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In this study, we propose a novel approach to analyze a dynamic correlation network of highly volatile financial asset returns by using a network clustering algorithm to deal with high dimensionality issues. We analyze the dynamic correlation network of selected Japanese stock returns as an empirical study of the correlation dynamics at the market level by applying the proposed method. Two types of network clustering algorithms are employed for the dimensionality reduction. Firstly, several stock groups instead of the existing business sector classification are generated by the hierarchical recursive network clustering of filtered stock returns in order to overcome the high dimensionality problem due to the large number of stocks. The stock returns are then filtered in advance to control for volatility fluctuations that can distort the correlation between stocks. Thus, the correlation network of individual stock returns is transformed into a correlation network of group-based portfolio returns. Secondly, the reduced size of the correlation network is extended to a dynamic one by using a model-based correlation estimation method. A time series of adjacency matrices is created on a daily basis as a dynamic correlation network from the estimation results. Then, the correlation network is summarized into only three representative correlation networks by clustering along the time axis. Some intertemporal comparisons of the dynamic correlation network are conducted by examining the differences between the three sub-period networks. Our dynamic correlation network analysis framework is not limited to stock returns, but can be applied to many other financial and non-financial volatile time series data.

Economies are frequently affected by natural disasters and both domestic and overseas financial crises. These events disrupt production and cause multiple other types of economic losses, including negative impacts on the banking system. Understanding the transmission mechanism that causes various negative second-order post-catastrophe effects is crucial if policymakers are to develop more efficient recovery strategies. In this work, we introduce a credit-based adaptive regional input-output (ARIO) model to analyse the effects of disasters and crises on the supply chain and bank-firm credit networks. Using real Japanese networks and the exogenous shocks of the 2008 Lehman Brothers bankruptcy and the Great East Japan Earthquake (March 11, 2011), this paper aims to depict how these negative shocks propagate through the supply chain and affect the banking system. The credit-based ARIO model is calibrated using Latin hypercube sampling and the design of experiments procedure to reproduce the short-term (one-year) dynamics of the Japanese industrial production index after the 2008 Lehman Brothers bankruptcy and the 2011 Great East Japan earthquake. Then, through simulation experiments, we identify the chemical and petroleum manufacturing and transport sectors as the most vulnerable Japanese industrial sectors. Finally, the case of the 2011 Great East Japan Earthquake is simulated for Japanese prefectures to understand differences among regions in terms of globally engendered indirect economic losses. Tokyo and Osaka prefectures are the most vulnerable locations because they hold greater concentrations of the above-mentioned vulnerable industrial sectors.

In this paper, a novel approach to building a dynamic correlation network of highly volatile financial asset returns is presented. Our method avoids the spurious correlation problem when estimating the dynamic correlation matrix of financial asset returns by using a filtering approach. A multivariate volatility model, DCC–GARCH, is employed to filter the fat-tailed returns. The method is proven to be more reliable for detecting dynamic changes in the correlation matrix compared with the widely used method of calculating time-dependent correlation matrices over a fixed size moving window, which can have fundamental problems when applied to fat-tailed returns. We apply the method to selected Japanese stock returns to observe the dynamic network changes as a case study. The estimated time-dependent correlation matrices are then compared with those calculated by using the traditional method to highlight the advantages of the proposed method. Two types of indicators, namely the largest eigenvalue and cosine distance measures, are introduced to identify significant changes in the correlation matrix for an initial screening of remarkable stress events. A more detailed network-based analysis is then conducted by examining topological measures calculated from the network adjacency matrices. The higher density and lower heterogeneity of the correlation network during stress periods are clearly observed, while the correlation network of stock returns is shown to be robust with regard to time. The method discussed in this paper is not limited to stock returns; it can also be applied to build a dynamic correlation network of other financial and non-financial time series with high volatility.

In this paper, the dynamic correlation of Japanese stock returns is estimated by using the dynamic conditional correlation (DCC–GARCH) model to study their correlation dynamics empirically. It is difficult to fit the model to the whole stock market jointly at the same time; therefore, a network-based clustering is applied for the dimensionality reduction of the sample data. Two types correlation structures are estimated: homogeneous groups of stocks in a balanced size are created by clustering to observe within-group correlation, while a single portfolio that comprises group portfolio returns is also created to observe between-group correlation. The estimation result reveals dynamic changes in correlation intensity represented by the largest eigenvalue of the estimated correlation matrix. A higher level of correlation intensity and volatility are observed during the crisis periods, namely after both the Lehman collapse and the Great East Japan Earthquake, for the between- and within-group correlations. It is also confirmed that the pattern of correlation change is significantly different between the groups. The proposed method is useful for monitoring dynamic correlation of asset returns efficiently in a large scale of portfolio.

