In chaotic modern society, there is an increasing demand for the realization of true 'fairness'. In Greek mythology, Themis, the 'goddess of justice', has a sword in her right hand to protect society from vices, and a 'balance of judgment' in her left hand that measures good and evil. In this study, we propose a fair distribution method 'utilising physics' for the profit in games of characteristic function form. Specifically, we show that the linear programming problem for calculating 'nucleolus' can be efficiently solved by considering it as a physical system in which gravity works. In addition to being able to significantly reduce computational complexity thereby, we believe that this system could have flexibility necessary to respond to real-time changes in the parameter.

We propose a hybrid quantum-classical algorithm, originated from quantum chemistry, to price European and Asian options in the Black-Scholes model. Our approach is based on the equivalence between the pricing partial differential equation and the Schrodinger equation in imaginary time. We devise a strategy to build a shallow quantum circuit approximation to this equation, only requiring few qubits. This constitutes a promising candidate for the application of Quantum Computing techniques (with large number of qubits affected by noise) in Quantitative Finance.

Distributed ledger technologies replace central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows down cross-market trading and exposes arbitrageurs to price risk. We theoretically derive arbitrage bounds induced by settlement latency. Using Bitcoin orderbook and network data, we estimate average arbitrage bounds of 121 basis points, explaining 91% of the cross-market price differences, and demonstrate that asset flows chase arbitrage opportunities. Controlling for inventory holdings as a measure of trust in exchanges does not affect our main results. Blockchain-based settlement without trusted intermediation thus introduces a non-trivial friction that impedes arbitrage activity.

We introduce a linear cross-impact framework in a setting in which the price of some given financial instruments (derivatives) is a deterministic function of one or more, possibly tradeable, stochastic factors (underlying). We show that a particular cross-impact model, the multivariate Kyle model, prevents arbitrage and aggregates (potentially non-stationary) traded order flows on derivatives into (roughly stationary) liquidity pools aggregating order flows traded on both derivatives and underlying. Using E-Mini futures and options along with VIX futures, we provide empirical evidence that the price formation process from order flows on derivatives is driven by cross-impact and confirm that the simple Kyle cross-impact model is successful at capturing parsimoniously such empirical phenomenology. Our framework may be used in practice for estimating execution costs, in particular hedging costs.

The COVID-19 pandemic has disrupted human activities, leading to unprecedented decreases in both global energy demand and GHG emissions. Yet a little known that there is also a low carbon shift of the global energy system in 2020. Here, using the near-real-time data on energy-related GHG emissions from 30 countries (about 70% of global power generation), we show that the pandemic caused an unprecedented de-carbonization of global power system, representing by a dramatic decrease in the carbon intensity of power sector that reached a historical low of 414.9 tCO2eq/GWh in 2020. Moreover, the share of energy derived from renewable and low-carbon sources (nuclear, hydro-energy, wind, solar, geothermal, and biomass) exceeded that from coal and oil for the first time in history in May of 2020. The decrease in global net energy demand (-1.3% in the first half of 2020 relative to the average of the period in 2016-2019) masks a large down-regulation of fossil-fuel-burning power plants supply (-6.1%) coincident with a surge of low-carbon sources (+6.2%). Concomitant changes in the diurnal cycle of electricity demand also favored low-carbon generators, including a flattening of the morning ramp, a lower midday peak, and delays in both the morning and midday load peaks in most countries. However, emission intensities in the power sector have since rebounded in many countries, and a key question for climate mitigation is thus to what extent countries can achieve and maintain lower, pandemic-level carbon intensities of electricity as part of a green recovery.

Does the ability to pledge an asset as collateral, after purchase, affect its price? This paper identifies the impact of collateral service flows on house prices, exploiting a plausibly exogenous constitutional amendment in Texas which legalized home equity loans in 1998. The law change increased Texas house prices 4%; this is price-based evidence that households are credit-constrained and value home equity loans to facilitate consumption smoothing. Prices rose more in locations with inelastic supply, higher pre-law house prices, higher income, and lower unemployment. These estimates reveal that richer households value the option to pledge their home as collateral more strongly.

