Option pricing is a significant problem for option risk management and trading. In this article, we utilize a framework to present financial data from different sources. The data is processed and represented in a form of 2D tensors in three channels. Furthermore, we propose two deep learning models that can deal with 3D tensor data. Experiments performed on the Chinese market option dataset prove the practicability of the proposed strategies over commonly used ways, including B-S model and vector-based LSTM.

We estimate capital and labor income Pareto exponents across 475 country-year observations that span 52 countries over half a century (1967-2018). We document two stylized facts: (i) capital income is more unequally distributed than labor income in the tail; namely, the capital exponent (1-3, median 1.46) is smaller than labor (2-5, median 3.35), and (ii) capital and labor exponents are nearly uncorrelated. To explain these findings, we build an incomplete market model with job ladders and capital income risk that gives rise to a capital income Pareto exponent smaller than but nearly unrelated to the labor exponent. Our results suggest the importance of distinguishing income and wealth inequality.

This paper is an attempt to deal with the recent realization (Vazirani, Yannakakis 2021) that the Hylland-Zeckhauser mechanism, which has remained a classic in economics for one-sided matching markets, is likely to be highly intractable. HZ uses the power of a pricing mechanism, which has endowed it with nice game-theoretic properties.

Hosseini and Vazirani (2021) define a rich collection of Nash-bargaining-based models for one-sided and two-sided matching markets, in both Fisher and Arrow-Debreu settings, together with implementations using available solvers, and very encouraging experimental results. This naturally raises the question of finding efficient combinatorial algorithms for these models.

In this paper, we give efficient combinatorial algorithms based on the techniques of multiplicative weights update (MWU) and conditional gradient descent (CGD) for several one-sided and two-sided models defined in HV 2021. Additionally, we define for the first time a Nash-bargaining-based model for non-bipartite matching markets and solve it using CGD. Furthermore, in every case, we study not only the Fisher but also the Arrow-Debreu version; the latter is also called the exchange version. We give natural applications for each model studied. These models inherit the game-theoretic and computational properties of Nash bargaining.

We also establish a deep connection between HZ and the Nash-bargaining-based models, thereby confirming that the alternative to HZ proposed in HV 2021 is a principled one.

Stock market returns are typically analyzed using standard regression, yet they reside on irregular domains which is a natural scenario for graph signal processing. To this end, we consider a market graph as an intuitive way to represent the relationships between financial assets. Traditional methods for estimating asset-return covariance operate under the assumption of statistical time-invariance, and are thus unable to appropriately infer the underlying true structure of the market graph. This work introduces a class of graph spectral estimators which cater for the nonstationarity inherent to asset price movements, and serve as a basis to represent the time-varying interactions between assets through a dynamic spectral market graph. Such an account of the time-varying nature of the asset-return covariance allows us to introduce the notion of dynamic spectral portfolio cuts, whereby the graph is partitioned into time-evolving clusters, allowing for online and robust asset allocation. The advantages of the proposed framework over traditional methods are demonstrated through numerical case studies using real-world price data.

In this article we describe a study aimed at estimating job vacancy statistics, in particular the number of entities with at least one vacancy. To achieve this goal, we propose an alternative approach to the methodology exploiting survey data, which is based solely on data from administrative registers and online sources and relies on dual system estimation (DSE).

As these sources do not cover the whole reference population and the number of units appearing in all datasets is small, we have developed a DSE approach for negatively dependent sources based on a recent work by Chatterjee and Bhuyan (2020). To achieve the main goal we conducted a thorough data cleaning procedure in order to remove out-of-scope units, identify entities from the target population, and link them by identifiers to minimize linkage errors. We verified the effectiveness and sensitivity of the proposed estimator in simulation studies.

From a practical point of view, our results show that the current vacancy survey in Poland underestimates the number of entities with at least one vacancy by about 10-15%. The main reasons for this discrepancy are non-sampling errors due to non-response and under-reporting, which is identified by comparing survey data with administrative data.

This paper considers population processes in which general, not necessarily Markovian, multivariate Hawkes processes dictate the stochastic arrivals. We establish results to determine the corresponding time-dependent joint probability distribution, allowing for general intensity decay functions, general intensity jumps, and general sojourn times. We obtain an exact, full characterization of the time-dependent joint transform of the multivariate population process and its underlying intensity process in terms of a fixed-point representation and corresponding convergence results. We also derive the asymptotic tail behavior of the population process and its underlying intensity process in the setting of heavy-tailed intensity jumps. By exploiting the results we establish, arbitrary joint spatial-temporal moments and other distributional properties can now be readily evaluated using standard transform differentiation and inversion techniques, and we illustrate this in a few examples.

