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LIBOR has been the most important figure in the financial systems for almost three decades. As a reference interest rate in mid-2020 linked derivatives amounted to more than USD 600 trillion worth of financial contracts. Since the disclosure of the scandal, its cessation is due with last rates published until June 2023. The crucial benchmark regimes will shift to risk-free overnight rates based on transactions.
This work provides a current analysis of the effects of the scandal and the transition process on the financial industry. It includes the incentive factors leading to the scandal and determining decisions in the ongoing process. Participants were tempted to conduct manipulatively. A design-lack in LIBOR submissions, a moral hazard dilemma, remuneration schemes, and imperfect administrative governance are core reasons for the scandal. As a consequence, new regulative requirements such as the IOSCO principles were established. These requirements, analyzed in this work, are to be fulfilled by the new RFR-based benchmark regime. New models based on the former require efforts to establish sufficient benchmarks. In many cases markets’ acceptance is anticipated. Complexity and challenges lie within the transition from one to the other system. This is reflected in the scope and length of the transition process in terms of consultations and publications.
This work provides current insight into the transition selected proposed approaches. Further research could evaluate the particular valuation issues/approaches of the transition and/or the varying proposals concerning a forward-looking approach based on RFRs. Besides, effects on emerging markets and other jurisdictions dependent on the crucial benchmarks provide related fields of research.
In this study two emerging markets and two developed financial markets are
examined for herd behaviour using a market-wide approach. The emerging market
representatives are Turkey and India whereas the developed market representatives
are Italy and Germany. The model developed by Chang et al. (2000) is used to
detect herd behaviour in the respective markets from January 2008 to September
2020. The four markets of interests are tested for the presence of herding under
different market conditions.
Evidence of market-wide herding is found at the Indian market when examining the
whole sample period. No evidence of market-wide herding for the whole sample
period is detected for Turkey, Italy and Germany. The sample has been separated
into falling and rising market days to detect potential asymmetry of herding
behaviour. Only the Indian market displayed evidence of herding during bullish
market days. Turkey, Italy and Germany showed no evidence of herding neither
during bullish nor bearish market days. Lastly, the sample was split according to
extreme market movements, using the 1% criteria. Evidence suggests that during
extreme market movements the Italian market shows herding patters. However, this
does not apply to Turkey, India and Germany.
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.
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.
Although a numerous reports have been published on the very imminent effects of FinTechs on Banking, there has been no in-depth analysis on individual enterprise level. This thesis focuses on not only the industry level changes caused by the rapid digitization and adoption of the FinTech services in the payment and credit market, but aims to identify the key factors to identify the cause of mass appeal to move away from the more traditional Banking services to the dynamic and ever evolving FinTech services.
This study helps shed light on the thinking behind these new FinTech services, in order to better understand how market opportunities are identified and customized products or services are created helping move the customer base from a traditional to a more revolutionized “banking” services.
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.
This paper adds more information to the existing literature regarding the performance analysis of Directional funds. The findings of this study suggest that both conventional market risks, strategy risks and macro-economic factors are needed for the sake of explaining the returns of Directional funds. By augmenting existing models and creating four new strategy-based factor models this study was able to explain the returns of four Directional strategies. Each of the four analysed Directional strategies applied distinctive investment approaches on diverse asset markets. Therefore, although there are some similarities present between some of the analysed strategies, this paper concluded that each Directional strategy is subject to different risk factors.
The objective of this paper was to determine the effect of macroeconomic variables on the profitability of banks in Germany using the quarterly data from the time period of 1996 to 2018. The data was collected from FRED, OECD and European Central Bank Statistical Data Warehouse. This study used multiple regression to examine the effect of macroeconomic variables (GDP, interest rate spread, share price, unemployment, exchange rate, inflation, credit loan and wage) on the profitability which is measured by ROA. The analysis was conducted in EViews10. The empirical finding from the study suggested that there is a significant relationship between interest rate spread, unemployment, share prices and return on asset. However, there is no significant relationship between GDP, exchange rate, inflation, credit loan, wage and return on asset. Therefore, the banks and government are recommended to implement better policies and monitor the macroeconomic variables to improve the financial performance of banks in Germany.
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.
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.
This paper focuses on the European stock market and the forces that determine the stock price movements on it. As a basis for the analysis, well known and used factor models’ methodology is applied for the investigation and explaining of the variance of the returns on the European stock market. An emphasis in the analysis is put on the description power of fundamental risk factors along with the momentum factor. As a result, five factors show abilities in explaining the returns in Europe. Particularly, the QMJ (Quality minus Junk), SMB (Small minus Big), PE (Price-to-Equity), ILLIQ (Illiquidity) and DE (Debt-to-Equity) show the greatest explanatory power among the overall 27 tested risk factors. Furthermore, a factor model constructed of the five aforementioned risk factors managed to achieve on average the greatest explanatory power when tested with six other famous factor models.
The Efficient Market Hypothesis would lead one to believe that stock markets are perfectly efficient and that abnormal/ deviant average returns are not possible. However, the existence of calendar anomalies empirically shows how specific periods during a week, month and year can influence the average returns in the stock markets. Research from scholarly journals and books, industry-related news sources, and industry-experts shows the occurrence of various calendar anomalies in global markets that led to unbalanced average returns and confronted the fundaments of the Efficient Market
Hypothesis.
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.
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.
The aim of the paper is to implement the SRISK model on systemically relevant European financial institutions in order to investigate the applicableness as well as the usefulness regarding the supervisory of financial institutions. The model is implemented on 28 banks situated in Europe using the SRISK model proposed by Brownlees and Engle.
Half of the banks are considered as systemically relevant and the other half was chosen for reasons of comparison and further investigation. For the computation the CAPM Beta is used due to temporal reasons in the computation of the Dynamic Conditional Beta. It is shown that results defer but a ranking and monitoring is still possible.
Linear concepts are widespread, although imprecise. Despite this, many well-known models in the field of finance are based on linear correlation. Contrary, the implementation of Copulas enables modelling complex nonlinear dependency structures. Additionally, they offer the opportunity to analyze the dependencies between variables more flexible and without the consideration of their marginal distributions. This thesis provides an introduction to Copula theory in general and thereby focuses on the application possibilities in the field of finance. The implementation part shows how Copulas can be used in the field of Portfolio Optimization, which is done with the mathematical programming software Wolfram Mathematica. Optimal asset combinations are compared depending on different dependence structure assumptions.
Foreign diversification has long been used to improve portfolio efficiency through risk reduction. The purpose of this thesis is to analyze the effect of exchange rate volatility on the risk and return of a portfolio invested in Germany.
In examining the gains from international diversification, a portfolio of 5 firms selected from the DAX is analyzed over the period 2015. The risk and return of the domestic German investor is calculated. Then the effects of exchange rate volatility on the risk and return of the portfolio if the investor is from China, Canada and US are analyzed.
The return of the portfolio is 15.7% and the risk is 21.6% over the year 2015 for domestic investor. We find that result of the scenarios does not support the theory. Only the result from Chinese investor’s scenario showed support to the theory. However, for the case of Canadian investor, the volatility of the exchange rate did add 5.64% to the initial portfolio risk but interestingly the return was not compensated for the increase in risk. Moreover, for the case of US investor, the volatility of the exchange rate reduced 5.64% to the initial portfolio risk and the return on investment also increased by 46.13%.
Purchasing Power Parity (PPP) and Interest Rate Parity theory (IRP) were used to
calculate the expected future exchange rate and some hedging strategies was recommend to reduce risk.
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.