Agricultural pricing policies are crucial for farm profitability and food security in India. This study analysed how input and output prices significantly influence the profitability of cereals in Karnataka, with the strategic support prices playing a crucial role in maintaining the price parity. The average annual TFP growth was 1.041 per cent. Rising input costs, particularly for human labour, led to reduced profitability for Jowar (6.12 per cent) and Ragi (4.89 per cent). The net effect was adverse for Jowar (-1.50 per cent) and Ragi (-0.86 per cent) due to rising input costs outpacing output prices. The study recommended increasing the MSP for Jowar (60 per cent) and Ragi (46.24 per cent) above the existing levels. A strategic price adjusted for changing input costs can stabilise farm incomes and promote sustainable production, enabling efficient pricing policies.

Small, trade-dependent economies often exhibit limited maritime connectivity, yet empirical evidence on the structural configuration of their container systems remains limited. This study analyzes route concentration and node distributions in Mauritania's maritime container system during 2019-2022 using shipment-level data measured in forty-foot equivalent units (FFE). Routes, origin nodes, destination nodes, and industries are represented as FFE-weighted probability distributions, and concentration and divergence metrics are used to assess structural properties. The results show strong corridor concentration across the seven observed routes (HHI = 0.296), with the top three accounting for approximately 84% of total FFE. Node structures differ by direction: imports are associated with a highly concentrated set of destination nodes (HHI = 0.848), while exports originate from only two origin nodes (HHI = 0.567) and are distributed across a large number of destinations (HHI = 0.053). Industry distributions are more concentrated for exports (HHI = 0.352) than for imports (HHI = 0.096), with frozen fish and seafood accounting for more than 53% of export volume. Temporal analysis shows that route concentration remains stable over time (HHI ~ 0.293-0.303), while node distributions exhibit measurable variation, particularly for export destinations (JSD ~ 0.395) and import origins (JSD ~ 0.250).

This paper considers an insurer with two collaborating business lines that faces three critical decisions: (1) dividend payout, (2) reinsurance coverage, and (3) capital injection between the lines, in the presence of model uncertainty. The insurer considers the reference model to be an approximation of the true model, and each line has its own robustness preference. The reserve level of each line is modeled using a diffusion process. The objective is to obtain a robust strategy that maximizes the expected weighted sum of discounted dividends until the first ruin time, while incorporating a penalty term for the distortion between the reference and alternative models in the worst-case scenario. We completely solve this problem and obtain the value function and optimal (equilibrium) strategies in closed form. We show that the optimal dividend-capital injection strategy is a barrier strategy. The optimal proportion of risk ceded to the reinsurer and the deviation of the worst-case model from the reference model are decreasing with respect to the aggregate reserve level. Finally, numerical examples are presented to show the impact of the model parameters and ambiguity aversion on the optimal strategies.

We develop a semi-static framework for the variance-optimal hedging of multi-asset derivatives exposed to correlation and covariance risk. The approach combines continuous-time dynamic trading in the underlying assets with a static portfolio of auxiliary contingent claims. Using a multivariate Galtchouk--Kunita--Watanabe decomposition, we show that the resulting global mean-variance problem decouples naturally into an inner continuous-time projection onto the space spanned by the underlying assets and an outer finite-dimensional quadratic optimization over the static hedging instruments. To systematically select suitable auxiliary claims, we leverage multidimensional functional spanning theory, establishing that otherwise unhedgeable cross-gamma exposures can be structurally mitigated through static strips of vanilla, product, and spread options. As a central application, we derive explicit semi-static replication formulas for covariance swaps and geometric dispersion trades. Our framework accommodates a broad class of asset dynamics, including quadratic and stochastic Volterra covariance models, as well as affine stochastic covariance models with jumps, yielding tractable semi-closed-form solutions via Fourier transform techniques. Extensive numerical experiments demonstrate that incorporating optimally weighted static strips of cross-asset instruments substantially reduces the mean-squared hedging error relative to purely dynamic benchmark strategies across various model classes.

This paper examines the latent functional block structure of Japan's production network using interregional input-output data. To isolate non-trivial production linkages, we first estimate a structural gravity model to account for spatial frictions and economic scale, and then apply a weighted stochastic blockmodel (SBM) to the resulting residual network. Because these residual linkages often connect distant regions, the SBM is well suited to grouping region-industry pairs based on their shared macroeconomic roles. The results reveal that even after explicitly filtering out the mechanical effects of geographic proximity, the network is organized into functional blocks that maintain a high degree of regional coherence. Beyond this baseline spatial clustering, we find evidence of cross-regional integration, a structural bifurcation between manufacturing and urban services in metropolitan areas, and broadly spanning primary sectors. These findings provide a network-based perspective on regional coordination, offering guidance for how structurally defined production blocks-rather than simple geographic proximity-can inform wide-area policy design.