The involvement of children in the family dairy farming is pivotal point to reduce the cost of production input, especially in smallholder dairy farming. The purposes of the study are to analysis the factors that influence children's participation in working in the family dairy farm. The study was held December 2020 in the development center of dairy farming in Pangalengan subdistrict, West Java Province, Indonesia. The econometric method used in the study was the logit regression model. The results of the study determine that the there were number of respondents who participates in family farms was 52.59% of total respondents, and the rest was no participation in the family farms. There are 3 variables in the model that are very influential on children's participation in the family dairy farming, such as X1 (number of dairy farm land ownership), X2 (number of family members), and X6 (the amount of work spent on the family's dairy farm). Key words: Participation, children, family, dairy farming, logit model

We explore the scaling properties of price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect within a framework of fractal analysis. The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin, Ethereum, Ripple, and Litecoin. Furthermore, asymmetric reactions in the volatility process to price fluctuations between uptrend (bull) and downtrend (bear) regimes are discussed from the viewpoint of cross-correlations quantified by the asymmetric DCCA coefficient, which is a different approach from the conventional GARCH-class models. We find that cross-correlations are stronger in downtrend markets than in uptrend markets for maturing Bitcoin and Ethereum. In contrast, for Ripple and Litecoin, inverted reactions are present where cross-correlations are stronger in uptrend markets. Our empirical findings uncover the dynamics of asymmetric volatility structure and provide new guidance in investigating dynamical relationships between price and volatility for cryptocurrencies.

The Massachusetts Attorney General issued an Enforcement Notice in 2016 to announce a new interpretation of a key phrase in the state's assault weapons ban. The Enforcement Notice increased sales of tagged assault rifles by 616% in the first 5 days, followed by a 9% decrease over the next three weeks. Sales of Handguns and Shotguns did not change significantly. Tagged assault rifle sales fell 28-30% in 2017 compared to previous years, suggesting that the Enforcement Notice reduced assault weapon sales but also that many banned weapons continued to be sold. Tagged assault rifles sold most in 2017 in zip codes with higher household incomes and proportions of white males. Overall, the results suggest that the firearm market reacts rapidly to policy changes and partially complies with firearm restrictions.

We build an optimal portfolio liquidation model for OTC markets, aiming at minimizing the trading costs via the choice of the liquidation time. We work in the Locally Linear Order Book framework of \cite{toth2011anomalous} to obtain the market impact as a function of the traded volume. We find that the optimal terminal time for a linear execution of a small order is proportional to the square root of the ratio between the amount being bought or sold and the average daily volume. Numerical experiments on real market data illustrate the method on a portfolio of corporate bonds.

We establish a generalization of Noether theorem for stochastic optimal control problems. Exploiting the tools of jet bundles and contact geometry, we prove that from any (contact) symmetry of the Hamilton-Jacobi-Bellman equation associated to an optimal control problem it is possible to build a related local martingale. Moreover, we provide an application of the theoretical results to Merton's optimal portfolio problem, showing that this model admits infinitely many conserved quantities in the form of local martingales.

Direct elicitation, guided by theory, is the standard method for eliciting latent preferences. The canonical direct-elicitation approach for measuring individuals' valuations for goods is the Becker-DeGroot-Marschak procedure, which generates willingness-to-pay (WTP) values that are imprecise and systematically biased by understating valuations. We show that enhancing elicited WTP values with supervised machine learning (SML) can substantially improve estimates of peoples' out-of-sample purchase behavior. Furthermore, swapping WTP data with choice data generated from a simple task, two-alternative forced choice, leads to comparable performance. Combining all the data with the best-performing SML methods yields large improvements in predicting out-of-sample purchases. We quantify the benefit of using various SML methods in conjunction with using different types of data. Our results suggest that prices set by SML would increase revenue by 28% over using the stated WTP, with the same data.

We develop an approach to solve Barberis (2012)'s casino gambling model in which a gambler whose preferences are specified by the cumulative prospect theory (CPT) must decide when to stop gambling by a prescribed deadline. We assume that the gambler can assist their decision using an independent randomization, and explain why it is a reasonable assumption. The problem is inherently time-inconsistent due to the probability weighting in CPT, and we study both precommitted and naive stopping strategies. We turn the original problem into a computationally tractable mathematical program, based on which we derive an optimal precommitted rule which is randomized and Markovian. The analytical treatment enables us to make several predictions regarding a gambler's behavior, including that with randomization they may enter the casino even when allowed to play only once, that whether they will play longer once they are granted more bets depends on whether they are in a gain or at a loss, and that it is prevalent that a naivite never stops loss.