Quantitative Finance
New submissions
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New submissions for Thu, 2 May 24
- [1] arXiv:2405.00234 [pdf, other]
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Title: Conceiving Naturally After IVF: the effect of assisted reproduction on obstetric interventions and child health at birthSubjects: General Economics (econ.GN)
A growing share of the world's population is being born via assisted reproductive technology (ART), including in-vitro fertilisation (IVF). However, two concerns persist. First, ART pregnancies correlate with predictors of poor outcomes at birth--and it is unclear whether this relationship is causal. Second, the emotional and financial costs associated with ART-use might exacerbate defensive medical behaviour, where physicians intervene more than necessary to reduce the risk of adverse medical outcomes and litigation. We address the challenge of identifying the pure effect of ART-use on both maternal and infant outcomes at birth by leveraging exogenous variation in the success of ART cycles. We compare the obstetric outcomes for ART-conceived births with those of spontaneously-conceived births after a failed ART treatment. Moreover, we flexibly adjust for key confounders using double machine learning. We do this using clinical registry ART data and administrative maternal and infant data from New South Wales (NSW) between 2009-2017. We find that ART slightly decreases the risk of obstetric interventions, lowering the risk of a caesarean section and increasing the rate of spontaneous labour (+3.5 p.p.). Moreover, we find that ART has a statistically and clinically insignificant effect on infant health outcomes.
Keywords: Fertility, Assisted reproduction, IVF, Caesarean Section, Obstetric, Infertility. JEL classification: I10, I12, I19. - [2] arXiv:2405.00235 [pdf, ps, other]
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Title: Blockchain Price vs. Quantity ControlsAuthors: Abdoulaye NdiayeSubjects: General Economics (econ.GN)
This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\mathcal{P}$) and quantity-based ($\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\mathcal{P}$ and $\mathcal{Q}$ mechanisms depends critically on the blockchain's specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\mathcal{P}$ and $\mathcal{Q}$ to meet varying demands and market conditions.
- [3] arXiv:2405.00247 [pdf, other]
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Title: The value of non-traditional credentials in the labor marketSubjects: General Economics (econ.GN)
This study investigates the labor market value of credentials obtained from Massive Open Online Courses (MOOCs) and shared on business networking platforms. We conducted a randomized experiment involving more than 800,000 learners, primarily from developing countries and without college degrees, who completed technology or business-related courses on the Coursera platform between September 2022 and March 2023. The intervention targeted learners who had recently completed their courses, encouraging them to share their credentials and simplifying the sharing process. One year after the intervention, we collected data from LinkedIn profiles of approximately 40,000 experimental subjects. We find that the intervention leads to an increase of 17 percentage points for credential sharing. Further, learners in the treatment group were 6\% more likely to report new employment within a year, with an 8\% increase in jobs related to their certificates. This effect was more pronounced among LinkedIn users with lower baseline employability. Across the entire sample, the treated group received a higher number of certificate views, indicating an increased interest in their profiles. These results suggest that facilitating credential sharing and reminding learners of the value of skill signaling can yield significant gains. When the experiment is viewed as an encouragement design for credential sharing, we can estimate the local average treatment effect (LATE) of credential sharing (that is, the impact of credential sharing on the workers induced to share by the intervention) for the outcome of getting a job. The LATE estimates are imprecise but large in magnitude; they suggest that credential sharing more than doubles the baseline probability of getting a new job in scope for the credential.
- [4] arXiv:2405.00357 [pdf, other]
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Title: Optimal nonparametric estimation of the expected shortfall riskSubjects: Risk Management (q-fin.RM); Probability (math.PR); Statistics Theory (math.ST); Mathematical Finance (q-fin.MF)
We address the problem of estimating the expected shortfall risk of a financial loss using a finite number of i.i.d. data. It is well known that the classical plug-in estimator suffers from poor statistical performance when faced with (heavy-tailed) distributions that are commonly used in financial contexts. Further, it lacks robustness, as the modification of even a single data point can cause a significant distortion. We propose a novel procedure for the estimation of the expected shortfall and prove that it recovers the best possible statistical properties (dictated by the central limit theorem) under minimal assumptions and for all finite numbers of data. Further, this estimator is adversarially robust: even if a (small) proportion of the data is maliciously modified, the procedure continuous to optimally estimate the true expected shortfall risk. We demonstrate that our estimator outperforms the classical plug-in estimator through a variety of numerical experiments across a range of standard loss distributions.
