Contributions published at Financial Economics (Felix Kübler)

I conduct a longitude study of the carry trade with 2008 as the cutting point. Employing a sample with 17 currencies and over 30 years since 1993, I find that the basic carry trade strategy with G10 currencies does underperform after 2008 relative to itself before 2008 and relative to the market. I demonstrate that refined carry strategies including basic carry with an extended currency basket, carry momentum, and volatility-adjusted carry can avoid this downturn. I observe that overall traditional risk factors fail to explain the excess return of the carry trade. However, I show that during certain periods more significance can be observed, and higher returns are associated with a lower and more negative correlation with the market. Keywords: Carry, Momentum, Investment Strategy, Factor Model

This paper investigates the relationship between stock liquidity and the corresponding ADR shares discount using a sample of cross-listed firms in both the A shares market in China and the ADR market in the USA. The liquidity hypothesis is examined by introducing the market depth variables. The result of this study indicates that a smaller difference in stock liquidity between A shares and ADR is related to a lower corresponding discount in the ADR market. Such an effect is validated by the subperiod check. Overall, this study highlights that market liquidity of cross-listed stocks explains a proportion of the variation in price disparity that long existed in the ADR market. Meanwhile, a stock-specific factor for the price disparity is also proposed in this paper.

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We consider dynamic stochastic economies with heterogeneous agents and introduce the concept of uniformly self-justified equilibria (USJE) - temporary equilibria for which expectations satisfy the following rationality requirements: i) individuals' forecasting functions for the next period's endogenous variables are assumed to lie in a compact, finite-dimensional set of functions, and ii) the forecasts constitute the best uniform approximation to a selection of the equilibrium correspondence. We show that in contrast to rational expectations equilibria, USJE always exist, and we develop a simple algorithm to compute them. As an application, we discuss a stochastic overlapping generations exchange economy. We give an example where recursive (rational expectations) equilibria fail to exist and explain how to construct USJE for that example. In addition, we provide numerical examples to illustrate our computational method.

This thesis aims to bring finance and machine learning together and examines whether a model can be created that is used to evaluate if the US stock market can be outperformed using solely freely available data excluding paid financial services of data providers entirely. Said examination is done for two time dimensions yearly and quarterly with a focus on both the complete market and a sector-based approach. Different machine learning techniques such as logistic regression, random forest, gradient boosting, and support vector machine are being used and applied for the task at hand. Although the results are less than ideal, the model performance may improve over time when more data is added. However, the sector-based results are more promising due to some sectors scoring higher in the examined model performance metrics. II

We develop a generic and transparent calibration strategy for climate models used in economics. The key idea is to choose the free model parameters to match the output of large-scale Earth System Models, which are run on pre-defined future emissions scenarios and collected in the Coupled Model Intercomparison Project, Phase 5 (CMIP5). We propose to jointly use four different test cases that are considered pivotal in the climate science literature. Two of these tests are highly idealized to allow for the separate quantitative examination of the carbon cycle and the temperature response. Another two tests are closer to the scenarios that arise from economic models. They test the climate module as a whole, that is, they incorporate gradual changes in CO2 emissions, exogenous forcing, and ultimately the temperature response. To illustrate the applicability of our method, we re-calibrate the free parameters of the climate part of the seminal DICE-2016 model for three different CMIP5 model responses: the multi-model mean as well as two other CMIP5 models that exhibit extreme but still permissible equilibrium climate sensitivities. As an additional novelty, our calibrations of DICE-2016 allow for an arbitrary time step in the model explicitly. By applying our comprehensive suite of tests, we show that i) both the temperature equations and the carbon cycle in DICE-2016 are miscalibrated and that ii) by re-calibrating their coefficients, we can match all CMIP5 targets we consider. Finally, we apply the economic model from DICE-2016 in combination with the newly calibrated climate model to compute the social cost of carbon and the optimal warming. We find that in our updated model, the social cost of carbon is similar to DICE-2016. However, the optimal long-run temperature in our calibration lies almost one degree below that obtained by DICE-2016. Moreover, the social cost of carbon turns out to be much less sensitive to the discount rate than in DICE-2016. We explain how the model's climate part relates to these differences and also show that under the optimal mitigation scenario, the temperature predictions of DICE-2016 (in contrast to our proposed calibration) fall outside of the CMIP5 scenarios, suggesting that one might want to be skeptical about policy predictions derived from DICE-2016.

This thesis aims to determine the influence of energy and fertilizer prices on the food basket components within the Swiss consumer price index (CPI). The study builds upon official data released by the World Bank, the administration of Switzerland, and the Food and Agriculture Organization of the United Nations. A Vector Autoregressive Model determines the influence of energy and fertilizer prices and forecasts resulting values. The study’s primary focus is whether a vector autoregressive model can quantify external shocks in the main variables of interest and their effects on the Swiss economy. The analysis finds that energy and fertilizer prices neither Granger cause Swiss food prices’ CPI level nor add benefit to a VAR model presented by the Swiss National Bank to predict Swiss inflation.

