We price European-style options written on forward contracts in a commodity market, which we model with an infinite-dimensional Heath-Jarrow-Morton (HJM) approach. For this purpose we introduce a new class of state-dependent volatility operators that map the square integrable noise into the Filipovi{\'{c}} space of forward curves. For calibration, we specify a fully parametrized version of our model and train a neural network to approximate the true option price as a function of the model parameters. This neural network can then be used to calibrate the HJM parameters based on observed option prices. We conduct a numerical case study based on artificially generated option prices in a deterministic volatility setting. In this setting we derive closed pricing formulas, allowing us to benchmark the neural network based calibration approach. We also study calibration in illiquid markets with a large bid-ask spread. The experiments reveal a high degree of accuracy in recovering the prices after calibration, even if the original meaning of the model parameters is partly lost in the approximation step.

In this paper, the Dirac's quantum mechanical interaction picture is applied to option pricing to obtain a solution of the Black-Scholes equation in the presence of a time-dependent arbitrage bubble. In particular, for the case of a call perturbed by a square bubble, an approximate solution (valid up third order in a perturbation series) is given in terms of the three first Greeks: Delta, Gamma, and Speed. Then an exact solution is constructed in terms of all higher order $S$-derivatives of the Black-Scholes formula. It is also shown that the interacting Black-Scholes equation is invariant under a discrete transformation that interchanges the interest rate with the mean of the underlying asset and vice versa. This implies that the interacting Black-Scholes equation can be written in a 'low energy' and a 'high energy' form, in such a way that the high-interaction limit of the low energy form corresponds to the weak-interaction limit of the high energy form. One can apply a perturbative analysis to the high energy form to study the high-interaction limit of the low energy form.

In this study, I aimed to estimate the incidence of catastrophic health expenditure and analyze the extent of inequalities in out-of-pocket health expenditure and its decomposition according to gender, sector, religion and social groups of the households across Districts of West Bengal. I analysed health spending in West Bengal, using National Sample Survey 71st round pooled data suitably represented to estimate up to district level. We measured CHE at different thresholds when OOP in health expenditure. Gini Coefficients and its decomposition techniques were applied to assess the degree of inequality in OOP health expenditures and between different socio geographic factors across districts. The incidence of catastrophic payments varies considerably across districts. Only 14.1 percent population of West Bengal was covered under health coverage in 2014. The inequality in OOP health expenditure for West Bengal has been observed with gini coefficient of 0.67. Based on the findings from this analysis, more attention is needed on effective financial protection for people of West Bengal to promote fairness, with special focus on the districts with higher inequality. This study only provides the extent of CHE and inequality across Districts of West Bengal but the causality may be taken in future scope of study.

China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century. But the pathways that would achieve net zero by 2060 are still unclear including the dependence on negative emissions technologies. Here, we use the Global Change Analysis Model (GCAM 5.3), a dynamic-recursive, technology-rich integrated assessment model, to simulate how negative emissions technologies, in general, and direct air capture (DAC), in particular, will contribute to China's meeting this target. Our results show that, for China to be net-zero in 2060, it would need to deploy negative emissions technologies (NETs) at very large scales, on the order of 2.5 GtCO2 negative emissions per year with up to 1.5 GtCO2 per year of that coming from DAC. DAC, like other forms of negative emissions, such as bioenergy with carbon capture and storage and afforestation is an emerging technology that has not been demonstrated at a commercial scale. Deploying NETs at this scale will have widespread impacts on financial systems and resources availability such as water, land, and energy in China and beyond.

This paper extends the core results of discrete time infinite horizon dynamic programming to the case of state-dependent discounting. We obtain a condition on the discount factor process under which all of the standard optimality results can be recovered. We also show that the condition cannot be significantly weakened. Our framework is general enough to handle complications such as recursive preferences and unbounded rewards. Economic and financial applications are discussed.

