Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. It is of high interest in financial applications to be able to capture the underlying joint dynamics of these two quantities. We address this problem by developing a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES. We refer to this model as LSTM-AL. We adopt the adaptive Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference in the LSTM-AL model. Empirical results show that the proposed LSTM-AL model can improve the VaR and ES forecasting accuracy over a range of well-established competing models.

U.S. metropolitan areas, particularly in the industrial Midwest and Northeast, are well-known for high levels of racial segregation. This is especially true where core cities end and suburbs begin; often crossing the street can lead to physically similar, but much less ethnically diverse, suburban neighborhood. While these differences are often visually or "intuitively" apparent, this study seeks to quantify them using Geographic Information Systems and a variety of statistical methods. 2016 Census block group data are used to calculate an ethnic Herfindahl index for a set of two dozen large U.S. cities and their contiguous suburbs. Then, a mathematical method is developed to calculate a block-group-level "Border Disparity Index" (BDI), which is shown to vary by MSA and by specific suburbs. Its values can be compared across the sample to examine which cities are more likely to have borders that separate more-diverse block groups from less-diverse ones. The index can also be used to see which core cities are relatively more or less diverse than their suburbs, and which individual suburbs have the largest disparities vis-\`a-vis their core city. Atlanta and Detroit have particularly diverse suburbs, while Milwaukee's are not. Regression analysis shows that income differences and suburban shares of Black residents play significant roles in explaining variation across suburbs.

Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing over time properties, making their analysis a complex task. Improvement of understanding and development of methods for financial time series analysis is essential for successful operation on financial markets. In this study we propose a volume-based data pre-processing method for making financial time series more suitable for machine learning pipelines. We use a statistical approach for assessing the performance of the method. Namely, we formally state the hypotheses, set up associated classification tasks, compute effect sizes with confidence intervals, and run statistical tests to validate the hypotheses. We additionally assess the trading performance of the proposed method on historical data and compare it to a previously published approach. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than a price action-based method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models on example of CatBoost estimator, as well as formally assess the relatedness of the proposed approach and SHAP feature interactions with a positive outcome.

Milwaukee's 53206 ZIP code, located on the city's near North Side, has drawn considerable attention for its poverty and incarceration rates, as well as for its large proportion of vacant properties. As a result, it has benefited from targeted policies at the city level. Keeping in mind that ZIP codes are often not the most effective unit of geographic analysis, this study investigates Milwaukee's socioeconomic conditions at the block group level. These smaller areas' statistics are then compared with those of their corresponding ZIP codes. The 53206 ZIP code is compared against others in Milwaukee for eight socioeconomic variables and is found to be near the extreme end of most rankings. This ZIP code would also be among Chicago's most extreme areas, but would lie near the middle of the rankings if located in Detroit. Parts of other ZIP codes, which are often adjacent, are statistically similar to 53206, however--suggesting that a focus solely on ZIP codes, while a convenient shorthand, might overlook neighborhoods that have similar need for investment. A multivariate index created for this study performs similarly to a standard multivariate index of economic deprivation if spatial correlation is taken into account, confirming that poverty and other socioeconomic stresses are clustered, both in the 53206 ZIP code and across Milwaukee.

It is well known that rightly applied reverse auctions offer big commercial potential to procurement departments. However, the sheer number of auction types often overwhelms users in practice. And since the implications of a wrongly chosen auction type are equally well known, the overall usage of reverse auctions lacks its potential significantly. In this paper, a novel method is being proposed that guides the user in selecting the right combination of basic auction forms for single lot events, considering both market-, as well as supplier-related, bijective criteria.

The study introduces an automated trading system for S\&P500 E-mini futures (ES) based on state-of-the-art machine learning. Concretely: we extract a set of scenarios from the tick market data to train the model and further use the predictions to model trading. We define the scenarios from the local extrema of the price action. Price extrema is a commonly traded pattern, however, to the best of our knowledge, there is no study presenting a pipeline for automated classification and profitability evaluation. Our study is filling this gap by presenting a broad evaluation of the approach showing the resulting average Sharpe ratio of 6.32. However, we do not take into account order execution queues, which of course affect the result in the live-trading setting. The obtained performance results give us confidence that this approach is worthwhile.

