New articles on Quantitative Finance


[1] 2402.19380

Not flexible enough? Impacts of electric carsharing on a power sector with variable renewables

Electrifying the car fleet is a major strategy for mitigating greenhouse gas emissions in the transport sector. However, electrification alone will not solve all the negative externalities associated with cars. In light of other problems such as street space as well as concerns about the use of mineral resources for battery electric cars, reducing the car fleet size would be beneficial, particularly in cities. Carsharing could offer a way to reconcile current car usage habits with a reduction in the car fleet size. However, it could also reduce the potential of electric cars to align their grid interactions with variable renewable electricity generation. We investigate how electric carsharing may impact the power sector in the future. We combine three open-source quantitative methods, including sequence clustering of car travel diaries, a probabilistic tool to generate synthetic electric vehicle time series, and an optimization model of the power sector. For 2030 scenarios of Germany with a renewable share of at least 80%, we show that switching to electric carsharing only moderately increases power sector costs. In our main setting, carsharing increases yearly power sector costs by less than 100 euros per substituted private electric car. This cost effect is largest under the assumption of bidirectional charging. It is mitigated when other sources of flexibility for the power sector are considered. Carsharing further causes a shift from wind power to solar PV in the optimal capacity mix, and may also trigger additional investments in stationary electricity storage. Overall, we find that shared electric cars still have the potential to be operated largely in line with variable renewable electricity generation. We conclude that electric carsharing is unlikely to cause much damage to the power sector, but could bring various other benefits, which may outweigh power sector cost increases.


[2] 2402.19399

An Empirical Analysis of Scam Token on Ethereum Blockchain: Counterfeit tokens on Uniswap

This article presents an empirical investigation into the determinants of total revenue generated by counterfeit tokens on Uniswap. It offers a detailed overview of the counterfeit token fraud process, along with a systematic summary of characteristics associated with such fraudulent activities observed in Uniswap. The study primarily examines the relationship between revenue from counterfeit token scams and their defining characteristics, and analyzes the influence of market economic factors such as return on market capitalization and price return on Ethereum. Key findings include a significant increase in overall transactions of counterfeit tokens on their first day of fraud, and a rise in upfront fraud costs leading to corresponding increases in revenue. Furthermore, a negative correlation is identified between the total revenue of counterfeit tokens and the volatility of Ethereum market capitalization return, while price return volatility on Ethereum is found to have a positive impact on counterfeit token revenue, albeit requiring further investigation for a comprehensive understanding. Additionally, the number of subscribers for the real token correlates positively with the realized volume of scam tokens, indicating that a larger community following the legitimate token may inadvertently contribute to the visibility and success of counterfeit tokens. Conversely, the number of Telegram subscribers exhibits a negative impact on the realized volume of scam tokens, suggesting that a higher level of scrutiny or awareness within Telegram communities may act as a deterrent to fraudulent activities. Finally, the timing of when the scam token is introduced on the Ethereum blockchain may have a negative impact on its success. Notably, the cumulative amount scammed by only 42 counterfeit tokens amounted to almost 11214 Ether.


[3] 2402.18764

An Analytical Approach to (Meta)Relational Models Theory, and its Application to Triple Bottom Line (Profit, People, Planet) -- Towards Social Relations Portfolio Management

Investigating the optimal nature of social interactions among generic actors (e.g., people or firms), aiming to achieve specifically-agreed objectives, has been the subject of extensive academic research. Using the relational models theory - comprehensively describing all social interactions among actors as combinations of only four forms of sociality: communal sharing, authority ranking, equality matching, and market pricing - the common approach within the literature revolves around qualitative assessments of the sociality models' configurations most effective in realizing predefined purposes, at times supplemented by empirical data. In this treatment, we formulate this question as a mathematical optimization problem, in order to quantitatively determine the best possible configurations of sociality forms between dyadic actors which would optimize their mutually-agreed objectives. For this purpose, we develop an analytical framework for quantifying the (meta)relational models theory, and mathematically demonstrate that combining the four sociality forms within a specific meaningful social interaction inevitably prompts an inherent tension among them, through a single elementary and universal metarelation. In analogy with financial portfolio management, we subsequently introduce the concept of Social Relations Portfolio (SRP) management, and propose a generalizable procedural methodology capable of quantitatively identifying the efficient SRP for any objective involving meaningful social relations. As an important illustration, the methodology is applied to the Triple Bottom Line paradigm to derive its efficient SRP, guiding practitioners in precisely measuring, monitoring, reporting and (proactively) steering stakeholder management efforts regarding Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) within and / or across organizations.


