Where will ECONVN 2026/2027 be held?

The conference will be held in Ho Chi Minh City, VNM.

What is the primary focus of ECONVN 2026/2027?

The conference focuses on chaotic dynamics in economics and finance.

The Tenth International Econometric and Financial Conference of Vietnam (ECONVN 2026/2027)

Name: The Tenth International Econometric and Financial Conference of Vietnam (ECONVN 2026/2027)
Start: 2026-10-12T00:00:00Z
End: 2026-10-14T00:00:00Z
Location: Ho Chi Minh City

Oct 12–14, 2026Ho Chi Minh City, VietnamIn-person

Registration open

The Tenth International Econometric and Financial Conference of Vietnam (ECONVN 2026/2027) is scheduled to take place in Ho Chi Minh City, Vietnam. This conference serves as a platform for researchers and practitioners to discuss and analyze chaotic dynamics within economics and finance. The event is designed to foster intellectual exchange and present new research findings in these complex fields.

Committees & topics

Topics
Nonlinear Dynamics and Chaos Theory in Economic and Financial Systems · Nonlinear Time Series Analysis and Chaotic Econometrics · Machine Learning and AI for Detecting Chaotic Patterns in Economic Data · Bayesian and Simulation-Based Methods for Nonlinear Dynamic Systems · Chaotic Dynamics in Business Cycles and Economic Growth · Climate Change, Energy Transition, and Macroeconomic Instability · Global Financial Integration and Nonlinear Spillover Effects · Agent-Based Modeling and Simulation of Chaotic Economic Systems

Why it matters

Understanding chaotic dynamics in economics and finance is crucial for policymakers, financial institutions, and businesses operating in an increasingly interconnected global economy. These dynamics can lead to significant market volatility, financial crises, and unpredictable economic shifts. By bringing together experts to explore these phenomena, the conference contributes to the development of more robust economic models and more effective policy responses.

The insights shared at ECONVN 2026/2027 can help in anticipating and mitigating the impacts of economic shocks, particularly relevant for emerging economies like VNM. The focus on econometric and financial modeling provides tools for better forecasting and risk management, which are vital for sustainable economic growth and stability. The discussions can also highlight regional specificities of chaotic dynamics, offering tailored solutions for Asian markets.

How to prepare

Delegates preparing for ECONVN 2026/2027 should focus on developing a strong understanding of advanced econometric techniques and financial modeling, particularly those applicable to non-linear and chaotic systems. Familiarity with recent research in areas such as market microstructure, behavioral finance, and systemic risk will be beneficial. Participants should also consider how their work contributes to the broader understanding of economic stability and resilience in the face of unpredictable events.

For those presenting, it is advisable to clearly articulate the methodology used and the implications of their findings for policy or practice. Given the international nature of the conference, presenters should be prepared to discuss their research within a global context, potentially drawing comparisons or contrasts with different economic environments. Engaging with the work of other researchers and being open to interdisciplinary perspectives will enhance the conference experience and potential for collaboration.

Country perspectives

Where the most-relevant 1 countries stand on the core issues in play. Click through for the full country profile.

Vietnam

As the host nation, VNM is keenly interested in the application of econometric and financial insights to its rapidly developing economy.

Role in topic

VNM provides a dynamic economic environment for studying chaotic dynamics, particularly as it integrates further into global markets. The conference in Ho Chi Minh City offers a local perspective on these global phenomena.

Topics & background

The background behind each topic and the actors that shape it.

Nonlinear Dynamics and Chaos Theory in Economic and Financial Systems

The application of nonlinear dynamics to economics emerged in the 1980s as researchers grew dissatisfied with linear equilibrium models that failed to explain persistent volatility, asset bubbles, and abrupt regime shifts. Building on the work of Edward Lorenz in meteorology and Benoit Mandelbrot's studies of cotton prices and fractal market structures, economists such as William Brock, José Scheinkman, and Richard Day demonstrated that deterministic chaotic processes could generate the irregular patterns observed in macroeconomic and financial data. The 1987 stock market crash and subsequent crises reinforced interest in models featuring strange attractors, bifurcations, and sensitive dependence on initial conditions. The core dispute concerns whether financial markets and macro aggregates are genuinely chaotic, merely stochastic with fat tails, or some hybrid in which low-dimensional nonlinear structure coexists with high-dimensional noise. Empirical tests such as the BDS statistic, correlation dimension estimation, and Lyapunov exponents have produced mixed evidence, partly because finite and noisy economic samples obscure true dynamics. The 2008 global financial crisis and the 2020 pandemic shock renewed attention to tipping points, endogenous instability, and Minsky-style cascades that conventional DSGE frameworks struggled to anticipate. Today the field sits at the intersection of econophysics, complexity economics, and mainstream macro-finance. Central banks and the BIS increasingly cite nonlinear feedback loops in stability assessments, while academic centers in the United States, the United Kingdom, France, Japan, and Vietnam pursue chaos-theoretic tools for forecasting, systemic risk monitoring, and policy design under deep uncertainty.

