Quantitative Finance

Lee, Geon;Kim, Tae-Kyoung;Kim, Hyun-Gyoon;Huh, Jeonggyu

doi: 10.48550/arXiv.2210.15969pmid: N/A

Abstract: In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility using iteration methods, such as the Newton--Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network using PyTorch, a well-known deep learning package, and optimize the network further using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the NR function in SciPy, a popular implementation of the NR method, we demonstrate that the emulation network is up to 1,000 times faster than the benchmark function.

Sen, Jaydip;Dutta, Abhishek

doi: 10.1007/978-981-19-3391-2_34pmid: N/A

Abstract: Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a systematic approach to portfolio optimization using two approaches, the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors of the Indian stock market. The portfolios are built following the two approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021, to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of its Eigen counterpart on both training and test data for the majority of the sectors studied.

Sergio, Alvarez-Telena;Marta, Diez-Fernandez

doi: 10.48550/arXiv.2210.08982pmid: N/A

Abstract: The natural alignment between business and architecture within big techs has boosted their transformation (crucially, upon API-fication and synergies exploitation) compared to that in the rest of organisations. The efficiency gap is so large that even the latter fear the irruption of big techs in their own arenas. Nevertheless, organisations have lately lost control of their architectures. They have become a mix of services offered by big techs and orchestrated by external consultants. Such a dynamic has naturally led to a large convergence between architectures across industries in spite of their idiosyncratic differences. Hence, there is room for improvement through a transformation governance that optimally weighs both microeconomics and microservices. As neither of the fields is easy to master, such an improvement remains a greenfield. This paper proposes a novel data architecture paradigm, Data Flex, that helps organisations take control of their transformation journey by becoming platforms - i.e. unlocking convergence with big techs efficiency levels. Further, it surpasses the theory by having evolved Data Flex first instance for the last 7 years. Along that time, the authors gathered real examples that filled out a cube defined by a series of dimensions significant enough to assert the universal validity of their approach.

Chen, Jason;Fogel, Kathy;John, Kose

doi: 10.48550/arXiv.2210.16899pmid: N/A

Abstract: This paper discusses a decentralized finance (DeFi) application called MakerDAO. The Maker Protocol, built on the Ethereum blockchain, enables users to create and hold currency. Current elements of the Maker Protocol are the Dai stable coin, Maker Vaults, and Voting. MakerDAO governs the Maker Protocol by deciding on key parameters (e.g., stability fees, collateral types and rates, etc.) through the voting power of Maker (MKR) holders. The Maker Protocol is one of the largest decentralized applications (DApps) on the Ethereum blockchain and is the first decentralized finance (DeFi) application to earn significant adoption. The objective of this paper is to analyze and discuss the significance, uses, and functions of this DeFi application.

Rahman, Farhana

doi: 10.48550/arXiv.2210.07955pmid: N/A

Abstract: The research paper aims to analyze the underlying relationship in between the profitability and cost of funds of a firm. A total of twelve companies were selected as a sample for this study which are listed in Dhaka Stock Exchange under Food and Allied Industry. A panel data set of 15 years from 2005 to 2019 was used to conduct the necessary analysis. In this paper, Return on Asset (ROA) is used as the accounting criteria of profitability. WACC is the independent variable while Firm Size, Firm Age and Firm Leverage are used as control variable for the study. Fixed Effects Panel Regression Model is used to analyze the dataset. The result of the analysis shows that WACC is negatively related with the profitability measure and this relationship is significant. The study has potential to be replicated by other industries like textile, cement, pharmaceutical & chemical, fuel & power, tannery etc.

Kapengut, Elie;Mizrach, Bruce

doi: 10.48550/arXiv.2210.13655pmid: N/A

Abstract: On September 15, 2022, the Ethereum network adopted a proof-of-stake (PoS) consensus mechanism. We study the impact on the network and competing platforms in a short event window around the Beacon chain merge. We find that the transition to PoS has reduced energy consumption by 99.98%. Miners have not transformed into validators, and total block reward income has fallen by 95.6%. The validator network's Herfindahl index is 1,159, 8.6% lower than the miners' prior to the merge. Ethereum supply growth has fallen nearly 95%. Transaction fees for Ether have nearly doubled and token transaction fees have increased 23.7%. The time between consecutive blocks is now steady at 12 seconds, a speed increase of 18.9%. Fewer transactions are being included in each block though, so the transactions per second have actually fallen by 58.2%. On Polygon, Matic fees rose 21.7% and token fees 31.7%. Polygon also slows, processing 12.7% fewer transactions per second. Solana's fees and speed are unaffected by the merge. Stablecoin transfer volumes rise on all three networks. Polygon has the largest gain for USD Coin, 230%, and the Mainnet the largest for Tether, 86%.

