We study an optimal dividend problem for an insurer who simultaneously controls investment weights in a financial market, liability ratio in the insurance business, and dividend payout rate. The insurer seeks an optimal strategy to maximize her expected utility of dividend payments over an infinite horizon. By applying a perturbation approach, we obtain the optimal strategy and the value function in closed form for log and power utility. We conduct an economic analysis to investigate the impact of various model parameters and risk aversion on the insurer's optimal strategy.

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio contains structured products or complex insurance contracts which do not admit closed form valuation formulas. We illustrate the method on different examples from banking and insurance. We focus on value-at-risk and expected shortfall, but the approach also works for other risk measures.

Cryptocurrencies (CCs) become more interesting for institutional investors' strategic asset allocation and will be a fixed component of professional portfolios in future. This asset class differs from established assets especially in terms of the severe manifestation of statistical parameters. The question arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components, based on modern methods from image / pattern recognition.

The Gaussian Process with a deep kernel is an extension of the classic GP regression model and this extended model usually constructs a new kernel function by deploying deep learning techniques like long short-term memory networks. A Gaussian Process with the kernel learned by LSTM, abbreviated as GP-LSTM, has the advantage of capturing the complex dependency of financial sequential data, while retaining the ability of probabilistic inference. However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge. In this paper, grid search algorithm, used for performing hyper-parameter optimization, is integrated with GP-LSTM to predict both the conditional mean and volatility of stock returns, which are then combined together to calculate the conditional Sharpe Ratio for constructing a long-short portfolio. The experiments are performed on a dataset covering all constituents of Shenzhen Stock Exchange Component Index. Based on empirical results, we find that the GP-LSTM model can provide more accurate forecasts in stock returns and volatility, which are jointly evaluated by the performance of constructed portfolios. Further sub-period analysis of the experiment results indicates that the superiority of GP-LSTM model over the benchmark models stems from better performance in highly volatile periods.

Various companies have developed vaccines to combat the pandemic caused 2020 by the virus COVID-19. Such vaccines and the distribution can have a major impact on the success of pharmaceutical companies, which in turn can show itself in their valuation and stock price. This poses the question if and how the trends or popularity of the companies might be connected to the value and stock price of said entities. To gain some insight into these questions, the work at hand looks at five COVID vaccine development companies and evaluates their correlations over the development of the vaccine as well as after the rollout start. The process was conducted by using python including various libraries. The result of this analysis was that there is a significant correlation between the Google Trend data and the respective stock prices (retrieved from yahoo! Finance) of the companies on average, where the time during the development of the drugs is more positively correlated and the post-rollout periods show a shift to a slightly negative inclining correlation. Furthermore, it was found that the smaller companies based on their market cap show a higher price volatility overall. In addition, higher average trend scores and thus popularity values were found after the rollout of the respective companies. In conclusion, a correlations between the trend data and the financial values have been found and corroborate the plots of the data. Due to the small size of the sample, the result cannot yet be considered statistically significant, but possibility for expansion exists and is already being worked on.

Limited memory of decision-makers is often neglected in economic models, although it is reasonable to assume that it significantly influences the models' outcomes. The hidden-action model introduced by Holmstr\"om also includes this assumption. In delegation relationships between a principal and an agent, this model provides the optimal sharing rule for the outcome that optimizes both parties' utilities. This paper introduces an agent-based model of the hidden-action problem that includes limitations in the cognitive capacity of contracting parties. Our analysis mainly focuses on the sensitivity of the principal's and the agent's utilities to the relaxed assumptions. The results indicate that the agent's utility drops with limitations in the principal's cognitive capacity. Also, we find that the agent's cognitive capacity limitations affect neither his nor the principal's utility. Thus, the agent bears all adverse effects resulting from limitations in cognitive capacity.

With almost 50% of annual commercial drug approvals being Personalised Medicine (PM) and its huge potential to improve quality of life, this emerging medical sector has received increased attention from the industry and medical research, driven by health and care services, and us, the patients. Notwithstanding the power of Advanced Therapy Medicinal Products (ATMPs) to treat progressive illnesses and rare genetic conditions, their delivery on large scale is still problematic. The biopharmaceutical companies are currently struggling to meet timely delivery and, given high prices of up to $2 million per patient, prove the cost-effectiveness of their ATMP. The fragility of ATMPs combined with the impossibility for replacements due to the nature of the treatment and the advanced stages of the patient's condition are some of the bottlenecks added to a generally critical supply chain. As a consequence, ATMPs are currently used in most cases only as a last resort. ATMPs are at the intersection of multiple healthcare logistic networks and, due to their novelty, research around their commercialisation is still in its infancy from an operations research perspective. To accelerate technology adoption in this domain, we characterize pertinent practical challenges in a PM supply chain and then capture them in a holistic mathematical model ready for optimisation. The identified challenges and derived model will be contrasted with literature of related supply chains in terms of model formulations and suitable optimisation methods. Finally, needed technological advancements are discussed to pave the way to affordable commercialisation of PM.

A monopolist seller of multiple goods screens a buyer whose type is initially unknown to both but drawn from a commonly known distribution. The buyer privately learns about his type via a signal. We derive the seller's optimal mechanism in two different information environments. We begin by deriving the buyer-optimal outcome. Here, an information designer first selects a signal, and then the seller chooses an optimal mechanism in response; the designer's objective is to maximize consumer surplus. Then, we derive the optimal informationally robust mechanism. In this case, the seller first chooses the mechanism, and then nature picks the signal that minimizes the seller's profits. We derive the relation between both problems and show that the optimal mechanism in both cases takes the form of pure bundling.

This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.

The Maker Protocol is a decentralized finance application that enables collateralized lending. The application uses open-bid, second-price auctions to complete its loan liquidation process. In this paper, we develop a bidding function for these auctions, focusing on the costs incurred to participate in the auctions. We then optimize these costs using parameters from historical auction data, and compare our optimal bidding prices to the historical auction prices. We find that the majority of auctions end at higher prices than our recommended optimal prices, and we propose several theories for these results.

This paper is devoted to the study of robust fundamental theorems of asset pricing in discrete time and finite horizon settings. The new concept "robust pricing system" is introduced to rule out the existence of model independent arbitrage opportunities. Superhedging duality and strategy are obtained.

Internet survey experiment is conducted to examine how providing peer information of evaluation about progressive firms changed individual's evaluations. Using large sample including over 13,000 observations collected by two-step experimental surveys, I found; (1) provision of the information leads individuals to expect higher probability of rising of stocks and be more willing to buy it. (2) the effect on willingness to buy is larger than the expected probability of stock price rising, (3) The effect for woman is larger than for man. (4) individuals who prefer environment (woman's empowerment) become more willing to buy stock of pro-environment (gender-balanced) firms than others if they have the information. (5) The effect of the peer information is larger for individuals with "warm -glow" motivation.