arXiv subjects

Avishek Bhandari

Publications and source records attributed to Avishek Bhandari.

10 recordsLinked to original sources

arXiv2026

Measuring the Arrow of Time: Identification, Estimation, and Inference for Directional Structure in Multivariate Time Series

Many questions across the sciences take the same form: several coupled series are observed together, and the analyst wants to know not merely that they move together but which one moves first, and how strongly. This paper sets out a complete method built on one organising idea: the direction of a coupled system is exactly the part of its behaviour that changes when the record is played backwards. Tools built on contemporaneous covariance alone (correlation matrices, distance measures, spanning trees, undirected centralities, principal components) carry no information about direction: a reversible system and a circulating one can share identical covariance at every sampling of the same point-in-time record. Formally, direction is a circulation matrix carried by the lagged covariance. Its vanishing is exactly statistical time reversibility for linear systems, feature maps carry the characterisation to nonlinear ones, and under the Gaussian benchmark its magnitude is an entropy-production functional of the identified circulation, the quadratic component of the divergence per unit time between the forward and reversed records. Around this estimand we build a cross-fitted estimator removing first-order bias, delete-block jackknife standard errors, and a randomisation test exact under its stated block null, with a familywise correction and a nonlinear extension. A sampling theory says when the arrow is measurable at all, and a design layer separates transmission from the ordering of clocks. A laboratory of four systems with known answers compares the method with correlation networks, Granger causality, transfer entropy, and connectedness indices, reporting the failures of each when read as a measure of direction, including our own. Complete algorithms and worked examples in two languages make the paper the base reference for a series of applications.

econ.EM↗

arXiv2026

Choice at Finite Capacity: The Bounded Agent as an Information Channel and the Recovery of Walrasian Demand

Standard economics assumes the consumer as a flawless calculator who always buys the best basket it can afford. This paper models the shopper instead as a limited information channel: it compresses its world to the detail its attention affords, so its choice is a probability distribution, not a single basket. The textbook consumer returns exactly as the unlimited-attention limit, while at the zero-attention end the shopper falls back on pure habit. The central result is about how this shopper's demand responds to price changes. That pattern of responses is just a rescaling of how the shopper's own choices vary and move together, so it comes out symmetric. And provided the budget really binds, because the shopper wants more than it can afford, raising a good's own price lowers demand for it once buying power is held fixed. So the downward pull comes from the budget and from compression, not from rationality. The framework also covers an artificial agent running a limited-capacity policy. A worked two-good quadratic consumer carries every quantity in closed form.

econ.TH↗

arXiv2026

Rational Bubbles at the Spectral Edge: An Operator-Spectral Theory of Fragility, Identification and Finite-Sample Certification

When markets move more and more in lockstep, are they drifting towards the point where a price bubble becomes possible, and can that drift be measured before the crossing? This paper joins two long-separate ideas, that a rational bubble is a price outgrowing its dividends and that a crisis threshold can be read off the strength of a market's single dominant factor, onto one object recovered from the data: a summary of how asset returns move together, paired with a discount rate. We call this crossing point the fragility edge and show it plays three roles at once. A stated discipline says what the data support: the edge firmly, with a margin of error; whether a bubble exists, only roughly; which asset carries it, not at all. Across eighteen global equity indices from 2004 to 2024, that dominant factor strengthens in every documented crisis, the market collapsing from about six to about four independent factors; once the discount is set so that calm markets sit at the edge, this strength crosses it in crisis. These readings coincide with crises, not forecasts.

econ.TH↗

arXiv2026

Equilibrium as a Limit: The Competitive Canon Nested in an Adaptive, Information-Theoretic Economy