Is deficit finance, explicit or implicit, free when borrowing rates are routinely lower than growth rates? Specifically, can the government make all generations better off by perpetually taking from the young and giving to the old? We study this question in simple closed and open economies and show that achieving Pareto gains requires implausible calibrations. Even then, the gains reflect, depending on the economy's openness, improved intergenerational risk-sharing, improved international risk-sharing, and beggaring thy neighbor – not intergenerational redistribution per se. Low government borrowing rates, including borrowing rates running far below growth rates, justify improved risk-sharing between generations and countries. They provide no convincing basis for using deficit finance to redistribute from young and future generations or other countries.

This thesis analyses the current situation of the hedge fund industry to predict its survival in the coming years. The focus is to understand the problems that hedge funds face today and whether there are positive aspects in connection to a hedge fund investment. The thesis starts with a historical overview of the industry and continues by presenting its clientele, its strategies, and the fee structure used by hedge funds. It continues by comparing hedge funds with passive investment alternatives, which are extremely popular today. The comparison is qualitative and quantitative, analysing the last 20 years using data from Bloomberg. The last chapter dives into several topics that influence hedge funds and their relationship with the public. The results suggest that hedge funds can offer their clients value that goes further than just returns, such as great diversification effects and the ability to invest in complicated financial structures and products, proving that there are multiple reasons why an investment in them can be a good idea. This, combined with many external reports that have shown a growing trend for hedge funds in the last couple of years, proves that they will continue to be an important part of the market in the future. The thesis shows various points that should be further developed by hedge funds, such as the fee structure and the relationship with the public

This thesis investigates the Non-proportional thinking (NPT) predictions regarding investors’ behavior in the European stock market. NPT predicts that lower-priced stocks experience more extreme abnormal returns compared to higher-priced stocks due to investors thinking in nominal rather than percentage terms when evaluating the impact of new information. Evidence of a negative relationship between the nominal share price level and abnormal returns around earnings announcement dates is found, however, the extent of engagement in NPT behaviors is estimated to be close to zero for the period and sample under investigation.

Climate change will impact current and future generations in different regions very differently. This paper develops a large-scale, annually calibrated, multi-region, overlapping generations model of climate change to study its heterogeneous effects across space and time. We model the relationship between carbon emissions and the global average temperature based on the latest climate science. Predicated average global temperature is used to determine, via pattern-scaling, region-specific temperatures and damages. Our main focus is determining the carbon policy that delivers present and future mankind the highest uniform percentage welfare gains – arguably the policy with the highest chance of global adoption. Damages from climate change are positive for all regions apart from Russia and Canada, with India and South Asia Pacific suffering the most. The optimal policy is implemented via a time-varying global carbon tax plus region- and generation-specific net transfers. Uniform welfare improving carbon policy can materially limit global emissions, dramatically shorten the use of fossil fuels, and raise the welfare of all current and future agents by over four percent. Unfortunately, the pursuit of carbon policy by individual regions, even large ones, makes only a limited difference. However, coalitions of regions, particularly ones including China, can materially limit carbon emissions.

As digital assets are constantly growing, stablecoins have attracted remarkable attention from central banks regarding financial stability risks. Focusing on 2018-2022, this study uses the DCC-GARCH (1,1) model to analyze the conditional dynamic correlation between stablecoins and traditional financial assets, specifically the energy market, Banks Index, DXY Index, and gold. The results imply that stablecoins significantly correlate with the energy market and the banking sector, while there is no significant co-movement with the DXY Index or gold. Furthermore, the results disclose a strongly increased correlation between all assets during financial market stress. These findings highlight that stablecoins are moving towards integration with the traditional financial market. As policymakers have not yet regulated stablecoins, this study suggests introducing appropriate regulation in the near future.

The model and the approach developed by Chen et al. (2019) are replicated and adapted to the framework of corporate bond expected returns. It relies on machine learning methods and estimates the stochastic discount factor for a set of U.S. corporate bonds using macroeconomic, firm-specific and debt-specific data. The thesis finds that accessing corporate bond data over a long period of time can prove challenging but when the negative effect of limited data is ignored, the model seems to perform even better on corporate bond data than on equity data. Furthermore, the thesis studies the predictive power of each type of input. It confirms that the handling of the macroeconomic data suggested by Chen et al. improve the performances. In addition, it shows that debt-specific data have strong predictive power while firm-specific data have predictive power, it does not bring incremental predictive power to the debt-specific data.

Recent research has placed enormous interest in the accurate prediction of stock prices. By extending conventional regression techniques, this thesis implements the concept of stress testing on an equity level using several machine learning methods, including trees, gradient boosting and neural networks (long short-term memory). The models are adapted using a novel time-series approach intended to preserve stationarity and extend the neural network using a bidirectional layer and weight penalization. Stock price changes are being forecasted and subject to severe scenarios, as commonly used on banking levels. Trees and gradient-boosted models have been identified to yield superior results in terms of common loss metrics. The implementation of macroeconomic shock scenarios reveals that predicted stock price changes are less dependent on severe scenarios using trees and gradient-boosted models; however, they are affected by material changes using neural networks. It is further investigated which macroeconomic parameters are of significant fraction in terms of forecasting stock price changes, whereas inflation, treasury and unemployment rates are among the most robust features.