[4] 2402.18872

Semistatic robust utility indifference valuation and robust integral functionals

We consider a discrete-time robust utility maximisation with semistatic strategies, and the associated indifference prices of exotic options. For this purpose, we introduce a robust form of convex integral functionals on the space of bounded continuous functions on a Polish space, and establish some key regularity and representation results, in the spirit of the classical Rockafellar theorem, in terms of the duality formed with the space of Borel measures. These results (together with the standard Fenchel duality and minimax theorems) yield a duality for the robust utility maximisation problem as well as a representation of associated indifference prices, where the presence of static positions in the primal problem appears in the dual problem as a marginal constraint on the martingale measures. Consequently, the resulting indifference prices are consistent with the observed prices of vanilla options.


[5] 2402.18959

MambaStock: Selective state space model for stock prediction

The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to their robust nonlinear generalization capabilities. Recently, Mamba, a structured state space sequence model with a selection mechanism and scan module (S6), has emerged as a powerful tool in sequence modeling tasks. Leveraging this framework, this paper proposes a novel Mamba-based model for stock price prediction, named MambaStock. The proposed MambaStock model effectively mines historical stock market data to predict future stock prices without handcrafted features or extensive preprocessing procedures. Empirical studies on several stocks indicate that the MambaStock model outperforms previous methods, delivering highly accurate predictions. This enhanced accuracy can assist investors and institutions in making informed decisions, aiming to maximize returns while minimizing risks. This work underscores the value of Mamba in time-series forecasting. Source code is available at https://github.com/zshicode/MambaStock.


[6] 2402.19203

On non-negative solutions of stochastic Volterra equations with jumps and non-Lipschitz coefficients

We consider one-dimensional stochastic Volterra equations with jumps for which we establish conditions upon the convolution kernel and coefficients for the strong existence and pathwise uniqueness of a non-negative c\`adl\`ag solution. By using the approach recently developed in arXiv:2302.07758, we show the strong existence by using a nonnegative approximation of the equation whose convergence is proved via a variant of the Yamada--Watanabe approximation technique. We apply our results to L\'evy-driven stochastic Volterra equations. In particular, we are able to define a Volterra extension of the so-called alpha-stable Cox--Ingersoll--Ross process, which is especially used for applications in Mathematical Finance.


[7] 2402.19421

Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines

In the domain of digital information dissemination, search engines act as pivotal conduits linking information seekers with providers. The advent of chat-based search engines utilizing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), exemplified by Bing Chat, marks an evolutionary leap in the search ecosystem. They demonstrate metacognitive abilities in interpreting web information and crafting responses with human-like understanding and creativity. Nonetheless, the intricate nature of LLMs renders their "cognitive" processes opaque, challenging even their designers' understanding. This research aims to dissect the mechanisms through which an LLM-powered chat-based search engine, specifically Bing Chat, selects information sources for its responses. To this end, an extensive dataset has been compiled through engagements with New Bing, documenting the websites it cites alongside those listed by the conventional search engine. Employing natural language processing (NLP) techniques, the research reveals that Bing Chat exhibits a preference for content that is not only readable and formally structured, but also demonstrates lower perplexity levels, indicating a unique inclination towards text that is predictable by the underlying LLM. Further enriching our analysis, we procure an additional dataset through interactions with the GPT-4 based knowledge retrieval API, unveiling a congruent text preference between the RAG API and Bing Chat. This consensus suggests that these text preferences intrinsically emerge from the underlying language models, rather than being explicitly crafted by Bing Chat's developers. Moreover, our investigation documents a greater similarity among websites cited by RAG technologies compared to those ranked highest by conventional search engines.