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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.
The paper took a macro perspective to investigate the credit risk described by a number of major macroeconomics factors for the two major German retail banks (i.e. Deutsche Bank AG, Commerz Bank AG) in the past 10 years. The paper assessed the link between the macroeconomics and loan quality. The AR(1) macroeconomic credit risk models is applied to both banks in a aggregated and individual level. Both the sensitivity and the scenario analysis have been conducted. After shocking the macroeconomic factors in the model, the out –of -sample forecast suggests that the macro factors which have the most significant influence on the credit risk are household debt ratio, the import value and the DAX index price return.
This master thesis explores the spillovers and the dynamic conditional correlation between the Renewable energy and S&P 500 as well as Renewable energy and Oil & Gas indices for the period from October 2012 to December 2016. Its main purpose is to give an overview on the diversification potential of the Renewable energy.
A quantitative approach is adopted in the research. First, a cointegration analysis is implemented. The results from the Johansen’s test suggested that there are no co-integrated vectors in the long term between the Renewable energy and the other two markets, which implies that these markets do not share the same stochastic trend in the long run. At the same time, the linear dependencies obtained from the VAR(1) model suggest that in the short run there are evidence of spillovers from the equity to the Renewable energy market. In order to analyse conditional correlation in two high volatility periods – the Oil price shock in 2014 and the Chinese market turbulence in 2015, this master thesis utilizes a Dynamic Conditional Correlation model (DCC). The results imply an increase in the correlation between the Renewable energy and S&P 500 Equity and Renewable energy and the Oil & Gas index during the Chinese market turbulence and the Oil price shock.
The study has a number of implications for portfolio managers, policy makers and academic scholars.
Stock trading is a challenging decision-making problem that involves stock selection and asset management. Due to the complexity of stock market data, development of efficient models for trading that work well in the domain of out of sample data is very difficult.
Deep multilayer perceptron is trained to predict if a given company beats S&P 500 index in trading 30 days. The signal from the neural network is used for portfolio creation and trading strategy. The performance of the method is benchmarked against S&P 500, Markowitz
portfolio with Buy-and-Hold strategy as well as with rebalancing. Deep learning based portfolios outperform, as measured by accumulated return, Sharpe ratio. However, they underperform in the case of maximum drawdown, while still exhibiting better Calmar ratio than all the benchmarks .
Are Real Estate Investment Trust considered a safe investment choice in times of financial shock?
(2021)
This paper focuses on real estate investment trust (REIT), one specific asset class in the real estate sector, and intends to answer the following research question: are real estate investment trusts a safe choice for investors, particularly during financial shocks? This study re-examines the relationship between public-traded U.S. REIT and other asset classes by implementing statistical dependence analysis and OLS regression to determine whether and how they follow other markets in times of ambiguity. This study's final results indicate that public-traded U.S. REIT investment should not be considered a safe investment during crisis times because it follows the equity market.
Diese Studie analysiert mit Hilfe der Event-Study-Methodik die Eigengeschäftsmeldungen von 2018 bis 2021 der im Prime Standard gelisteten deutschen Unternehmen. Die finale Stichprobe besteht aus 1370 Kauf- und 231 Verkaufsmeldungen von 194 Unternehmen. Es werden signifikant positive durchschnittliche abnormale Renditen nach Kaufmeldungen und signifikant Negative nach Verkaufsmeldungen ermittelt. Die Untersuchung bestätigt damit die vom Gesetzgeber angenommene Signalwirkung des Eigenhandels von Führungskräften für den deutschen Aktienmarkt. Bei einer Haltedauer von 21 Tagen nach dem Ereignis ist die Reaktion auf Verkaufsmeldungen (3,10 %) deutlich größer als die Reaktion auf Kaufmeldungen (2,23 %). Die durchgeführte Regressionsanalyse, bei der die kumulierte abnormale Rendite (CAR) die abhängige Variable ist, führt weiterhin zu folgenden Ergebnissen: Ein höherer Bekanntheitsgrad des Unternehmens, gemessen an der Anzahl der Suchanfragen im Monat vor dem Ereignis, wirkt sich längerfristig signifikant positiv auf die Höhe der CAR aus. Demnach erhöht ein hoher Bekanntheitsgrad die positive Marktreaktion nach Kaufmeldungen und verringert die negative Marktreaktion nach Verkaufsmeldungen. Die Branchenanalyse zeigt, dass sich die Marktreaktion nur in wenigen Fällen signifikant zwischen den Branchen unterscheidet. Es erscheint daher wenig sinnvoll, sich bei der Analyse von Meldungen auf bestimmte Branchen zu konzentrieren.
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.