The competitive equilibrium of general equilibrium theory exists as a fixed point and is, by the theorys own results on aggregate excess demand, in general silent on whether that fixed point is unique, stable, or attained. This paper takes the economy to be not a configuration to be solved for but a process to be recovered, an asymptotically mean stationary information source carrying a partially identified operator of statistical dependence, populated by agents that are finite-capacity information channels. Within this adaptive order the competitive, rational expectations equilibrium is recovered exactly, as a joint limit taken along an explicit scaling path. Three parameter limits and two fixed-point conditions deliver it, the entropy rate falls to zero, agent channel capacity diverges, selection intensity grows infinitely sharp, adaptive learning reaches its expectationally stable rest point, and the recovered structure ceases to coevolve. At that corner the limiting object satisfies the axioms of the canon and its rest state is a Walrasian equilibrium, away from it the adaptive economy is a strict generalisation, carrying a positive entropy rate and a recovered dependence structure that the equilibrium primitive cannot express. We give the nesting as a theorem, establish the result by result correspondence with existence, with the Sonnenschein Mantel Debreu indeterminacy, and with the regular economies recovery, and characterise exactly what the equilibrium limit erases.

econ.TH↗

arXiv2026

Econstellar: An Open-Source AI-Augmented Research Engine for Computational Financial Econometrics

Turning a promising economic idea into a credible empirical finding is, in practice, an expensive undertaking: it demands a great deal of specialised computation, and the results are seldom released in a form that others can check or build upon. Econstellar is our response. It is an open, publicly served research engine that runs publication-grade financial econometrics from an ordinary web browser and explains what the results mean, so that a reader does not merely read a finding but can re-run it, vary its inputs, and trace exactly how it was produced. Three choices give the system its character. The heavy computation is placed on the processor that suits it, rather than forced onto hardware ill-matched to the task, which is much of the reason analysis of this kind is so rarely served to the public. An artificial-intelligence assistant selects and interprets the analyses but never originates a number, so every quantity it reports is a real computation the reader can reproduce. And the engine a visitor exercises is the same code that produced the figures in our published research. We expose seventeen econometric methods, each reported with a verified live value and reproducible at the public endpoint, computed under a single discipline: prices are treated as non-stationary and all methods are applied to returns. The system also regenerates, on demand, the headline result of an accompanying study of financial contagion, from the package that generated it. The platform is the working core of an active research programme spanning three software releases and three preprints, and it is available now, free and open-source, at a live public address. Our aim is a simple one: to shorten the distance between a research claim and the moment another person can independently verify it.

econ.EM↗

arXiv2026

Scale-Ordered Contagion: A Spectral Theory of Heterogeneous Information Adaptation in Financial Networks

In this paper, an attempt is made to examine how the speed at which financial markets absorb information governs the way shocks travel between them. It may be noted that a market which digests news slowly will register an incoming shock more gradually, and hence over longer horizons, than a market which reacts quickly. Building on the Heterogeneous Agents Contagion versus Interdependence (HACI) framework, in which advanced economies adapt quickly and emerging economies slowly, we develop a spectral theory of contagion in which both the originating and the receiving market filter the shock. The central result is an intuitive one: the slower of the two markets determines the time horizon over which contagion is felt most strongly. From this we obtain three testable predictions, jointly the Scale-Ordered Contagion Hypothesis, namely that contagion involving slower markets peaks at longer horizons; that the horizon pattern is the same in both directions for any pair of markets; and that only the strength of contagion, and not its timing, differs by direction. We then turn the theory into an estimator that recovers each market's speed of adaptation from the data, and bring the predictions to G20 equity markets over the period 2006 to 2026. The results are supportive, though we report them honestly: the horizon-ordering prediction holds (p=0.042); the symmetry prediction, which is the sharpest of the three, holds for all twenty-eight market pairs (p>0.05); the strength-asymmetry prediction lies in the predicted direction but is not statistically significant; and the method identifies India and China cleanly as the slowest adapters, though the fastest markets cannot be told apart from daily data. The framework thus offers a simple and testable account of why short-horizon and long-horizon contagion differ systematically with the speeds of the markets involved.

econ.EM↗

arXiv2026

What Drives Contagion? Identifying and Attributing Cross-Border Transmission Mechanisms

