International Finance M.Sc.
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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.