We model the dynamics of poverty using a stochastic model of Geometric Brownian Motion with reallocation (RGBM) and explore both transient and persistent poverty over 1952-2006. We find that annual transitions in and out of poverty are common and show a rising trend, with the rise largely being driven by transitions out of poverty. Despite this promising trend, even toward the end of the time frame, there is a non-trivial proportion of individuals still transitioning annually into poverty, indicative of the economic fragility of those near the poverty line. We also find that there is still a marked persistence of poverty over time, though the probability of poverty persistence is slowly declining. Particularly concerning in this context are the poverty trajectories of those at the very bottom of the income distribution. The choice of poverty line appears to impact the dynamics, with higher poverty lines corresponding to lower transitions and higher persistence probabilities. The distinct nature of emergent transient and persistence dynamics suggests that the approaches to counter these phenomena need to be different, possibly incorporating both missing financial markets and state action.

In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state and action spaces and unbounded cost under both finite and infinite planning horizons. Our optimality criterion is based on the recursive application of static risk measures. This is motivated by recursive utilities in the economic literature, has been studied before for the entropic risk measure and is extended here to an axiomatic characterization of suitable risk measures. We derive a Bellman equation and prove the existence of Markovian optimal policies. For an infinite planning horizon, the model is shown to be contractive and the optimal policy to be stationary. Moreover, we establish a connection to distributionally robust MDPs, which provides a global interpretation of the recursively defined objective function. Monotone models are studied in particular.

The emergence of the COVID-19 pandemic, a new and novel risk factor, leads to the stock price crash due to the investors' rapid and synchronous sell-off. However, within a short period, the quality sectors start recovering from the bottom. A stock price model has been developed during such crises based on the net-fund-flow ($\Psi_t$) due to institutional investors, and financial antifragility ($\phi$) of a company. We assume that during the crash, the stock price fall is independent of the $\phi$. We study the effects of shock lengths and $\phi$ on the stock price during the crises period using the $\Psi_t$ obtained from synthetic and real fund flow data. We observed that the possibility of recovery of stock with $\phi>0$, termed as quality stock, decreases with an increase in shock-length beyond a specific period. A quality stock with higher $\phi$ shows V-shape recovery and outperform others. The shock length and recovery period of quality stock are almost equal that is seen in the Indian market. Financially stressed stocks, i.e., the stocks with $\phi<0$, show L-shape recovery during the pandemic. The stock data and model analysis shows that the investors, in the uncertainty like COVID-19, invest in the quality stocks to restructure their portfolio to reduce the risk. The study may help the investors to make the right investment decision during a crisis.

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency - only limited by the available satellite imagery - which can alleviate data limitations decisively.

We develop a modelling framework for multiple yield curves driven by continuous-state branching processes with immigration (CBI processes). Exploiting the self-exciting behavior of CBI jump processes, this approach can reproduce the relevant empirical features of spreads between different interbank rates. In particular, we introduce multi-curve models driven by a flow of tempered alpha-stable CBI processes. Such models are especially parsimonious and tractable, and can generate contagion effects among different spreads. We provide a complete analytical framework, including a detailed study of discounted exponential moments of CBI processes. The proposed approach allows for explicit valuation formulae for all linear interest rate derivatives and semi-closed formulae for non-linear derivatives via Fourier techniques and quantization. We show that a simple specification of the model can be successfully calibrated to market data.

Ransomware has emerged as one of the most concerned cyber risks in recent years, which has caused millions of dollars monetary loss over the world. It typically demands a certain amount of ransom payment within a limited timeframe to decrypt the encrypted victim's files. This paper explores whether the ransomware should be paid in a novel game-theoretic model from the perspective of Bayesian game. In particular, the new model analyzes the ransom payment strategies within the framework of incomplete information for both hacker and victim. Our results show that there exist pure and randomized Bayesian Nash equilibria under some mild conditions for the hacker and victim. The sufficient conditions that when the ransom should be paid are presented when an organization is compromised by the ransomware attack. We further study how the costs and probabilities of cracking or recovering affect the expected payoffs of the hacker and the victim in the equilibria. In particular, it is found that the backup option for computer files is not always beneficial, which actually depends on the related cost. Moreover, it is discovered that fake ransomware may be more than expected because of the potential high payoffs. Numerical examples are also presented for illustration.