Key players

Nonlinear Time Series Analysis and Chaotic Econometrics

Nonlinear time series econometrics developed from the recognition that ARMA and other linear models could not capture asymmetric business cycles, volatility clustering, or threshold behavior in macro and financial data. Beginning in the 1980s, Howell Tong's threshold autoregressive models, Clive Granger and Timo Teräsvirta's smooth-transition frameworks, and Robert Engle's ARCH/GARCH family transformed empirical practice. Parallel advances in chaotic econometrics—correlation integrals, BDS tests, recurrence analysis, and Lyapunov exponent estimation—offered tools to detect deterministic nonlinear structure beneath apparent randomness. The central methodological dispute is identification: distinguishing chaos from stochastic nonlinearity, structural breaks, or long-memory processes in samples that are short by physical-science standards. Practitioners debate the reliability of dimension estimates for macroeconomic series, the role of measurement error, and how to combine nonlinear filters with regime-switching and state-space methods. The rise of high-frequency financial data has eased some sample-size constraints but introduced microstructure noise that complicates inference. Current frontiers include nonlinear cointegration, copula-based dependence under nonlinearity, mixed-frequency nonlinear models, and the integration of machine learning with classical nonlinear econometrics. Central banks and statistical agencies in the EU, the US, and Asia increasingly publish nonlinear nowcasts, and researchers in emerging markets such as Vietnam, Indonesia, and Brazil apply these tools to capital flows and exchange-rate dynamics that exhibit pronounced regime-dependence.

Key players

Machine Learning and AI for Detecting Chaotic Patterns in Economic Data

The application of machine learning to economic forecasting accelerated after 2010 as computational capacity, data availability, and algorithmic innovations converged. Methods including random forests, gradient boosting, support vector machines, and deep neural networks proved able to approximate highly nonlinear mappings without requiring explicit functional forms. Reservoir computing, recurrent neural networks (LSTM, GRU), and transformer architectures have shown particular promise for detecting low-dimensional chaotic signatures embedded in noisy economic series, building on Takens' embedding theorem and modern manifold-learning techniques. The core dispute concerns interpretability, robustness, and economic meaning. Critics argue that black-box models risk overfitting non-stationary economic data and offer little structural insight, while proponents counter that hybrid approaches—physics-informed neural networks, neural ODEs, and ML-augmented DSGE models—can recover chaotic dynamics that classical estimators miss. Regulators, notably the Financial Stability Board and the ECB, have raised concerns about model risk, data governance, and procyclical behavior when ML tools are deployed for trading or supervision. The field stands at a turning point in which large foundation models are being adapted to macroeconomic nowcasting, fraud detection, and systemic-risk surveillance. Sovereign initiatives in the United States, China, the EU, and Singapore are funding AI-for-finance research, while emerging economies including Vietnam pursue ML-based tools for early-warning systems against capital-flow reversals and commodity-price chaos.

Key players

Bayesian and Simulation-Based Methods for Nonlinear Dynamic Systems

Bayesian econometrics matured in the 1990s and 2000s as Markov Chain Monte Carlo (MCMC), particle filtering, and sequential Monte Carlo methods made estimation of nonlinear, non-Gaussian state-space models computationally feasible. Researchers including Christopher Sims, John Geweke, and Frank Schorfheide demonstrated how Bayesian inference could discipline DSGE models, while particle filters developed by Doucet, Pitt, and others enabled likelihood evaluation for systems with stochastic volatility, regime switching, and other nonlinearities. Approximate Bayesian Computation (ABC) extended these methods to models whose likelihoods are intractable but simulable, including agent-based and chaotic systems. The central methodological dispute concerns prior sensitivity, identification, and computational cost. Critics argue that Bayesian estimates of nonlinear DSGE and chaotic models can be driven by priors when data are weakly informative, while defenders emphasize Bayesian methods' coherent treatment of uncertainty and ability to incorporate expert judgment. The rise of Hamiltonian Monte Carlo (via Stan), variational inference, and GPU-accelerated particle methods has expanded what is tractable, but bridging Bayesian inference with genuinely chaotic systems—where likelihoods can be pathological—remains an open problem. Central banks including the Federal Reserve, ECB, Bank of England, and Riksbank now routinely use Bayesian nonlinear methods for policy analysis. Academic frontiers include Bayesian neural networks, simulation-based inference (SBI) using normalizing flows, and likelihood-free methods for high-dimensional chaotic and agent-based models relevant to financial-stability assessment.