This paper examines how trade policy uncertainty influences the correlation between U.S. stock indices and short-term government bonds. The objective is to assess whether policy-related shocks, especially those linked to trade tensions, alter the traditional stock-T bill relationship and its implications for investors. We extend the Dynamic Conditional Correlation (DCC) framework by incorporating exogenous variables to account for external shocks. Three specifications are analyzed: one using the Trade Policy Uncertainty (TPU) index, one including a dummy variable reflecting presidential-cycle effects, and one combining both through an interaction term. The analysis is based on daily data for major U.S. stock indices and the 3-month Treasury bill. Results indicate that trade policy uncertainty exerts a significant effect on stock-T bill correlations. Moreover, its influence becomes stronger under specific political conditions, suggesting that political agendas can amplify the impact of trade-related shocks on financial markets. Crucially, augmenting the DCC framework with trade-policy-related variables improves also the economic relevance of correlation forecasts. Therefore, this study contributes to the literature by explicitly integrating policy-related uncertainty into correlation modeling through an augmented DCC framework. The findings provide new insights for portfolio allocation and risk management in environments characterized by heightened trade tensions.

This paper introduces a new extension of the Conditional Autoregressive Value at Risk (CAViaR) model aimed at improving tail risk forecasting across assets. The proposed component-based model, CAViaR with Spillover Effects (CAViaR-SE), decomposes the conditional Value at Risk into a proper-risk component and a spillover component driven by a linear combination of tail risks from influential assets. These assets are selected via a recursive partial correlation algorithm, allowing multiple spillover sources with minimal parameterization. The spillover component acts as a predictable quantile shifter, directly affecting the conditional quantile dynamics rather than the volatility scale. Empirical results on Dow Jones Industrial Average stocks show that spillover effects account for a substantial share of total tail risk and significantly improve out-of-sample tail risk forecasts. Backtesting procedures, together with Model Confidence Set (MCS) analysis, confirm that CAViaR-SE provides well-calibrated risk measures and statistically superior forecasts compared to standard and augmented CAViaR models.

Recent advances in generative AI systems have dramatically reduced the cost of digital production, fueling narratives that widespread participation in software creation will yield a proliferation of viable companies. This paper challenges that assumption. We introduce the Builder Saturation Effect, formalizing a model in which production scales elastically but human attention remains finite. In markets with near-zero marginal costs and free entry, increases in the number of producers dilute average attention and returns per producer, even as total output expands. Extending the framework to incorporate quality heterogeneity and reinforcement dynamics, we show that equilibrium outcomes exhibit declining average payoffs and increasing concentration, consistent with power-law-like distributions. These results suggest that AI-enabled, democratised production is more likely to intensify competition and produce winner-take-most outcomes than to generate broadly distributed entrepreneurial success. Contribution type: This paper is primarily a work of synthesis and applied formalisation. The individual theoretical ingredients - attention scarcity, free-entry dilution, superstar effects, preferential attachment - are well established in their respective literatures. The contribution is to combine them into a unified framework and direct the resulting predictions at a specific contemporary claim about AI-enabled entrepreneurship.

India generates substantial volumes of public agricultural data, yet artificial intelligence (AI) adoption in farming remains limited and largely confined to pilot initiatives. This paper examines this gap by assessing India's agricultural data infrastructure against the requirements of AI systems deployed at scale. Drawing on a systematic review of major national datasets and digital initiatives including Soil Health Cards, crop insurance, AgriStack, and selected state platforms we identify persistent structural constraints, including temporal misalignment between data collection and agricultural decision cycles, spatial fragmentation arising from the absence of common geocodes linking soil, weather, and yield information, limited machine readability due to reliance on static data formats, and unclear governance frameworks that restrict data access and reuse. These deficiencies impede cross-dataset integration and automated decision support, with disproportionate consequences for smallholders, who constitute 86~\% of India's farmers and lack the capacity to compensate for weak data infrastructure. Drawing on implementation evidence from India and comparative international experiences, the paper identifies recurring features associated with scalable digital agriculture systems, including incentives linked to data provision, service bundling through local institutions, and sensor-enabled risk management.

We consider a tick-by-tick model of price formation, in which buy and sell orders are modeled as self-exciting point processes (Hawkes process), similar to the one in [Bacry, Delattre, Hoffmann, Muzy, Modelling microstructure noise with mutually exciting point processes, Quantitative Finance, 2013] and [El Euch, Fukasawa, Rosenbaum, The microstructural foundations of leverage effect and rough volatility, Finance and Stochastics, 2018]. We adopt an agent based approach by studying the aggregation of a large number of these point processes, mutually interacting in a mean-field sense. The financial interpretation of the model is that of an asset on which several labeled agents place buy and sell orders following these point processes, influencing the price. The mean-field interaction introduces positive correlations between order volumes coming from different agents that reflect features of real markets such as herd behavior and contagion. When the large scale limit of the aggregated asset price is computed, if parameters are set to a critical value, a singular phenomenon occurs: the aggregated model converges to a stochastic volatility model with leverage effect and faster-than-linear mean reversion of the volatility process. The faster-than-linear mean reversion of the volatility process is supported by econometric evidence, and we have linked it in [Dai Pra, Pigato, Multi-scaling of moments in stochastic volatility models, Stochastic Processes and their Applications, 2015] to the observed multifractal behavior of assets prices and market indices. This seems connected to the Statistical Physics perspective that expects anomalous scaling properties to arise in the critical regime.