Cuchiero, Christa;Di Persio, Luca;Guida, Francesco;Svaluto-Ferro, Sara

doi: 10.48550/arXiv.2210.09331pmid: N/A

Abstract: We introduce a framework that allows to employ (non-negative) measure-valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath-Jarrow-Morton approach can be translated to this framework, thus guaranteeing arbitrage free modeling in infinite dimensions. We derive an analog to the HJM-drift condition and then treat in a Markovian setting existence of non-negative measure-valued diffusions that satisfy this condition. To analyze mathematically convenient classes we build on Cuchiero et al. (2021) and consider measure-valued polynomial and affine diffusions, where we can precisely specify the diffusion part in terms of continuous functions satisfying certain admissibility conditions. For calibration purposes these functions can then be parameterized by neural networks yielding measure-valued analogs of neural SPDEs. By combining Fourier approaches or the moment formula with stochastic gradient descent methods, this then allows for tractable calibration procedures which we also test by way of example on market data. We also sketch how measure-valued processes can be applied in the context of renewable energy production modeling.

Xu, Jiahua;Feng, Yebo

doi: 10.48550/arXiv.2210.04194pmid: N/A

Abstract: Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregation-layer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and protocols for loanable funds (PLFs). We examine the yield farming mechanism by first studying the operations encoded in the yield farming smart contracts, and then performing stylized, parameterized simulations on various yield farming strategies. We conduct a thorough literature review on related work, and establish a framework for yield farming protocols that takes into account pool structure, accepted token types, and implemented strategies. Using our framework, we characterize major yield aggregators in the market including Yearn Finance, Beefy, and Badger DAO. Moreover, we discuss anecdotal attacks against yield aggregators and generalize a number of risks associated with yield farming.

Berre, Max

doi: 10.48550/arXiv.2210.14518pmid: N/A

Abstract: While startup valuations are influenced by revenues, risks, age, and macroeconomic conditions, specific causality is traditionally a black box. Because valuations are not disclosed, roles played by other factors (industry, geography, and intellectual property) can often only be guessed at. VC valuation research indicates the importance of establishing a factor-hierarchy to better understand startup valuations and their dynamics, suggesting the wisdom of hiring data-scientists for this purpose. Bespoke understanding can be established via construction of hierarchical prediction models based on decision trees and random forests. These have the advantage of understanding which factors matter most. In combination with OLS, the also tell us the circumstances of when specific causalities apply. This study explores the deterministic role of categorical variables on the valuation of start-ups (i.e. the joint-combination geographic, urban, and sectoral denomination-variables), in order to be able to build a generalized valuation scorecard approach. Using a dataset of 1,091 venture-capital investments, containing 1,044 unique EU and EEA, this study examines microeconomic, sectoral, and local-level impacts on startup valuation. In principle, the study relies on Fixedeffects and Joint-fixed-effects regressions as well as the analysis and exploration of divergent micropopulations and fault-lines by means of non-parametric approaches combining econometric and machinelearning techniques.

Bailey, Jason R.;Lauria, Davide;Lindquist, W. Brent;Mittnik, Stefan;Rachev, Svetlozar T.

doi: 10.48550/arXiv.2210.14266pmid: N/A

Abstract: We consider the use of P-spline generalized additive hedonic models for real estate prices in large U.S. cities, contrasting their predictive efficiency against linear and polynomial based generalized linear models. Using intrinsic and extrinsic factors available from Redfin, we show that GAM models are capable of describing 84% to 92% of the variance in the expected ln(sales price), based upon 2021 data. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers on the northwest coast. The results indicate city dependent differences in the significance of environmental factors. We find that inclusion of the environmental factors increases the adjusted R-squared of the GAM model by less than one percent.