Key players

Chaotic Dynamics in Business Cycles and Economic Growth

Debate over endogenous, nonlinear sources of business cycles dates to Goodwin's predator-prey model (1967) and Kaldor's nonlinear investment functions, but gained renewed prominence in the 1980s through Richard Day, Jean-Michel Grandmont, and Michele Boldrin, who showed that simple deterministic growth and overlapping-generations models could generate chaotic trajectories. This challenged the dominant view—rooted in real business cycle theory—that fluctuations are primarily driven by exogenous stochastic shocks to an otherwise stable equilibrium. The core dispute is whether observed macroeconomic volatility reflects endogenous nonlinear dynamics (financial cycles, Minskyan instability, sectoral interactions), exogenous shocks amplified by linear propagation, or some combination. The 2008 crisis, the eurozone debt crisis, and the post-pandemic inflation surge intensified interest in endogenous models featuring credit cycles, heterogeneous agents, and tipping points. Yet identifying chaos in macro data is hampered by short samples, structural change, and policy intervention that may itself stabilize or destabilize dynamics. Current research integrates chaotic growth models with heterogeneous-agent New Keynesian (HANK) frameworks, financial-accelerator mechanisms, and complexity-based productivity dynamics. Policy institutions including the IMF, BIS, and OECD increasingly recognize nonlinear cycle features in their surveillance, while emerging economies face particular concerns about commodity-driven and capital-flow-driven chaotic fluctuations.

Key players

Climate Change, Energy Transition, and Macroeconomic Instability

Climate change entered mainstream macroeconomics with William Nordhaus's DICE model in the 1990s, but the field has shifted from steady-state cost-benefit analysis toward recognition of nonlinear tipping points, fat-tailed damage functions (Weitzman), and macro-financial transition risks. The 2015 Paris Agreement, the 2017 Task Force on Climate-related Financial Disclosures (TCFD), and the 2017 Network for Greening the Financial System (NGFS) institutionalized climate-economy linkages, while the 2022 energy crisis following Russia's invasion of Ukraine demonstrated how rapid energy-price shocks can destabilize inflation, growth, and fiscal positions simultaneously. The core dispute concerns the pace, sequencing, and distributional burden of the energy transition, and whether climate risks can be modeled within standard macro frameworks or require fundamentally nonlinear, complexity-based tools. Disorderly transition scenarios—stranded fossil assets, carbon-price shocks, technology disruption—risk triggering financial instability, while delayed transition raises the probability of catastrophic physical damages. NGFS scenarios and IPCC AR6 highlight nonlinear interactions among temperature, productivity, migration, and sovereign-debt dynamics. Today the issue is contested across producer and consumer states, with the EU advancing carbon border adjustments (CBAM), the US pursuing industrial policy via the Inflation Reduction Act, China dominating clean-tech supply chains, and oil exporters managing transition risk. Emerging economies including Vietnam face the dual challenge of decarbonizing rapidly growing energy systems while preserving macroeconomic stability and export competitiveness.

Key players

Global Financial Integration and Nonlinear Spillover Effects

Global financial integration accelerated after the 1980s with capital-account liberalization, the rise of cross-border banking, and the expansion of global value chains. The 1997 Asian financial crisis, the 2008 global financial crisis, and the 2013 'taper tantrum' demonstrated that interconnected markets transmit shocks in nonlinear, often abrupt ways—through fire sales, margin spirals, currency mismatches, and confidence collapses. Hélène Rey's 'global financial cycle' hypothesis and BIS research on the dollar funding system have reframed the conventional trilemma as a dilemma in which capital mobility alone constrains monetary autonomy. The core dispute is how to measure and govern nonlinear spillovers. Network econometrics, threshold VARs, quantile spillover indices (Diebold-Yilmaz, Adrian-Brunnermeier CoVaR), and copula-based contagion models have proliferated, but consensus on early-warning indicators remains elusive. Policy debate divides advocates of free capital mobility, proponents of macroprudential and capital-flow management tools (now endorsed by the IMF's Institutional View), and reformers seeking a stronger global financial safety net. The 2022-2024 monetary tightening cycle, dollar appreciation, and sovereign-debt distress in frontier markets have renewed attention to nonlinear spillovers from advanced-economy policy. Emerging Asian economies, including Vietnam, face heightened exposure through trade-finance linkages, foreign-currency borrowing, and portfolio flows, while regional safety nets such as the Chiang Mai Initiative Multilateralization remain underutilized.