This paper proposes a unified theoretical model to identify and test a comprehensive set of probabilistic updating biases within a single framework. The model achieves separate identification by focusing on the updating of belief distributions, rather than point beliefs alone. Estimating the model in a laboratory experiment reveals significant individual heterogeneity: all tested biases are present and exhibit systematic co-occurrence patterns across individuals, with motivated-belief biases (optimism and pessimism) and sequence-related biases (gambler's and hot-hand fallacy) emerging as key drivers of biased inference. At the population level most biases average out, but base-rate neglect remains a persistent influence. This study contributes to the belief-updating literature by providing a methodological toolkit for researchers examining links between conflicting biases and connections between updating biases and other behavioral phenomena.

This paper provides some of the first large-scale descriptive evidence on how consumers adopt and use platform-embedded shopping AI in e-commerce. Using data on 31 million users of Ctrip, China's largest online travel platform, we study "Wendao," an LLM-based AI assistant integrated into the platform. We document three empirical regularities. First, adoption is highest among older consumers, female users, and highly engaged existing users, reversing the younger, male-dominated profile commonly documented for general-purpose AI tools. Second, AI chat appears in the same broad phase of the purchase journey as traditional search and well before order placement; among journeys containing both chat and search, the most common pattern is interleaving, with users moving back and forth between the two modalities. Third, consumers disproportionately use the assistant for exploratory, hard-to-keyword tasks: attraction queries account for 42% of observed chat requests, and chat intent varies systematically with both the timing of chat relative to search and the category of products later purchased within the same journey. These findings suggest that embedded shopping AI functions less as a substitute for conventional search than as a complementary interface for exploratory product discovery in e-commerce.

Consider two sequences of heterogeneous and independent portfolios of risks $T_1,T_2,\ldots$ and $T^*_{1}, T^*_{2},\ldots$ and, let $N_1$ and $N_2$ be two positive integer-valued random variables, independent of $T_i'$ and $T^*_i$, respectively. In this article, we investigate different stochastic inequalities involving $\min\{T_1,\ldots,T_{N_1}\}$ and $\min\{T^*_1,\ldots,T^*_{N_2}\},$ and $\max\{T_1,\ldots,T_{N_1}\}$ and $\max\{T^*_1,\ldots,T^*_{N_2}\}$ in the sense of usual stochastic order and reversed hazard rate order concerning maltivariate chain majorization order. These new results strengthen and generalize some of the well known results in the literature, including \cite{barmalzan2017ordering}, \cite{balakrishnan2018} and \cite{kundu2021_shock} for the case of random claim sizes. Different numerical examples are provided to highlight the applicability of this work. Finally, some interesting applications of our results in reliability theory and auction theory are presented.

We conduct the first laboratory school choice experiment in which parents-the relevant decision makers in the field-are the experimental subjects. We compare Deferred Acceptance (DA) with two manipulable but potentially more efficient alternatives: Efficiency-Adjusted Deferred Acceptance (EADA) and the Rank-Minimizing mechanism (RM). We find that all mechanisms are frequently manipulated, with no significant differences in truth-telling rates. Parents and students manipulate at similar rates, supporting the external validity of student-based experiments, though students make significantly more obvious errors, suggesting parents' deviations are more deliberate. Despite widespread manipulation, the predicted welfare-stability tradeoff largely survives: DA never produces Pareto-efficient allocations yet generates little justified envy; whereas RM delivers substantial efficiency gains at a meaningful stability cost. EADA occupies a middle ground: its efficiency gains over DA are modest and imprecisely estimated yet double justified envy. Higher cognitive ability is associated with more deviations, and under EADA with worse outcomes. While DA does not induce truth-telling, it is the only mechanism in which manipulation never pays off and rarely changes outcomes.

Martingale Optimal Transport (MOT) provides a framework for robust pricing and hedging of illiquid derivatives. Classical MOT enforces exact calibration of model marginals to the mid-prices of vanilla options. Motivated by the industry practice of fitting bid and ask marginals to vanilla prices, we introduce a relaxation of MOT in which model-implied volatilities are only required to lie within observed bid--ask spreads; equivalently, model marginals lie between the bid and ask marginals in convex order. The resulting Bid--Ask MOT (BAMOT) yields realistic price bounds for illiquid derivatives and, via strong duality, can be interpreted as the superhedging price when short and long positions in vanilla options are priced at the bid and ask, respectively. We further establish convergence of BAMOT to classical MOT as bid--ask spreads vanish, and quantify the convergence rate using a novel distance intrinsically linked to bid--ask spreads. Finally, we support our findings with several synthetic and real-data examples.