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This master thesis aims to re-examine the topic of asset pricing anomalies and factor investing in Japan, South Korea and China over the past two decades. The analysis is fourfold: first, the existence of size, value and momentum effects is investigated; second, the performance of the aforementioned factor strategies is analyzed from an investor’s perspective; third, factor exposures and risk premia are estimated using the Fama-MacBeth approach and finally, in order to shed more light and attempt to explain the widely acknowledged failure of momentum in certain countries, on the one hand, a combination of value and momentum strategies is constructed and, on the other hand, the role of the underlying market dynamics is included in the analysis of momentum. I find that over the period studied, size strategies generate excess returns in all three countries. Value effects are present in Japan and South Korea but not in China, while momentum strategies generated high returns in South Korea but do not seem to be profitable in Japan and China. The combined strategy outperforms its pure-play counterparts in all three countries while effectively reducing volatility. In examining the explanatory power of excess returns, none of the factors appear to be priced, hence no significant risk premia could be found. Looking for reasons for the unprofitable momentum strategy in Japan and China, I find that the average momentum returns are highest when the market continues to fall, showing that there are conditional positive momentum returns. Similarly, option-like behavior is observed in both countries when a bearish market rebounds.

As one of the important financial instruments in sustainable finance, the green bond has attracted great attention from investors in the capital market. Based on 146 green bonds issued from 2017-2022, this paper empirically studies the stock market reaction to green bond issuance in China with the event study and cross-sectional regression model. The results imply that the stock market responds positively to the announcement of green bond issuance. Besides, certified green bond issuers have more positive stock market responses than uncertified ones. However, the issuers located within the policy pilot area do not show a better stock market response than issuers located in other areas. These results draw attention to the importance of the information disclosure in green bond policy, as well as the effectiveness of regional incentive policy.

This study examines the short-term, medium-term, and long-term performance of Swiss initial public offerings (IPOs) from 2000 to 2018. After the first day of trading, the IPOs outperform an industry-based benchmark by 5.80% on average. But over the medium and long term, there is a significant underperformance of -10.71% and -14.70% if the outliers of the sample are extracted. After three years (defined here as “long term”) in particular, the results contradict previous findings on Swiss IPOs. This can be attributed either to the outliers that have been removed such as the company EFG Financial Products Holding AG which had a three-year outperformance of 1057.57%, or to methodological issues. It is interesting to see that there is an outperformance across all periods if these outliers are included. While such results are no longer statistically significant, they imply that an investor who participated in every Swiss IPO from 2000 to 2018 equally and sold the shares after three years would have outperformed a Swiss industry-based benchmark. To understand which factors in the IPO process can cause a certain performance pattern afterwards, the public offerings are categorized according to age, size, underpricing, underwriter reputation, IPO volume, and industry. The performance of each subdivision is then analysed over a period of one day, one month, six months, one year, and three years, respectively, and compared to an index that is matched to the corresponding industry of the firm going public. The findings show similar results for four of these categories: age, underwriter reputation, IPO volume, and industry. That is, high benchmark-adjusted returns on the first day result in an underperformance after three years for IPOs which are considered riskier. On the other hand, the other two factors of size and underpricing do not follow this pattern. They record a different relationship where a high adjusted first-day return leads to an even higher long-term adjusted performance. Thus, it can be concluded that size and underpricing are not reliable proxies for ex-ante uncertainty.

Climate change will impact current and future generations in different regions very differently. This paper develops the first large-scale, annually calibrated, multi-region, overlapping generations model of climate change and carbon policy. It features region-specific temperature and damage functions with the phased impact of emissions on global and regional temperature calibrated to the latest scientific evidence. Absent policy, calibrated worst-case damages in the next 200 years reach and remain near 20 percent of GDP for most regions, with India, Brazil, and the South Asian Pacific suffering roughly 40 percent of GDP losses. Russia and Canada benefit somewhat from global warming. Carbon taxation, coupled with region- and generation-specific transfers, can both correct the carbon externality and raise the welfare of all current and future agents across all regions by 4.3 percent. The impact on the use and duration of fossil fuels is dramatic, as is the reduction in the path of global emissions. However, achieving completely uniform welfare gains leaves future generations in particular regions with exceptionally high net taxes. Fortunately, a carbon tax-cum redistribution policy that limits the consumption-equivalent net tax burden on any generation in any region to less than 10 percent can deliver a 4.0 percent or higher welfare gain for all peoplekind – present and future. However, carbon taxes set through time, at carbon’s marginal intertemporal social cost do far too little to mitigate climate change unless all major emitters, particularly China, adopt them and do so immediately.

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