- [5] arXiv:2405.00473 [pdf, other]
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Title: Pricing and delta computation in jump-diffusion models with stochastic intensity by Malliavin calculusComments: 5 fingures. 1 tableSubjects: Pricing of Securities (q-fin.PR); Probability (math.PR)
In this paper, the pricing of financial derivatives and the calculation of their delta Greek are investigated as the underlying asset is a jump-diffusion process in which the stochastic intensity component follows the CIR process. Utilizing Malliavin derivatives for pricing financial derivatives and challenging to find the Malliavin weight for accurately calculating delta will be established in such models. Due to the dependence of asset price on the information of the intensity process, conditional expectation attribute to show boundedness of moments of Malliavin weights and the underlying asset is essential. Our approach is validated through numerical experiments, highlighting its effectiveness and potential for risk management and hedging strategies in markets characterized by jump and stochastic intensity dynamics.
- [6] arXiv:2405.00522 [pdf, other]
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Title: DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend ForecastingSubjects: General Economics (econ.GN)
In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.
- [7] arXiv:2405.00537 [pdf, other]
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Title: Quantifying Price Improvement in Order Flow AuctionsSubjects: Trading and Market Microstructure (q-fin.TR)
This work introduces a framework for evaluating onchain order flow auctions (OFAs), emphasizing the metric of price improvement. Utilizing a set of open-source tools, our methodology systematically attributes price improvements to specific modifiable inputs of the system such as routing efficiency, gas optimization, and priority fee settings. When applied to leading Ethereum-based trading interfaces such as 1Inch and Uniswap, the results reveal that auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points in our sample. We further identify the sources of such price improvements to be added liquidity for large swaps. This research lays a foundation for future innovations in blockchain based trading platforms.
- [8] arXiv:2405.00576 [pdf, other]
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Title: Calibration of the rating transition model for high and low default portfoliosSubjects: Risk Management (q-fin.RM); Methodology (stat.ME)
In this paper we develop Maximum likelihood (ML) based algorithms to calibrate the model parameters in credit rating transition models. Since the credit rating transition models are not Gaussian linear models, the celebrated Kalman filter is not suitable to compute the likelihood of observed migrations. Therefore, we develop a Laplace approximation of the likelihood function and as a result the Kalman filter can be used in the end to compute the likelihood function. This approach is applied to so-called high-default portfolios, in which the number of migrations (defaults) is large enough to obtain high accuracy of the Laplace approximation. By contrast, low-default portfolios have a limited number of observed migrations (defaults). Therefore, in order to calibrate low-default portfolios, we develop a ML algorithm using a particle filter (PF) and Gaussian process regression. Experiments show that both algorithms are efficient and produce accurate approximations of the likelihood function and the ML estimates of the model parameters.
- [9] arXiv:2405.00606 [pdf, ps, other]
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Title: Some properties of Euler capital allocationAuthors: Lars HoldenComments: 12 pages, 3 figures, 4 tables, 15 referencesSubjects: Risk Management (q-fin.RM); Probability (math.PR); Portfolio Management (q-fin.PM)
The paper discusses capital allocation using the Euler formula and focuses on the risk measures Value-at-Risk (VaR) and Expected shortfall (ES). Some new results connected to this capital allocation is known. Two examples illustrate that capital allocation with VaR is not monotonous which may be surprising since VaR is monotonous. A third example illustrates why the same risk measure should be used in capital allocation as in the evaluation of the total portfolio. We show how simulation may be used in order to estimate the expected Return on risk adjusted capital in the commitment period of an asset. Finally, we show how Markov chain Monte Carlo may be used in the estimation of the capital allocation.
