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Studies on the empirical validation of beta-return relationship postulated in the CAPM has a relatively long history. The first study was conducted by John Lintner in 1965, shortly after the introduction of the model. In one of the recent studies, Malcolm Baker (forthcoming) examines two portfolios, one consisting of the 30% of U.S. stocks with the lowest beta, another of the 30% with the highest beta. By the end of the period, the low-beta portfolio significantly outperforms the high-beta portfolio (Economist, 2016).
Following two recognized testing methods developed by Lintner (1965) and Fama/MacBeth (1973), this bachelor thesis examines the nature of the relationship between beta and return for the German stock market. The sample periods observed in this study range from January 1973 to May 2016 depending on the testing method. The obtained results are then compared with the outcome of the previous studies for the European stock market. The analysis reveals that the relationship between beta and return is insignificant in most of the models. These results are consistent with the European stock market, if the unconditional cross-sectional analysis is considered.
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.