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The digital currency Bitcoin offers investors high returns
and a low correlation with other asset classes. However, Bit-
coin's unusually high volatility raises doubt about its eligibility
for investors.
The objective of this thesis is to ascertain, if the market risk
of Bitcoin can be adequately forecasted with the prevalent risk
measures Value-at-Risk and Expected Shortfall. To this end, an
empirical analysis is performed, which applies commonly used
techniques of risk modeling on seven years of Bitcoin return
data. Forecasts for VaR and ES are backtested and the results
compared with those of other asset classes.
The empirical results show, that although Bitcoin's fore-
casts perform significantly worse than those of other asset classes,
risk models with conditional volatility are able to estimate re-
liable VaR and ES for Bitcoin. Other findings incluce the inef-
fectiveness of historical simulation models and the importance
of the assumed distribution of returns.
In countries such as the UK and Australia, Public-private partnerships (PPPs) have become the preferred tool for the public sector to procure infrastructure. With this development it has become necessary to assess PPPs regarding their success, and it seems that research has been insufficient to this point. This thesis will give an in-depth insight into the field of success measurement for PPPs. First, it explains the theory behind PPPs by explaining the theory. Then, in the next step, a framework for success measurement in PPPs will be derived based on this theory. It will serve as the basis for the Success Measurement System (SMS) that will be developed. In this regard, the thesis adds value to existing research by using a fully encompassing approach. The SMS will allow for a sound measurement of success for PPPs, which includes a measurement of success for different stakeholders, different phases and categories.
In the main part of this thesis, the system is applied on eight transport PPPs through case studies. The insights, from these case studies, will be used to answer the following questions: First, are PPPs in general successful and should governments continue their implementation? Second, in which areas do PPPs fail? This is especially relevant to improve outcomes of PPPs in the future and to enhance PPP policies. Third, how can PPPs be compared with each other? In this regard, the results of the case studies will serve as a benchmark for future PPPs.
In regard of the first and the second question, the research has shown that PPPs are in general successful but often fail to meet cost and time targets. In addition, sometimes problems were encountered during the procurement phase, contract management and risk allocation. These are the areas where the public and the private sector should focus to improve outcome of future PPPs. The results of the SMS assessment led to an average success rate which can be used as a benchmark to compare outcomes of other PPPs.
Despite its simplicity, the yield curve is one of the best predictors of future economic activity. Empirical studies suggest that the yield curve is capable of forecasting recessions in major economies. In this paper, the relationship between the yield curve and stock bear markets will be studied with the focus on predicting bear markets in the U.S. and Germany. Also this paper seeks to answer the question if a market-timing strategy, based on yield-curve information, can outperform the market.
The results of this study suggest that for the U.S. the spread between 10-year and 1-year interest rates outperforms other spreads in predicting bear markets. Furthermore, the yield spread can be used to profitably time the market and outperform a buy-and-hold strategy.
For the Germany yield curve, the study has found a statistical significant relationship between the yield curve and bear markets. However, depending on the observation period, the forecasting ability differs tremendously. For the entire period, the yield curve was not able to predict local bear markets reliably, nor was it possible to use the information contained in the yield curve to outperform the stock market.
The aim of this study is to analyze if Turkish firms apply a market timing strategy on their financing decisions and to analyze the persistence of the market timing decision on their capital structure. The sample data of this study contains the 85 initial public offers from 2010 to 2015 in Istanbul Stock Exchange(BIST). Regression analysis method is used for testing the relationship between market timing and equity issues, and short-run and long-run effect of market timing. The year before IPO and the three subsequent years after the IPO considered for analyzing the impact of market timing. The results of this study show that there is a positive relationship between market timing and equity issues. Firms that are decided to go public in “hot” market periods, issue more equites and reduce their leverage ratio sharply right after the IPO, and this relationship shows an impact on capital structure only in short run. This short-term impact of market timing starts vanishing after the second year of going public.
Most of the studies on factor models have been based on the U.S. stock market. Results of these studies have shown that conventional factor models explain the vast part of its return variation and that each of these models strengthens the descriptive power of the traditional CAPM. However, when applying factor models on the stock markets of emerging countries, studies have yielded rather mixed results. Even though most of them find that conventional factor models explain a major part of their returns, they argue that other country-specific risk factors should also be considered, in order derive a model that describes their returns in the most comprehensive way. For this reason, this paper investigates the significance of both common and specific risk factors, in order to find which are the most important risk factors that impact the performance of the stock markets in the BRICS countries. The effect of systematic co-moments is also tested.
Empirical evidence shows that the market premium is the most significant risk factor for all of the BRICS countries. Additionally, the value, size and momentum factors are proven to be insignificant, whereas the investment and profitability factors are among the main determinants of stock returns. Specific factors also appear to be more important than the common one. Moreover, the popular extensions of the CAPM and higher-order moments do not substantially strengthen its descriptive power. Additionally, conventional factor models explain a lower part of the return variation of the BRICS countries compared to the U.S. stock market, indicating that they do not possess the same explanatory power in developed and emerging markets.
After the fall of Lehman Brothers, systemic risk which triggers the whole financial system has gained more attentions from researchers. Recently, graph theory is applied to measure this risk. DebtRank algorithm is one of the network based models which illustrates
the on going shock propagation when no default occurs. Knowingly the important of systemic risk, this paper captures the broad picture of a potential interbank network of ASEAN region in the recent period 2013-2017 using DebtRank. This is done by assign a stress of different levels on external assets of banks by two scenarios: simultaneously and individually. Networks are constructed based on probabilities and the desired density of the network is found to be 10% of total possible links. The first case shows that the studied banking system in ASEAN is stable as systemic risk has the falling pattern. However,
the rising figure in 2017 implies the system in this year is less stale than previous years.
Moreover, the declining of loss caused by contagion is due to the decrease of interconnectedness and rise in capital during this period. Furthermore, the total loss calculated in a range of external shocks is a concave curve. In the second scenario, the results show that the most harmful banks are, at the same time, most fragile one. Besides, there are evidence of high dependence of the individual impact on systemic risk of the system and the vulnerability on bank size and connectivity.
The FinTech industry is very dynamic and the multitude of innovative business models created by these new entrants has increased over the past years. FinTechs display a competitive advantage in the field of technology, agility and customer-centricity that traditional banks cannot compete with. However, the regulatory requirements often pose a challenge for FinTechs to grow and expand their business models. Nevertheless, in recent years the phenomenon of BaFin licensed FinTechs such as N26 or Solaris Bank became apparent in Germany.
Since this topic is of high actuality no studies can be found on the topic of licensed FinTechs yet. Hence, this research paper will examine how the business models of these BaFin licensed FinTechs is constructed and what impact financial licenses by the BaFin can have on these business models and their positioning in the financial services industry. The focus thereby lies on FinTechs with a BaFin license active in the B2B-sector only. Based on an interview series conducted with relevant experts from different licensed FinTechs the findings show that the financial license allows these firms to become an independent entity, expand their product offering and strengthen their market position in the financial services industry.
In this research Cash Conversion Cycle was used to examine working capital management in the supply chain of the automotive industry during 2006–2016. The results of this study reveal patterns and trends in working capital developments over time, which can be used for further managerial analyses and strategy planning as for the single company and for the supply chain level. There was no substantial change in the overall cycle time for the whole industry, but rather the changes happened among the supply chain representatives. During the economic downturn bigger companies took over the credit risk and capital costs, by extending their days sales outstanding. Overall trends show strong regional differences in the levels of Cash Conversion Cycle and its elements.