In this paper, we propose a new model to address the problem of negative interest rates that preserves the analytical tractability of the original Cox-Ingersoll-Ross (CIR) model without introducing a shift to the market interest rates, because it is defined as the difference of two independent CIR processes. The strength of our model lies within the fact that it is very simple and can be calibrated to the market zero yield curve using an analytical formula. We run several numerical experiments at two different dates, once with a partially sub-zero interest rate and once with a fully negative interest rate. In both cases, we obtain good results in the sense that the model reproduces the market term structures very well. We then simulate the model using the Euler-Maruyama scheme and examine the mean, variance and distribution of the model. The latter agrees with the skewness and fat tail seen in the original CIR model. In addition, we compare the model's zero coupon prices with market prices at different future points in time. Finally, we test the market consistency of the model by evaluating swaptions with different tenors and maturities.

The meme stock phenomenon is yet to be explored. In this note, we provide evidence that these stocks display common stylized facts on the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock "mementum" which exhibits a different characterization with respect to other stocks with high volumes of activity (persistent and not) on social media. Understanding these properties helps the investors and market authorities in their decision.

Stratifying commercial product portfolios into multiple classes of decreasing priority, ABCD analysis, is a common supply chain tool. Key planning parameters that drive strategic and execution priorities are tied to the resulting segmentation. These priorities in turn drive supply chain performance. For large product assortments, manual segmentation is infeasible so an automated algorithm is needed. We therefore advocate that careful attention be paid to the design of such an ABCD algorithm and present three key features that can be incorporated into such a calculation to improve its quality and commercial utility.

In recent years, a wide range of investment models have been created using artificial intelligence. Automatic trading by artificial intelligence can expand the range of trading methods, such as by conferring the ability to operate 24 hours a day and the ability to trade with high frequency. Automatic trading can also be expected to trade with more information than is available to humans if it can sufficiently consider past data. In this paper, we propose an investment agent based on a deep reinforcement learning model, which is an artificial intelligence model. The model considers the transaction costs involved in actual trading and creates a framework for trading over a long period of time so that it can make a large profit on a single trade. In doing so, it can maximize the profit while keeping transaction costs low. In addition, in consideration of actual operations, we use online learning so that the system can continue to learn by constantly updating the latest online data instead of learning with static data. This makes it possible to trade in non-stationary financial markets by always incorporating current market trend information.

The optimal taxation of assets requires attention to two concerns: 1) the elasticity of the supply of assets and 2) the impact of taxing assets on distributional objectives. The most efficient way to attend to these two concerns is to tax assets of different types separately, rather than having one tax on all assets. When assets are created by specialized effort rather than by saving, as with innovations, discoveries of mineral deposits and development of unregulated natural monopolies, it is interesting to consider a regime in which the government awards a prize for the creation of the asset and then collects the remaining value of the asset in taxes. Analytically, the prize is like a wage after taxes. In this perspective, prizes are awarded based on a variation on optimal taxation theory, while assets of different types are taxed in divergent ways, depending on their characteristics. Some categories of assets are abolished.

We address a long-standing open problem in risk theory, namely the optimal strategy to pay out dividends from an insurance surplus process, if the dividend rate can never be decreased. The optimality criterion here is to maximize the expected value of the aggregate discounted dividend payments up to the time of ruin. In the framework of the classical Cram\'{e}r-Lundberg risk model, we solve the corresponding two-dimensional optimal control problem and show that the value function is the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We also show that the value function can be approximated arbitrarily closely by ratcheting strategies with only a finite number of possible dividend rates and identify the free boundary and the optimal strategies in several concrete examples. These implementations illustrate that the restriction of ratcheting does not lead to a large efficiency loss when compared to the classical un-constrained optimal dividend strategy.

Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation. In this paper, we investigate ordering relations between two aggregation sets for which the sets of marginals are related by two simple operations: distribution mixtures and quantile mixtures. Intuitively, these operations ``homogenize" marginal distributions by making them similar. As a general conclusion from our results, more ``homogeneous" marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures. We proceed to study inequalities on the worst-case values of risk measures in risk aggregation, which represent conservative calculation of regulatory capital. Among other results, we obtain an order relation on VaR under quantile mixture for marginal distributions with monotone densities. Numerical results are presented to visualize the theoretical results and further inspire some conjectures. Finally, we provide applications on portfolio diversification under dependence uncertainty and merging p-values in multiple hypothesis testing, and discuss the connection of our results to joint mixability.