Cross-lists for Thu, 2 May 24
- [10] arXiv:2405.00046 (cross-list from physics.soc-ph) [pdf, ps, other]
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Title: Synchronization in a market model with time delaysComments: 12 pages, 4 figuresSubjects: Physics and Society (physics.soc-ph); Statistical Finance (q-fin.ST)
We examine a system of N=2 coupled non-linear delay-differential equations representing financial market dynamics. In such time delay systems, coupled oscillations have been derived. We linearize the system for small time delays and study its collective dynamics. Using analytical and numerical solutions, we obtain the bifurcation diagrams and analyze the corresponding regions of amplitude death, phase locking, limit cycles and market synchronization in terms of the system frequency-like parameters and time delays. We further numerically explore higher order systems with N>2, and demonstrate that limit cycles can be maintained for coupled N-asset models with appropriate parameterization.
- [11] arXiv:2405.00047 (cross-list from physics.soc-ph) [pdf, ps, other]
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Title: The Quantum Dynamics of Cost Accounting: Investigating WIP via the Time-Independent Schrodinger EquationAuthors: Maksym LazirkoSubjects: Physics and Society (physics.soc-ph); General Finance (q-fin.GN); Quantum Physics (quant-ph)
The intersection of quantum theory and accounting presents a novel and intriguing frontier in exploring financial valuation and accounting practices. This paper applies quantum theory to cost accounting, specifically Work in Progress (WIP) valuation. WIP is conceptualized as materials in a quantum superposition state whose financial value remains uncertain until observed or measured. This work comprehensively reviews the seminal works that explored the overlap between quantum theory and accounting. The primary contribution of this work is a more nuanced understanding of the uncertainties involved, which emerges by applying quantum phenomena to model the complexities and uncertainties inherent in managerial accounting. In contrast, previous works focus more on financial accounting or general accountancy.
- [12] arXiv:2405.00051 (cross-list from physics.soc-ph) [pdf, other]
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Title: Arbitrage impact on the relationship between XRP price and correlation tensor spectra of transaction networksComments: 12 pages, 8 figuresSubjects: Physics and Society (physics.soc-ph); General Finance (q-fin.GN); Statistical Finance (q-fin.ST)
The increasing use of cryptoassets for international remittances has proven to be faster and more cost-effective, particularly for migrants without access to traditional banking. However, the inherent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, introduces potential risks during periods of high volatility. This study investigates the intricate dynamics between XRP price fluctuations across diverse crypto exchanges and the correlation of the largest singular values of the correlation tensor of XRP transaction networks. Particularly, we show the impact of arbitrage opportunities across different crypto exchanges on the relationship between XRP price and correlation tensor spectra of transaction networks. Distinct periods, non-bubble and bubble, showcase different characteristics in XRP price fluctuations. Establishing a connection between XRP price and transaction networks, we compute correlation tensors and singular values, emphasizing the significance of the largest singular value. Comparisons with reshuffled and Gaussian random correlation tensors validate the uniqueness of the empirical tensor. A set of simulated weekly XRP prices, resembling arbitrage opportunities across various crypto exchanges, further confirms the robustness of our findings. It reveals a pronounced anti-correlation during bubble periods and a non-significant correlation during non-bubble periods with the largest singular value, irrespective of price fluctuations across different crypto exchanges.
