector error correction model (VECM) being applied to the sample periods, the findings indicate that the changes of stock returns are not influenced among the short-term dynamic relationships by the changes of institutional investors' buy-sell difference, but only affected by one-period-lag of itself. (2) Among the short-term dynamic relationships, the changes of foreign investment institutions' buy-sell difference are affected by one-period-lag of institutional investors that positively affected by one-period-lag of the dealers, and inversely affected by one-period-lag of itself and one-period-lag of the domestic investment institutions. However, it is positively affected by one-period-lag of long-term equilibrium, which indicates foreign investment institutions follow positive feedback trading strategies. (3)The changes of the domestic investment institutions' buy-sell difference are only affected by one-period-lag of itself among the short-term dynamic relationships. (4)The changes of the dealers' buy-sell difference are positively affected among the short-term dynamic relationships by one-period-lag of the foreign investment institutions. As for the long-term relationships, it is affected by one-period-lag of long-term equilibrium, which also indicates the dealers follow positive feedback trading strategies. (5)The foreign investment institutions and the dealers have the mutual feedback relationship.

Bakgrund: I november 2014 beslutade Riksbanken att ta steget mot en nollränta och i februari 2015 gick Riksbanken ut med ytterligare en sänkning till -0,10 procent. På så vis fick Sverige för första gången en negativ reporänta. Enligt makroekonomisk teori ska en sänkning av reporäntan stimulera konsumtion och investeringar i ekonomin. Huruvida reporäntan och dess räntesänkningar skapar förutsättningar för företag att investera är ett aktuellt och viktigt forskningsområde. Forskningen i ämnet är tunn på den svenska marknaden och således är forskningsbidraget från denna studie av betydelse.Syfte: Syftet med studien är att undersöka och analysera hur förändringar i reporänta, makro-ekonomiska faktorer samt finansiella indikatorer påverkar investeringar hos svenska företag.Genomförande: Studien bygger på en kvantitativ metod. En Vector Autoregressive model har skapats för att redogöra hur reporäntan, de makroekonomiska faktorerna och de finansiella indikatorerna påverkar företagens investeringar. För att möjliggöra en analys av dessa effekter har impulse response functions skattats i modellen. På så vis undersöks det hur en isolerad enhetsökning i de valda variablerna påverkar företagens investeringar över flera tidsperioder. För att genomföra en mer omfattande analys skattas tre modeller där den första tar hänsyn till både makroekonomiska faktorer och finansiella indikatorer. Den andra modellen exkluderar de finansiella indikatorerna och den tredje modellen speglar reporäntans utveckling i två olika tidsperioder.Resultat: Företagens investeringar påverkas av flertalet faktorer. En enhetsökning av utlåningsräntan, växelkursen och företagens inflationsförväntningar uppvisar ett signifikant negativt samband. En enhetsökning av BNP-tillväxten visar däremot ett signifikant positivt samband. Reporäntan visar ingen direkt effekt på investeringar i de första två modellerna. Däremot uppvisar reporäntan skillnader i den tredje modellen, där ett negativt samband förekommer i den första av de två observerade tidsperioderna. / Background: The central bank of Sweden decided in November 2014 to set the repo rate close to zero. Further they decided to lower the repo rate to -0,10 percent in February 2015. In regard to this, Sweden had a negative repo rate for the first time. According to macroeconomic theory a decrease in the repo rate is performed to stimulate an economy’s investments and consumptions. Whether or not a decrease in interest rates gives greater incentives for firms to invest is a topical subject and an important field of research. In addition to this, the existing research on the Swedish market is insufficient within this field, which gives us further motives to conduct this study.Aim: The purpose of this study is to examine and analyse how changes in the repo rate, macroeconomic factors and financial indicators affects investments of Swedish firms.Completion: The study is conducted with a quantitative approach. A Vector Autoregressive model is created in order to examine the impact of changes in the repo rate, the macroeconomic factors and the financial indicators on firms’ investments. Impulse response functions are estimated to allow a further analysis of these effects. Hence, it is conceivable to examine how one isolated unit-increase in a specific variable affects firms’ investment through several time periods. Furthermore, we estimate three models, one which includes both macroeconomic variables and financial indicators and another which excludes the financial indicators. The last model reflects the repo rate’s impact on investments in two separate time periods.Result: Investments of firms are affected by numerous of factors. One unit-increase of the lending rate, the exchange rate and firms’ expectations of inflation exhibit a negative relation to investments. Furthermore, one unit-increase in GDP-growth tends to increase investments. However, the repo rate has no impact on investments in the first two models. In spite of this, evidence from the third model indicates that the repo rate has a negative impact on investments during the first period.

The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC. / A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.

My thesis will focus on optimal investment decisions, especially those that are planned for longer investment horizon. I will review the literature, showing that changes in investment opportunities can alter the risk-return tradeoff over time and that asset return predictability has an important effect on the variance and correlation structure of returns on bonds, stocks and T bills across investment horizons. The main attention will be given to pension funds, which are institutional investors with relatively long investment horizon. I will find the term structure of risk-return tradeoff in the empirical part of this paper. Later on I will add some variables into the model and investigate whether it can improve the results. Finally the optimal investment strategies will be constructed for various levels of risk tolerance and the results will be compared with strategies of Czech pension funds. I am going to use data from Thomson Reuters Datastream, Wharton Research Data Services and additionally from some other sources.

No description available.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.