Calcium deficiency in high yielding bovines during calving causes milk fever which leads to economic losses of around INR 1000 crores (USD 137 million) per annum in Haryana, India. With increasing milk production, the risk of milk fever is continuously rising. In the context, we aim to address the most fundamental research question: What is the effect of a preventive health product (anionic mineral mixture (AMM)) on milk fever incidence, milk productivity and farmers income? In an effort to contribute to the scanty economic literature on effect of preventive measures on nutritional deficiency disorders in dairy animals, specifically, on AMM effects in India, this study uses a randomized controlled design to estimate internally valid estimates. Using data from 200 dairy farms, results indicate that milk fever incidence decreases from 21 per cent at baseline to 2 per cent in treated animals at follow-up. Further, AMM leads to a 12 per cent and 38 per cent increase in milk yield and farmers net income, respectively. Profits earned due to the prevention of milk fever [INR 16000 (USD 218.7)] overweighs the losses from milk fever [INR 4000 (USD 54.7)]; thus, prevention using AMM is better than cure.

Existing deep learning methods for solving mean-field games (MFGs) with common noise fix the sampling common noise paths and then solve the corresponding MFGs. This leads to a nested-loop structure with millions of simulations of common noise paths in order to produce accurate solutions, which results in prohibitive computational cost and limits the applications to a large extent. In this paper, based on the rough path theory, we propose a novel single-loop algorithm, named signatured deep fictitious play, by which we can work with the unfixed common noise setup to avoid the nested-loop structure and reduce the computational complexity significantly. The proposed algorithm can accurately capture the effect of common uncertainty changes on mean-field equilibria without further training of neural networks, as previously needed in the existing machine learning algorithms. The efficiency is supported by three applications, including linear-quadratic MFGs, mean-field portfolio game, and mean-field game of optimal consumption and investment. Overall, we provide a new point of view from the rough path theory to solve MFGs with common noise with significantly improved efficiency and an extensive range of applications. In addition, we report the first deep learning work to deal with extended MFGs (a mean-field interaction via both the states and controls) with common noise.

The intellectual property protection system constructed by China's Foreign Investment Law has opened a new phase of rule of law protection of intellectual property rights for foreign-invested enterprises, which is an important institutional support indispensable for optimizing the business environment under the rule of law.The development of the regime was influenced by the major concerns of investors' home countries, the "innovation-driven development" strategy, and the trend towards a high level of stringent protection of international intellectual property and investment rules.In addition, there is a latent game of interests between multiple subjects, which can be analyzed by constructing two standard formal game models according to legal game theory.The first game model aims to compare and analyze the gains and losses of China and India's IPR protection system for foreign-invested enterprises to attract foreign investment.The second game model is designed to analyze the benefits of China and foreign investors under their respective possible behaviors before and after the inclusion of IPR protection provisions in the Foreign Investment Law, with the optimal solution being a "moderately cautious" strategy for foreign investors and a "strict enforcement" strategy for China.

Recent event of ousting Rohingyas from Rakhine State by the Tatmadaw provoked worldwide public-and-academic interest in history and social evolution of the Rohingyas, and this is to what the article is devoted. As the existing literature presents a debate over Who are the Rohingyas?, and How legitimate is their claim over Rakhine State?, the paper reinvestigates the issues using a qualitative research method. Compiling a detailed history, the paper finds that Rohingya community developed through historically complicated processes marked by invasions and counter-invasions. The paper argues many people entered Bengal from Arakan before British brought people into Rakhine state. The Rohingyas believe Rakhine State is their ancestral homeland and they developed a sense of Ethnic Nationalism. Their right over Rakhine State is as significant as other groups. The paper concludes that the UN must pursue solution to the crisis and the government should accept the Rohingyas as it did the land or territory.

Several studies have shown that large changes in the returns of an asset are associated with the sized of the gaps present in the order book In general, these associations have been studied without explicitly considering the dynamics of either gaps or returns. Here we present a study of these relationships. Our results suggest that the causal relationship between gaps and returns is limited to instantaneous causation.