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Thomas Walther
Former Member
Title
Prof. Dr.
Last Name
Walther
First name
Thomas
Phone
+41 71 224 2088
Now showing
1 - 10 of 10
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PublicationStranded Asset Risk and Political Uncertainty: The Impact of the Coal Phase-out on the German Coal Industry( 2020)
;Breitenstein, Miriam ;Anke, Carl-Philipp ;Nguyen, Duc KhuongType: working paperVolume: 2020Issue: 2 -
PublicationLet's Talk About Risk! The Firm Value Effect of Risk Disclosure for European Energy Utilities( 2020)
;Düsterhöft, Maximilian ;Schiemann, FrankType: working paper -
PublicationCan Bitcoin Investors Profit from Buy, Hold, and Sell Recommendations by Crypto Analysts?( 2020)
;Gerritsen, Dirk ;Lugtigheid, RickUsing a hand-collected dataset containing buy, hold, and sell recommendations for Bitcoin published by crypto analysts, we show that hold and sell recommendations are followed by negative abnormal returns whereas buy recommendations are not associated with nonzero abnormal returns. Based on all outstanding recommendations, we compute recommendation changes relative to (i) the latest issued recommendation and (ii) the outstanding consensus recommendation. Downgrades are followed by negative abnormal returns. We conclude that crypto analysts are skilled information intermediaries on the Bitcoin market.Type: working paper -
PublicationForecasting Realized Volatility of Crude Oil Futures Prices based on Variable Selection Approaches( 2020)
;Luo, Jiawen ;Ji, Qiang ;Klein, TonyWe augment the HAR model with additional information channels to forecast realized volatility of WTI futures prices. These channels include stock markets, sentiment indices, commodity and FX markets, and text-based Google indices. We then apply four differing machine learning techniques to identify the most suitable endo- and exogenous factors which improve baseline model forecasts. We show that machine learning generated forecasts provide better forecasting quality and that portfolios which are constructed with these forecasts outperform their competing models. We find LASSO and SSVS to provide outperforming forecasts and portfolio weights. Analyzing the selection process, we show that variable choices vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.Type: working paper -
PublicationAsset Classes and Portfolio Diversification: Evidence from a Stochastic Spanning Approach( 2020-10-30)
;Nguyen, D.K. ;Topaloglou, N.We propose a stochastic spanning approach to assess whether a traditional portfolio of stocks and bonds spans augmented portfolios including commodities, foreign exchange, and real estate. We empirically show that in all seven portfolio combinations, the augmented portfolio is not spanned by the traditional one. Our results are further confirmed by both parametric and non-parametric tests in an out-of-sample setting. Therefore, traditional investors can generally benefit in terms of higher Sharpe ratios from augmenting their portfolio with alternative asset classes. Additional analysis demonstrates that diversification benefits can be explained by the current state of the U.S. economy and stock markets.Type: working paper -
PublicationEmpirical Analysis of the Illiquidity Premia of German Real Estate Securities( 2020)
;Paul, ThomasKüster Simic, AndréIn this study, we are the first to analyze the illiquidity premia and their effect on the expected returns of German real estate securities. We show that illiquidity plays an important role in expected returns for real estate stocks and investment trusts (REITs), but have less clear effects on open- and closed-end funds. We also find that evidence of structural breaks in the relation between returns and illiquidity. We use a unique data set which includes real estate stocks, REITs, and open- and closed-end real estate funds for 2003 to 2017, and follow Amihud’s (2002) structural approach. We estimate Amihud's illiquidity factors, investigate the relationships between expected returns and illiquidity, and analyze the effects of expected and unexpected market illiquidity on future returns.Type: working paper -
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PublicationModeling and Forecasting Commodity Market Volatility with Long-Term Economic and Financial VariablesThis paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed GARCH-MIDAS approach which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for Crude Oil (WTI and Brent, Gold, Silver and Platinum as well as a commodity index, our results show the necessity of disentangling the short-term and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of the global real economic activity as well as the changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.Type: working paper
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PublicationExogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach to Forecasting( 2018-06)Klein, TonyWe apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of four highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, and Ripple) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. Only the average forecast combination results in lower loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.Type: working paper