Key players

Agent-Based Modeling and Simulation of Chaotic Economic Systems

Agent-based modeling (ABM) emerged in economics in the 1990s through the Santa Fe Institute and the work of W. Brian Arthur, John Holland, and Leigh Tesfatsion, offering a computational alternative to representative-agent equilibrium models. ABMs construct economies from heterogeneous, boundedly rational agents whose interactions generate aggregate dynamics—often featuring fat tails, bubbles, crashes, and chaotic regimes—without imposing equilibrium. The 2008 crisis prompted serious institutional interest, with Andy Haldane (then Bank of England) and the OECD's Andrew Haldane and Doyne Farmer calling for ABMs in systemic-risk analysis. The central dispute is empirical validation. Critics argue that ABMs contain too many free parameters and lack the disciplined estimation procedures of DSGE models, while proponents counter that simulation-based inference, ABC, and machine-learning surrogates now permit rigorous calibration to data. ABMs have demonstrated value in housing markets (Bank of England), interbank networks (ECB), and macro models such as the EURACE and Schumpeter-meeting-Keynes frameworks developed in Italy and Germany. Current frontiers include large-scale ABMs combining heterogeneous households, firms, and banks with climate and pandemic shocks; integration of ABMs with reinforcement learning to model adaptive expectations; and use of ABMs as policy testbeds where chaotic, off-equilibrium dynamics matter. Institutions including the Bank of England, ECB, Banca d'Italia, and OECD now maintain ABM capacity, and emerging-market researchers, including in Vietnam, are adopting ABMs for financial-stability stress testing.

Key players

Resources

News, lessons, and country profiles to prep for The Tenth International Econometric and Financial Conference of Vietnam (ECONVN 2026/2027).

Recent reporting to ground your prep

Actor Vijay's Political Move

US Job Openings at 6.7M

Trump's Economic Reset

Trump's Impact on GOP

Lessons

Bite-sized lessons to build the basics

Dynamic Analysis

Hidden Stakeholders

Gender Dynamics Across Cultures

What Is Stakeholder Mapping?

Courses

Guided courses that go deeper on the topic

Advanced Negotiation

Multilateral Negotiation

Policy Brief Writing

Conflict Resolution

The states in play, with the data that shapes their stance

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

By committee

Frequently asked questions

The Tenth International Econometric and Financial Conference of Vietnam (ECONVN 2026/2027)

Oct 12–14, 2026Ho Chi Minh City, VietnamIn-person

Registration open

Committees & topics

Topics
Nonlinear Dynamics and Chaos Theory in Economic and Financial Systems · Nonlinear Time Series Analysis and Chaotic Econometrics · Machine Learning and AI for Detecting Chaotic Patterns in Economic Data · Bayesian and Simulation-Based Methods for Nonlinear Dynamic Systems · Chaotic Dynamics in Business Cycles and Economic Growth · Climate Change, Energy Transition, and Macroeconomic Instability · Global Financial Integration and Nonlinear Spillover Effects · Agent-Based Modeling and Simulation of Chaotic Economic Systems

Why it matters

How to prepare

Country perspectives

Where the most-relevant 1 countries stand on the core issues in play. Click through for the full country profile.

Vietnam

As the host nation, VNM is keenly interested in the application of econometric and financial insights to its rapidly developing economy.

Role in topic

Topics & background

The background behind each topic and the actors that shape it.

Nonlinear Dynamics and Chaos Theory in Economic and Financial Systems

Key players

Nonlinear Time Series Analysis and Chaotic Econometrics

Key players

Machine Learning and AI for Detecting Chaotic Patterns in Economic Data

Key players

Bayesian and Simulation-Based Methods for Nonlinear Dynamic Systems

Key players

Chaotic Dynamics in Business Cycles and Economic Growth

Key players

Climate Change, Energy Transition, and Macroeconomic Instability

Key players

Global Financial Integration and Nonlinear Spillover Effects

Key players

Agent-Based Modeling and Simulation of Chaotic Economic Systems

Key players

Resources

News, lessons, and country profiles to prep for The Tenth International Econometric and Financial Conference of Vietnam (ECONVN 2026/2027).

Recent reporting to ground your prep

Actor Vijay's Political Move

US Job Openings at 6.7M

Trump's Economic Reset

Trump's Impact on GOP

Lessons

Bite-sized lessons to build the basics

Dynamic Analysis

Hidden Stakeholders

Gender Dynamics Across Cultures

What Is Stakeholder Mapping?

Courses

Guided courses that go deeper on the topic

Advanced Negotiation

Multilateral Negotiation

Policy Brief Writing

Conflict Resolution

The states in play, with the data that shapes their stance

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

Country dossier

By committee

Committees & topics

Why it matters

How to prepare

Country perspectives

Topics & background

Resources

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Frequently asked questions

Committees & topics

Why it matters

How to prepare

Country perspectives

Topics & background

Resources

Related conferences

3rd ASEAN Future Forum (AFF 2026)

National Conference to Thoroughly Understand and Implement Resolution No. 06-NQ/TW on the Implementation of the Party's 14th National Congress's Foreign Policy

International Conference on Political Strategy and International Relations

34th HPAIR Asia Conference 2026

HPAIR ACONF 2026: Rising with Resolve

International Conference on Human Rights, Civil Wars, and Violence Studies (ICHRCWVS) 2026

Frequently asked questions