Contributions published at Quantitative Business Administration

This paper presents a new method for improving the performance of trend-following trading strategies. This new approach improves the inherent problem of trend-following strategies, which is their lagging signals. We simulate alternative price paths of financial assets using a modification of a distribution-free, semi-parametric approach that combines a GARCH-type process with historical simulation. These simulated price paths are used to construct and optimize trend-following trading strategies. The study is conducted in a multi-asset environment. Our empirical results demonstrate the superior performance for multiple assets on a large set of performance metrics compared to widely applied trend-following trading strategies. The results are robust to variations in input specifications, such as tested time and lookback period, number of simulated price paths, and price steps per simulation, but also in terms of trading strategy calibration and market positioning (long-only, long–short, short-only).

We propose a general emulation method for constructing low-dimensional approximations of complex dynamic climate models. Our method uses artificially designed uncorrelated CO2 emissions scenarios, which are much better suited for the construction of an emulator than are conventional emissions scenarios. We apply our method to the climate model MAGICC to approximate the impact of emissions on global temperature. Comparing the temperature forecasts of MAGICC and our emulator, we show that the average relative out-of-sample forecast errors in the low-dimensional emulation models are below 2%. Our emulator offers an avenue to merge modern macroeconomic models with complex dynamic climate models.

AIMS OF THE STUDY Healthy adults have had the option to receive prescriptionless vaccination against influenza in pharmacies of several Swiss cantons since the 2015/16 influenza season. We aimed to assess in a cost-benefit analysis the resulting net benefits for the Swiss economy and public health, and the benefits that could be expected if an extension of the current vaccination recommendations was implemented. METHODS The proportion of influenza vaccines administered in pharmacies was calculated from data provided by pharmacies entering information in phS-net.ch, data from vaccines covered by insurance companies, and vaccine supply data. The economic and public health impact was estimated in a cost-benefit analysis based on published data. RESULTS In the 2016/17 and 2017/18 influenza seasons, 7306 of a total of 1.07 million (0.7%) and 15,617 of a total of 1.15 million (1.4%) influenza vaccine doses, respectively, were administered in pharmacies in Switzerland. The net cost savings for the economy due to vaccination in pharmacies in the 2016/17 and 2017/18 seasons were CHF 66,633 and CHF 143,021, respectively. In the 2017/18 season, this resulted –in a net saving per 100,000 inhabitants of CHF 1918, 94.4 cases of illness, 17.6 visits to primary care physicians, 0.328 hospitalisations, 1.1 hospitalisation days, 0.019 deaths prevented, and 0.353 life-years gained. Influenza vaccination proved to be cost-effective provided that a vaccine efficacy of 59% is exceeded. Extrapolations for the healthy, working-age population revealed that a vaccination coverage rate of 50% and a vaccine efficacy of 70% could save the Swiss economy CHF 18.4 million annually. CONCLUSIONS The service allowing citizens to receive influenza vaccination in Swiss pharmacies is sparsely used. Since influenza vaccination is cost-beneficial as soon as vaccine efficacy surpasses a critical threshold, an extension of the vaccine recommendation for healthy, working-age adults should be considered from an economic point of view.

This paper offers two composite bond market factor investment strategies each for the Swiss bond market and for the global sovereign bond market. These composite factor strategies can be useful tools when making tactical asset allocation decisions between bonds and cash, and they can act as a base for the duration debate. As such, the output of our bond market factors can guide tactical interest rate views and therefore interest rate risk management. To construct these composite factors, we use four economically meaningful individual factors. Following an investment strategy based on a composite bond market factor, constructed as the equally weighted average of individual components, we are able to outperform cash as well as the staticbuy-and-hold strategy with regard to the Sharpe ratio, annualized standard deviation and maximum drawdown. Testing the composite and individual factors on their performance during periods of historical rising interest rates, we observe improved drawdown results compared with holding the underlying asset passively.

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The probability of a miner finding a valid block in the bitcoin blockchain is assumed to follow the Poisson distribution. However, simple, descriptive, statistical analysis reveals that blocks requiring a lot of time to find — long blocks — are won only by miners with a relatively higher hash power per second. This suggests that relatively bigger miners might have an advantage with regard to winning long blocks, which can be understood as a sort of “within block learning”. Modelling the bitcoin mining problem as a race, and by means of a multinomial logit model, we can reject that the time spent mining a particular block does not affect the probability of a miner finding a valid version of this block in a manner that is proportional to her size. Further, we postulate that the probability of a miner finding a valid block is governed by the negative hypergeometric distribution. This would explain the descriptive statistics that emerge from the data and be aligned with the technical aspects of bitcoin mining. We draw an analogy between bitcoin mining and the classical “urn problem” in statistics to sustain our theory. This result can have important consequences for the miners of proof-of-work cryptocurrencies in general, and for the bitcoin mining community in particular.

