A model is proposed that models Bitcoin prices by taking into account market attention. Assuming that market attention follows a mean-reverting Cox-Ingersoll-Ross process and allowing it to influence Bitcoin returns (after some delay) leads to a tractable affine model with semi-closed formulae for European put and call prices. A maximum likelihood estimation procedure is proposed for this model. The accuracy of its call and put prices outperforms a number of standard models when tested on real data.

The rise of Ethereum and other blockchains that support smart contracts has led to the creation of decentralized exchanges (DEXs), such as Uniswap, Balancer, Curve, mStable, and SushiSwap, which enable agents to trade cryptocurrencies without trusting a centralized authority. While traditional exchanges use order books to match and execute trades, DEXs are typically organized as constant function market makers (CFMMs). CFMMs accept and reject proposed trades based on the evaluation of a function that depends on the proposed trade and the current reserves of the DEX. For trades that involve only two assets, CFMMs are easy to understand, via two functions that give the quantity of one asset that must be tendered to receive a given quantity of the other, and vice versa. When more than two assets are being exchanged, it is harder to understand the landscape of possible trades. We observe that various problems of choosing a multi-asset trade can be formulated as convex optimization problems, and can therefore be reliably and efficiently solved.

Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots and expanding the universe of alpha discovery. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy - indicating that MBO data is additive to LOB-based features.

Water-logging is a major challenge for Dhaka city, the capital of Bangladesh. The rapid, unregulated, and unplanned urbanization, as well as detrimental social, economic, infrastructural, and environmental consequences, not to mention diseases like dengue, challenge the several crash programs combating water-logging in the city. This study provides a brief contextual analysis of the Dhakas topography and natural, as well as storm water drainage systems, before concentrating on the man-made causes and effects of water-logging, ultimately exploring a few remedial measures.

Based on law of large numbers and central limit theorem under nonlinear expectation, we introduce a new method of using G-normal distribution to measure financial risks. Applying max-mean estimators and small windows method, we establish autoregressive models to determine the parameters of G-normal distribution, i.e., the return, maximal and minimal volatilities of the time series. Utilizing the value at risk (VaR) predictor model under G-normal distribution, we show that the G-VaR model gives an excellent performance in predicting the VaR for a benchmark dataset comparing to many well-known VaR predictors.

Using three rounds of NSS datasets, the present paper attempts to understand the relationship between income inequality and intergenerational income mobility (IGIM) by segregating generations into social and income classes. The originality of the paper lies in assessing the IGIM using different approaches, which we expect to contribute to the existing literature. We conclude that the country has low-income mobility and high inequality which is no longer associated with a particular social class in India. Also, both may have a negative or positive relationship, hence needs to be studied at a regional level.

Over the past 200 years, rising rates of information proliferation have created new environments for information competition and, consequently, new selective forces on information evolution. These forces influence the information diet available to consumers, who in turn choose what to consume, creating a feedback process similar to that seen in many ecosystems. As a first step towards understanding this relationship, we apply animal foraging models of diet choice to describe the evolution of long and short form media in response to human preferences for maximising utility rate. The model describes an increase in information rate (i.e., entropy) in response to information proliferation, as well as differences in entropy between short-form and long-form media (such as social media and books, respectively). We find evidence for a steady increase in word entropy in diverse media categories since 1900, as well as an accelerated entropy increase in short-form media. Overall the evidence suggests an increasingly competitive battle for our attention that is having a lasting influence on the evolution of language and communication systems.

A point process model for order flows in limit order books is proposed, in which the conditional intensity is the product of a Hawkes component and a state-dependent factor. In the LOB context, state observations may include the observed imbalance or the observed spread. Full technical details for the computationally-efficient estimation of such a process are provided, using either direct likelihood maximization or EM-type estimation. Applications include models for bid and ask market orders, or for upwards and downwards price movements. Empirical results on multiple stocks traded in Euronext Paris underline the benefits of state-dependent formulations for LOB modeling, e.g. in terms of goodness-of-fit to financial data.

The Covid-19 pandemic still causes severe impacts on society and the economy. This paper studies excess mortality during the pandemic years 2020 and 2021 in Germany empirically with a special focus on the life insurer's perspective. Our conclusions are based on official counts of German governmental offices on the living and deaths of the entire population. Conclusions, relevant for actuaries and specific insurance business lines, including portfolios of pension, life, and health insurance contracts, are provided.

On-demand ridepooling (ODRP) can become a powerful alternative to reduce congestion and emissions, if it attracts private car users. Therefore, it is crucial to identify the strategic phenomena that determine when ODRP systems can run efficiently. In this paper, we analyze the performance of an ODRP system, in which the fleet of low-capacity vehicles is endogenously adapted to the demand, and operated in a zone covered by a single transit line. The routing of the on-demand fleet follows some of the rules of public transport systems; namely, it is not-for-profit, some users can be required to walk, and all requests must be served. Considering both users' and operators' costs we identify two sources of scale economies: when demand grows, the average cost is reduced due to a) an equivalent of the Mohring Effect (also present in public transport), and b) due to matching users with more similar routes when they are assigned to the vehicles, which we call Better-matching Effect. A counter-balance force, called Flex-route Effect, is observed when the vehicle loads increase and users face longer detours. We find a specific demand range in which the latter effect dominates the others, imposing diseconomies of scale when only users' costs are considered. Such a phenomenon emerges because the routes are not fixed; hence, it is not observed in traditional public transport systems. However, when considering both users' and operators' costs, scale economies prevail. Our simulations show that relaxing door-to-door vehicle requirements to allow short walks is crucial for the performance of ODRP. In fact, we observe that an ODRP system with human-driven vehicles and walks allowed has a total cost at a similar level to that of a door-to-door ODRP system with driverless vehicles.