We propose a simple model to simulate an interaction between banks and a financial market. In our model, banks are exposed to two sources of risks: market risk from their investments in assets external to the banking system and credit risk from lending in the interbank market. By and large, both risks increase during severe financial turmoil. In this scenario, the paper shows the conditions under which individual and the systemic defaults tend to coincide. This paper attempts to conduct a numerical simulation of banking ecosystems by using the actual values of financial items extracted from 89 Japanese banks’ balance sheets. From this numerical simulation, we confirm two points: (1) when financial market prices decrease due to crashes in a trend-followers-dominant market, banks lose their net worth coincidentally. Thus, the capital adequacy ratio decreases synchronously, and any bank may not provide other banks with money through the interbank markets. (2) In a contrarians-dominant or contrarians-predominant market, we observed mean-reverting fluctuations in market prices. Bankruptcies happen asynchronously, and market prices eventually decrease. However, other banks may provide the bank suffering from a shortage of assets with money through the interbank markets. We further compare the characteristics of the banking system in four types of market modes.

This paper analyzes Value at Risk (VaR) and Expected Shortfall (ES) calculation methods in terms of bias and dispersion against benchmarks computed from a fat-tailed parametric distribution. The daily log returns of the Nikkei-225 stock index are modeled by a truncated stable distribution. The VaR and ES values of the fitted distribution are regarded as benchmarks. The fitted distribution is also used as a sampling distribution; sample returns with different sizes are generated for the simulations of the VaR and ES calculations. Two parametric methods: normal distribution and generalized Pareto distribution and two non-parametric methods: historical simulation and kernel smoothing are selected as the targets of this analysis. A comparison of the simulated VaR, ES, and the ES/VaR ratio with the benchmarks at multiple confidence levels reveals that the normal distribution approximation has a significant downward bias, especially in the ES calculation. The estimates by the other three methods are much closer to the benchmarks on average, although some of them become unstable with smaller sample sizes and/or at higher confidence levels. Specifically, ES tends to be more biased and unstable than VaR at higher confidence levels.

Systemic risk in banking systems remains a crucial issue that it has not been completely understood. In our toy model, banks are exposed to two sources of risks, namely, market risk from their investments in assets external to the banking system and credit risk from their lending in the interbank market. By and large, both risks increase during severe financial turmoil. Under this scenario, the paper shows the conditions under which both the individual and the systemic default tend to coincide.

The currency crisis in the 1997 diversified views on Asian economies. In one view, the ever-expanding image of Asian economies faded away, resulting in a view that Asia could never regain the power to achieve sustainable strong economic growth. In another view, Asia is still believed to continue its high growth rate with strong exports after relatively short adjustment phase. When discussing the future of East Asian economies1, it is very important to capture the whole image of economic relations with other economic regions as well as its interaction within the region. However, some key statistics, which are available in advanced countries, are not always available in Asian economies. Analyses on trade and direct investment between Asia and advanced countries occasionally depend on the statistics compiled by advanced countries. Also, due to the lack of statistical data, analysis of intra-regional economic activities is not so easy. In this paper, we tried to show the current situation and dynamic change of Asian economies quantitatively through analyses of statistical data from various kinds of sources. In this paper, East Asia comprises nine countries and regions: NIEs (South Korea, Taiwan, Singapore and Hong Kong), four ASEAN countries (Thailand, Malaysia, Indonesia and the Philippines (herein after called \"ASEAN 4\")) and China.

This paper analyses the trade relationships within East Asia and between East Asia and the US and Japan, with particular emphasis on the structural changes that occurred during the 1990s. The main purpose of the analysis is to gain a better understanding of the potential global impact of these changes. The analysis of revealed comparative advantage patterns underlines the strong trading position of East Asia in the ICT sector, with the simultaneous gain in \"comparative advantage\" on the export and on the import side during the 1990s suggesting an increasing role of East Asian countries as a processing and production center. In order to study the consequences of the increasing internationalization of the production process in East Asia a VAR of inter- and intra-regional trade flows is estimated. The main finding is that there are quantitatively significant indirect international transmission channels of country-specific shocks along the international production chain, with substantial differences in the exposure to such shocks between Japan and the US.

To prevent infections associated with medical implants, various antimicrobial silver-coated implant materials have been developed. However, these materials do not always provide consistent antibacterial effects in vivo despite having dramatic antibacterial effects in vitro, probably because the antibacterial effects involve silver-ion-mediated reactive oxygen species generation. Additionally, the silver application process often requires extremely high temperatures, which damage non-metal implant materials. We recently developed a bacteria-resistant coating consisting of hydroxyapatite film on which ionic silver is immobilized via inositol hexaphosphate chelation, using a series of immersion and drying steps performed at low heat. Here we applied this coating to a polymer, polyetheretherketone (PEEK), and analyzed the properties and antibacterial activity of the coated polymer in vitro and in vivo. The ionic silver coating demonstrated significant bactericidal activity and prevented bacterial biofilm formation in vitro. Bio-imaging of a soft tissue infection mouse model in which a silver-coated PEEK plate was implanted revealed a dramatic absence of bacterial signals 10 days after inoculation. These animals also showed a strong reduction in histological features of infection, compared to the control animals. This innovative coating can be applied to complex structures for clinical use, and could prevent infections associated with a variety of plastic implants.