In this paper, we formulate the likelihood function of structural models as a parametric optimization problem, where the model equations enter as constraints, forming a mathematical program with equilibrium constraints (Su and Judd, 2012). We trace the solution to its first-order conditions in dependence on a controlled parameter using homotopy continuation, delivering a relation from the controlled parameter to the corresponding maximum likelihood estimates and their confidence intervals. This enables us to estimate models with identification issues, multiplicity of equilibria, etc. As applications, we first trace the parameter estimates of the bus engine replacement model of Rust (1987), a dynamic discrete choice model, in dependence of the discount factor β. Using relative value iteration, we find that β is well identified and statistically significantly larger than 1. Second, for a simple static binary choice model, we demonstrate how the effects of multiplicity of equilibria and a lack of identification can be mitigated by the tracing method.

Users’ data has become a crucial production factor for companies and a necessary asset if they are to compete in the digital ecosystem. However, users’ data is a production factor that is not mobile across companies, since a company can only use the data that its customers—its “users”— generate within its own environment and not the data that its users produce outside of it. This represents a market friction that hinders competition, leads to monopolies, and raises the entry barriers for new companies. Additionally, the users generating and owning the data stored in a company have no control over or overview of their data and cannot monetize it. We model the users’ data as a production factor in the value generation function of companies and introduce the concept of data elasticity of value. Further, and in light of advances in distributed database management, blockchain technology, and data protection regulation, we propose an information system that allows users to sell their data freely to companies other than those within which the data was generated, receiving a self-generated, market-driven basic income. A consequence of this system is that data becomes a mobile production factor, since any company can work with the data that its users generate outside of that company’s own environments. Moreover, our system solves some of the data- ownership problems of the current Internet business model, lowers the entry barriers for new data-intensive companies, and enables new income streams for data-intensive companies, which in the case of online platforms allows them to avoid a dependence on online advertisement to finance their operations. We propose this ecosystem at a conceptual level and simulate the impact of companies having access to higher fractions of their users’ data under different data elasticities of value. We show that the introduction of such a system could theoretically, and under the taken assumptions, more than double the aggregated value of data-intensive companies.

Systems to improve the Know Your Customer (KYC) process using blockchain technology have only been proposed at a conceptual level and they all share some attributes that make their adoption by financial institutions (FIs) very difficult. We propose and program a blockchain-based system that reduces and shares out, among the financial institutions that work with a customer, the costs of the KYC process, and also makes it possible for FIs to dynamically update the information related to the customers and for this information to be disseminated among participating FIs. Additionally, our system solves some of the attributes that hinder the adoption of previous solutions by FIs. The result is a programed, stand-alone solution that can be implemented by FIs to reduce the cost of the KYC process without requiring any central instance to store the customer’s data, and in which FIs share the initial costs of the KYC process, as well as the running costs of keeping the information about the customers up to date. Our system increases the level of security and regulatory compliance in the KYC process, and significantly reduces its costs for all parties involved.

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This paper develops a method to efficiently estimate hidden Markov models with continuous latent variables using maximum likelihood estimation. To evaluate the (marginal) likelihood function, I decompose the integral over the unobserved state variables into a series of lower dimensional integrals, and recursively approximate them using numerical quadrature and interpolation. I show that this procedure has very favorable numerical properties: First, the computational complexity grows linearly in the number of periods, making the integration over hundreds and thousands of periods feasible. Second, I prove that the numerical error accumulates sublinearly in the number of time periods integrated, so the total error can be well controlled for a very large number of periods using, for example, Gaussian quadrature and Chebyshev polynomials. I apply this method to the bus engine replacement model of Rust [Econometrica 55(5): 999–1033] to verify the accuracy and speed of the procedure in both actual and simulated data sets.

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Blockchain technology offers new opportunities for the development of central bank digital currencies (CBDCs). Although discussion on the matter is still in its early stages, researchers and practitioners have proposed possible frameworks via which to explore the potential of this new form of money for central banks and governments. Since blockchain technology is very broad, central banks can conceive of many different blockchain types to sustain CBDC, and the decisions taken by a central bank at a technical level determine the economic possibilities of the resulting monetary system. In other words, the technical attributes of a blockchain have crucial implications for the monetary system that such a blockchain might sustain. In this article, we propose a grid that identifies nine fundamental technical dimensions to be assessed by central banks when establishing a digital currency system based on blockchain technology, and that analyzes the different implications for the central bank as it moves through each of the identified dimensions. Our objective is to offer this grid as a tool to aid in the structured, conceptual, and technical development of national currencies based on blockchain. By way of illustration, we use the grid to analyze three practical scenarios that significantly vary in their implications for the monetary system.