Vector Autoregression (VAR)

Market Reactions and Information Spillovers in Bank Mergers: A Multi-Method Analysis of the Japanese Banking Sector ArXiv ID: 2512.06550 “View on arXiv” Authors: Haibo Wang, Takeshi Tsuyuguchi Abstract Major bank mergers and acquisitions (M&A) transform the financial market structure, but their valuation and spillover effects remain open to question. This study examines the market reaction to two M&A events: the 2005 creation of Mitsubishi UFJ Financial Group following the Financial Big Bang in Japan, and the 2018 merger involving Resona Holdings after the global financial crisis. The multi-method analysis in this research combines several distinct methods to explore these M&A events. An event study using the market model, the capital asset pricing model (CAPM), and the Fama-French three-factor model is implemented to estimate cumulative abnormal returns (CAR) for valuation purposes. Vector autoregression (VAR) models are used to test for Granger causality and map dynamic effects using impulse response functions (IRFs) to investigate spillovers. Propensity score matching (PSM) helps provide a causal estimate of the average treatment effect on the treated (ATT). The analysis detected a significant positive market reaction to the mergers. The findings also suggest the presence of prolonged positive spillovers to other banks, which may indicate a synergistic effect among Japanese banks. Combining these methods provides a unique perspective on M&A events in the Japanese banking sector, offering valuable insights for investors, managers, and regulators concerned with market efficiency and systemic stability ...

Community-level Contagion among Diverse Financial Assets ArXiv ID: 2509.15232 “View on arXiv” Authors: An Pham Ngoc Nguyen, Marija Bezbradica, Martin Crane Abstract As global financial markets become increasingly interconnected, financial contagion has developed into a major influencer of asset price dynamics. Motivated by this context, our study explores financial contagion both within and between asset communities. We contribute to the literature by examining the contagion phenomenon at the community level rather than among individual assets. Our experiments rely on high-frequency data comprising cryptocurrencies, stocks and US ETFs over the 4-year period from April 2019 to May 2023. Using the Louvain community detection algorithm, Vector Autoregression contagion detection model and Tracy-Widom random matrix theory for noise removal from financial assets, we present three main findings. Firstly, while the magnitude of contagion remains relatively stable over time, contagion density (the percentage of asset pairs exhibiting contagion within a financial system) increases. This suggests that market uncertainty is better characterized by the transmission of shocks more broadly than by the strength of any single spillover. Secondly, there is no significant difference between intra- and inter-community contagion, indicating that contagion is a system-wide phenomenon rather than being confined to specific asset groups. Lastly, certain communities themselves, especially those dominated by Information Technology assets, tend to act as major contagion transmitters in the financial network over the examined period, spreading shocks with high densities to many other communities. Our findings suggest that traditional risk management strategies such as portfolio diversification through investing in low-correlated assets or different types of investment vehicle might be insufficient due to widespread contagion. ...

Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints ArXiv ID: 2507.20039 “View on arXiv” Authors: Zihan Lin, Haojie Liu, Randall R. Rojas Abstract This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR’s forecast error variance to quantify how much each stock’s shocks contribute to another’s uncertainty information we invert to form influence-based edge weights in our network. By applying the Minimum Spanning Tree (MST) algorithm, we extract the core inter-stock structure and identify central stocks through degree centrality. A dynamic portfolio is constructed using the top-ranked stocks, with capital allocated based on Value at Risk (VaR). To refine stock selection, we incorporate forecasts from ARIMA and Neural Network Autoregressive (NNAR) models. Trading simulations over a one-year period demonstrate that the MST-based strategies outperform a buy-and-hold benchmark, with the tuned NNAR-enhanced strategy achieving a 63.74% return versus 18.00% for the benchmark. Our results highlight the potential of combining network structures, predictive modeling, and risk metrics to improve adaptive financial decision-making. ...

Clustered Network Connectedness: A New Measurement Framework with Application to Global Equity Markets ArXiv ID: 2502.15458 “View on arXiv” Authors: Unknown Abstract Network connections, both across and within markets, are central in countless economic contexts. In recent decades, a large literature has developed and applied flexible methods for measuring network connectedness and its evolution, based on variance decompositions from vector autoregressions (VARs), as in Diebold and Yilmaz (2014). Those VARs are, however, typically identified using full orthogonalization (Sims, 1980), or no orthogonalization (Koop, Pesaran and Potter, 1996; Pesaran and Shin, 1998), which, although useful, are special and extreme cases of a more general framework that we develop in this paper. In particular, we allow network nodes to be connected in ``clusters", such as asset classes, industries, regions, etc., where shocks are orthogonal across clusters (Sims style orthogonalized identification) but correlated within clusters (Koop-Pesaran-Potter-Shin style generalized identification), so that the ordering of network nodes is relevant across clusters but irrelevant within clusters. After developing the clustered connectedness framework, we apply it in a detailed empirical exploration of sixteen country equity markets spanning three global regions. ...

Emerging countries’ counter-currency cycles in the face of crises and dominant currencies ArXiv ID: 2410.23002 “View on arXiv” Authors: Unknown Abstract This article examines how emerging economies use countercyclical monetary policies to manage economic crises and fluctuations in dominant currencies, such as the US dollar and the euro. Global economic cycles are marked by phases of expansion and recession, often exacerbated by major financial crises. These crises, such as those of 1997, 2008 and the disruption caused by the COVID-19 pandemic, have a particular impact on emerging economies due to their heightened vulnerability to foreign capital flows and exports.Counter-cyclical monetary policies, including interest rate adjustments, foreign exchange interventions and capital controls, are essential to stabilize these economies. These measures aim to mitigate the effects of economic shocks, maintain price stability and promote sustainable growth. This article presents a theoretical analysis of economic cycles and financial crises, highlighting the role of dominant currencies in global economic stability. Currencies such as the dollar and the euro strongly influence emerging economies, notably through exchange rate variations and international capital movements. Analysis of the monetary strategies of emerging economies, through case studies of Brazil, India and Nigeria, reveals how these countries use tools such as interest rates, foreign exchange interventions and capital controls to manage the impacts of crises and fluctuations in dominant currencies. The article also highlights the challenges and limitations faced by these countries, including structural and institutional constraints and the reactions of international financial markets.Finally, an econometric analysis using a Vector AutoRegression (VAR) model illustrates the impact of monetary policies on key economic variables, such as GDP, interest rates, inflation and exchange rates. The results show that emerging economies, although sensitive to external shocks, can adjust their policies to stabilize economic growth in the medium and long term. ...