We address the joint detection-and-attribution problem in cross-border financial contagion through a two-stage framework. The first stage applies wavelet-quantile transfer entropy across time-scales and lower, median, and upper-tail quantiles. The second stage attributes each significant link to one of five channels comprising of i) Trade, ii) Financial, iii) Geopolitical, iv) Behavioural, and v) Monetary Policy, via instrumental-variables two-stage least squares with channel-specific external instruments, LASSO-based instrument selection (Belloni, Chernozhukov and Hansen, 2014), local projections at one-, five-, and twenty-two-day horizons (Jorda, 2005), heteroskedasticity-based identification (Rigobon, 2003) for episodes in which over-identification is rejected, and Cinelli-Hazlett (2020) sensitivity bounds. The framework is applied to 18 G20 equity markets across eight crisis sub-periods spanning January 2006 to March 2026. Network density varies meaningfully across sub-periods (range 14% to 32%). Dominant-channel identification is robust across methods in the Pre-Crisis baseline and the European Sovereign Debt Crisis, both dominated by financial frictions; for the remaining six episodes identification is method-sensitive, and we report the share posterior alongside an explicit identification-status classification. Trade is empirically prominent across all post-2007 episodes, ranging from 9% during Pre-Crisis to 28% during the Global Financial Crisis. The behavioural channel is bounded above by 22% across all eight episodes under the de-confounded composite. The framework provides a methodologically disciplined account of cross-border contagion mechanisms and offers identification-status disclosure not systematically present in the existing literature.

econ.GN↗

arXiv2025

Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets

This paper introduces a novel framework for analyzing systemic risk in financial markets through multi-scale network dynamics using Model Context Protocol (MCP) for agent communication. We develop an integrated approach that combines transfer entropy networks, agent-based modeling, and wavelet decomposition to capture information flows across temporal scales implemented in the MCPFM (Model Context Protocol Financial Markets) R package. Our methodology enables heterogeneous financial agents including high-frequency traders, market makers, institutional investors, and regulators to communicate through structured protocols while maintaining realistic market microstructure. The empirical analysis demonstrates that our multi-scale approach reveals previously hidden systemic risk patterns, with the proposed systemic risk index achieving superior early warning capabilities compared to traditional measures. The framework provides new insights for macroprudential policy design and regulatory intervention strategies. The complete implementation is available as an open-source R package at https://github.com/avishekb9/MCPFM to facilitate reproducible research and practical applications.

q-fin.RM↗

arXiv2020

Long memory in select stock returns using an alternative wavelet log-scale alignment approach

This study investigates the efficiency of some select stock markets. Using an improved wavelet estimator of long range dependence, we show evidence of long memory in the stock returns of some emerging Asian economies. However, developed markets of Europe and the United States did not exhibit long memory thereby confirming the efficiency of developed stock markets. On the other hand, emerging Asian markets are found to be less efficient as long memory is more pronounced in these markets.

q-fin.ST↗

arXiv2020

A wavelet analysis of inter-dependence, contagion and long memory among global equity markets

This study attempts to investigate into the structure and features of global equity markets from a time-frequency perspective. An analysis grounded on this framework allows one to capture information from a different dimension, as opposed to the traditional time domain analyses, where multiscale structures of financial markets are clearly extracted. In financial time series, multiscale features manifest themselves due to presence of multiple time horizons. The existence of multiple time horizons necessitates a careful investigation of each time horizon separately as market structures are not homogenous across different time horizons. The presence of multiple time horizons, with varying levels of complexity, requires one to investigate financial time series from a heterogeneous market perspective where market players are said to operate at different investment horizons. This thesis extends the application of time-frequency based wavelet techniques to: i) analyse the interdependence of global equity markets from a heterogeneous investor perspective with a special focus on the Indian stock market, ii) investigate the contagion effect, if any, of financial crises on Indian stock market, and iii) to study fractality and scaling properties of global equity markets and analyse the efficiency of Indian stock markets using wavelet based long memory methods.

econ.EM↗