We consider a global market constituted by several submarkets, each with its own assets and num\'eraire. We provide theoretical foundations for the existence of equivalent martingale measures and results on superreplication prices which allows to take into account difference of features between submarkets.

We study the convergence properties of the short maturity expansion of option prices in the uncorrelated log-normal ($\beta=1$) SABR model. In this model the option time-value can be represented as an integral of the form $V(T) = \int_{0}^\infty e^{-\frac{u^2}{2T}} g(u) du$ with $g(u)$ a ``payoff function'' which is given by an integral over the McKean kernel $G(s,t)$. We study the analyticity properties of the function $g(u)$ in the complex $u$-plane and show that it is holomorphic in the strip $|\Im(u) |< \pi$. Using this result we show that the $T$-series expansion of $V(T)$ and implied volatility are asymptotic (non-convergent for any $T>0$). In a certain limit which can be defined either as the large volatility limit $\sigma_0\to \infty$ at fixed $\omega=1$, or the small vol-of-vol limit $\omega\to 0$ limit at fixed $\omega\sigma_0$, the short maturity $T$-expansion for the implied volatility has a finite convergence radius $T_c = \frac{1.32}{\omega\sigma_0}$.

A notion of essential supremum is developed when the uncertainty is measured by a family of non-dominated and non-compact probability measures. It provides new perspectives on super-replication and allows the Absence of Instantaneous Profit (AIP) to be characterized.

This paper investigates the finite horizon risk-sensitive portfolio optimization in a regime-switching credit market with physical and information-induced default contagion. It is assumed that the underlying regime-switching process has countable states and is unobservable. The stochastic control problem is formulated under partial observations of asset prices and sequential default events. By establishing a martingale representation theorem based on incomplete and phasing out filtration, we connect the control problem to a quadratic BSDE with jumps, in which the driver term is non-standard and carries the conditional filter as an infinite-dimensional parameter. By proposing some truncation techniques and proving a uniform a priori estimates, we obtain the existence of a solution to the BSDE using the convergence of solutions associated to some truncated BSDEs. The verification theorem can be concluded with the aid of our BSDE results, which in turn yields the uniqueness of the solution to the BSDE.

In this paper we perform robustness and sensitivity analysis of several continuous-time rough Volterra stochastic volatility models with respect to the process of market calibration. Robustness is understood in the sense of sensitivity to changes in the option data structure. The latter analyses then should validate the hypothesis on importance of the roughness in the volatility process dynamics. Empirical study is performed on a data set of Apple Inc. equity options traded in four different days in April and May 2015. In particular, the results for RFSV, rBergomi and aRFSV models are provided.

We develop a generic calibration strategy for climate models used in economics. The key idea is to choose the free model parameters to match the output of large-scale Earth System Models, which are run on pre-defined future emissions scenarios and collected in the Coupled Model Intercomparison Project (CMIP5). We propose to use four test cases that are considered pivotal in the climate science literature. Two of these tests are highly idealized to allow for the separate examination of the carbon cycle and the temperature response. Another two tests incorporate gradual changes in CO2 emissions, exogenous forcing, and the temperature response. We re-calibrate the free parameters of the climate part of the seminal DICE-2016 model for three different CMIP5 model responses: the multi-model mean as well as two other CMIP5 models that exhibit extreme equilibrium climate sensitivities. As an additional novelty, our calibrations of DICE-2016 allow for an arbitrary time step in the model explicitly. We show that i) both the temperature equations and the carbon cycle in DICE-2016 are miscalibrated and that ii) by re-calibrating its coefficients, we can match all three CMIP5 targets. We apply the economic model from DICE-2016 in combination with the newly calibrated climate model to compute the social cost of carbon and the optimal warming. We find that in our updated model, the social cost of carbon is very similar to DICE-2016, however, the optimal long-run temperature lies almost one degree below that obtained by DICE-2016. This difference in climate behavior is reflected in the over-sensitivity of the social cost of carbon to the discount rate. Under the optimal mitigation scenario, the temperature predictions of DICE-2016 (in contrast to our proposed calibration) fall outside of the CMIP5 scenarios, suggesting that one might want to be skeptical about policy predictions derived from DICE-2016.

The world's stock markets display a strikingly suspicious, decades long pattern of overnight and intraday returns that nobody (other than us) has plausibly explained and that nobody (other than us) has clearly and persistently alerted you to. We use correspondence on this topic over the past five years to show that the silence of others on this issue does not arise from their having a good reason to believe this pattern is fine. Separately, and regardless of whether this pattern turns out to be fine, we have documented that people in a position to alert you to the presence of strikingly suspicious return patterns in the world's stock markets that nobody can innocuously explain are aware of this issue, have no good reason to believe it is not a problem, and chose to not tell you.