Computational models of managerial search often build on backward-looking search based on hill-climbing algorithms. Regardless of its prevalence, there is some evidence that this family of algorithms does not universally represent managers' search behavior. Against this background, the paper proposes an alternative algorithm that captures key elements of Simon's concept of satisficing which received considerable support in behavioral experiments. The paper contrasts the satisficing-based algorithm to two variants of hill-climbing search in an agent-based model of a simple decision-making organization. The model builds on the framework of NK fitness landscapes which allows controlling for the complexity of the decision problem to be solved. The results suggest that the model's behavior may remarkably differ depending on whether satisficing or hill-climbing serves as an algorithmic representation for decision-makers' search. Moreover, with the satisficing algorithm, results indicate oscillating aspiration levels, even to the negative, and intense - and potentially destabilizing - search activities when intra-organizational complexity increases. Findings may shed some new light on prior computational models of decision-making in organizations and point to avenues for future research.

Centralized monetary policy, leading to persistent inflation, is often inconsistent, untrustworthy, and unpredictable. Algorithmic stable coins enabled by blockchain technology are promising in solving this problem. Algorithmic stable coins utilize a monetary policy that is entirely rule-based. However, there is little understanding about how to optimize the rule. We propose a model that trade-offs between the price and supply stability. We further study the comparative statistics by varying several design features. Finally, we discuss the empirical implications and further research for industry applications.

Implied volatility is at the very core of modern finance, notwithstanding standard option pricing models continue to derive option prices starting from the joint dynamics of the underlying asset price and the spot volatility. These models often cause difficulties: no closed formulas for prices, demanding calibration techniques, unclear maps between spot and implied volatility. Inspired by the practice of using implied volatility as quoting system for option prices, models for the joint dynamics of the underlying asset price and the implied volatility have been proposed to replace standard option pricing models. Starting from Carr and Sun (2014), we develop a framework based on the Implied Remaining Variance where minimal conditions for absence of arbitrage are identified, and smile bubbles are dealt with. The key concepts arising from the new IRV framework are those of locally consistent dynamics and sandwiched martingale. Within the new IRV framework, the results of Schweizer and Wissel (2008b) are reformulated, while those of El Amrani, Jacquier and Martini (2021) are independently derived.

Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems currently in use. Specifically, the former predicts the proportion of time individuals are on income support in the subsequent four years with greater accuracy, by a magnitude of at least 22% (14 percentage points increase in the R2), compared to the latter. This gain can be achieved at no extra cost to practitioners since the algorithms use administrative data currently available to caseworkers. Consequently, our machine learning algorithms can improve the detection of long-term income support recipients, which can potentially provide governments with large savings in accrued welfare costs.

Consumer preference elicitation is critical to devise effective policies for the diffusion of electric vehicles (EVs) in India. This study contributes to the EV demand literature in the Indian context by (a) analysing the EV attributes and attitudinal factors of Indian car buyers that determine consumers' preferences for EVs, (b) estimating Indian consumers' willingness to pay (WTP) to buy EVs with improved attributes, and c) quantifying how the reference dependence affects the WTP estimates. We adopt a hybrid choice modelling approach for the above analysis. The results indicate that accounting for reference dependence provides more realistic WTP estimates than the standard utility estimation approach. Our results suggest that Indian consumers are willing to pay an additional USD 10-34 in the purchase price to reduce the fast charging time by 1 minute, USD 7-40 to add a kilometre to the driving range of EVs at 200 kilometres, and USD 104-692 to save USD 1 per 100 kilometres in operating cost. These estimates and the effect of attitudes on the likelihood to adopt EVs provide insights about EV design, marketing strategies, and pro-EV policies (e.g., specialised lanes and reserved parking for EVs) to expedite the adoption of EVs in India.