- [13] arXiv:2405.00540 (cross-list from cs.CY) [pdf, other]
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Title: Heat, Health, and Habitats: Analyzing the Intersecting Risks of Climate and Demographic Shifts in Austrian DistrictsSubjects: Computers and Society (cs.CY); General Economics (econ.GN); Atmospheric and Oceanic Physics (physics.ao-ph)
The impact of hot weather on health outcomes of a population is mediated by a variety of factors, including its age profile and local green infrastructure. The combination of warming due to climate change and demographic aging suggests that heat-related health outcomes will deteriorate in the coming decades. Here, we measure the relationship between weekly all-cause mortality and heat days in Austrian districts using a panel dataset covering $2015-2022$. An additional day reaching $30$ degrees is associated with a $2.4\%$ increase in mortality per $1000$ inhabitants during summer. This association is roughly doubled in districts with a two standard deviation above average share of the population over $65$. Using forecasts of hot days (RCP) and demographics in $2050$, we observe that districts will have elderly populations and hot days $2-5$ standard deviations above the current mean in just $25$ years. This predicts a drastic increase in heat-related mortality. At the same time, district green scores, measured using $10\times 10$ meter resolution satellite images of residential areas, significantly moderate the relationship between heat and mortality. Thus, although local policies likely cannot reverse warming or demographic trends, they can take measures to mediate the health consequences of these growing risks, which are highly heterogeneous across regions, even in Austria.
- [14] arXiv:2405.00566 (cross-list from cs.CE) [pdf, other]
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Title: NumLLM: Numeric-Sensitive Large Language Model for Chinese FinanceSubjects: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); General Finance (q-fin.GN)
Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, existing FinLLMs exhibit unsatisfactory performance in understanding financial text when numeric variables are involved in questions. In this paper, we propose a novel LLM, called numeric-sensitive large language model (NumLLM), for Chinese finance. We first construct a financial corpus from financial textbooks which is essential for improving numeric capability of LLMs during fine-tuning. After that, we train two individual low-rank adaptation (LoRA) modules by fine-tuning on our constructed financial corpus. One module is for adapting general-purpose LLMs to financial domain, and the other module is for enhancing the ability of NumLLM to understand financial text with numeric variables. Lastly, we merge the two LoRA modules into the foundation model to obtain NumLLM for inference. Experiments on financial question-answering benchmark show that NumLLM can boost the performance of the foundation model and can achieve the best overall performance compared to all baselines, on both numeric and non-numeric questions.
Replacements for Thu, 2 May 24
- [15] arXiv:2206.04424 (replaced) [pdf, other]
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Title: Estimating the Gains (and Losses) of Revenue ManagementComments: 79 pages (the appendix starts at p.42)Subjects: General Economics (econ.GN)
- [16] arXiv:2307.03391 (replaced) [pdf, other]
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Title: On Unified Adaptive Portfolio ManagementComments: 22 pages, 7 figuresSubjects: Portfolio Management (q-fin.PM); Optimization and Control (math.OC); Computational Finance (q-fin.CP); Risk Management (q-fin.RM)
- [17] arXiv:2307.07015 (replaced) [pdf, other]
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Title: Advertiser Learning in Direct Advertising MarketsSubjects: General Economics (econ.GN)
- [18] arXiv:2311.08650 (replaced) [pdf, ps, other]
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Title: The Use of Symmetry for Models with Variable-size VariablesAuthors: Takeshi FukasawaSubjects: General Economics (econ.GN)
- [19] arXiv:2404.02595 (replaced) [pdf, other]
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Title: QFNN-FFD: Quantum Federated Neural Network for Financial Fraud DetectionSubjects: Quantum Physics (quant-ph); Machine Learning (cs.LG); Risk Management (q-fin.RM)
- [20] arXiv:2404.18709 (replaced) [pdf, other]
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Title: Three-state Opinion Dynamics for Financial Markets on Complex NetworksAuthors: Bernardo J. Zubillaga, Mateus F. B. Granha, André L. M. Vilela, Chao Wang, Kenric P. Nelson, H. Eugene StanleyComments: 15 pages, 14 figuresSubjects: Physics and Society (physics.soc-ph); Statistical Mechanics (cond-mat.stat-mech); General Economics (econ.GN)
- [21] arXiv:2404.19109 (replaced) [pdf, other]
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Title: The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 DatasetAuthors: Claudio Bellei, Muhua Xu, Ross Phillips, Tom Robinson, Mark Weber, Tim Kaler, Charles E. Leiserson, Arvind, Jie ChenSubjects: Machine Learning (cs.LG); General Finance (q-fin.GN)
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