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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
We built classification trees that provide sorting rules to reproduce the stock groups identified by hierarchical network clustering of Japanese stocks listed on the Tokyo Stock Exchange. The ...clustering method was based on a modularity maximization algorithm, which is frequently used for community detection in a network. We applied the clustering method to a correlation network in our previous research work. When building the classification trees, we tested various types of non-price data and selected effective variables with relative importance scores. The selection of variables proved to be consistent with a standard stock price model. Specifically, market capitalization and price book-value ratio were included as significantly important variables. Some other factors were also included as variables that clarify the properties of the Japanese stock market. The classification tree method can be applied to categorize stocks without a full set of continuous time series data into some groups, when our network clustering is difficult to be implemented. It can eventually contribute to improving risk measurement and management of stock portfolios.
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.
This paper studies the dynamic correlation matrix estimation of highly volatile financial returns, which is used to build a dynamic correlation network. The widely used method of calculating ...time-dependent linear correlation matrices by moving window of a fixed sample period can have fundamental problems when applied to fat-tailed returns. A multivariate volatility model, DCC-GARCH, is employed to filter the fat-tailed returns and estimate the dynamic correlation of returns in order to overcome such difficulties. The time-dependent correlation matrices are calculated and compared with the ones that are calculated by the traditional calculation method to highlight the advantages of the proposed dynamic correlation based method. As a case study, the model is fitted to the Japanese stock returns to analyze dynamic changes in the correlation matrix. The method is not limited to financial returns, but can also be applied to build a dynamic correlation network of other time series data with high volatility.
This paper analyses high dimensional correlation structure of Japanese stocks to find a more data-oriented and flexible grouping than the Japan standard sector classification for better portfolio ...risk management. Modularity maximization and spectral clustering are employed to recursively divide GARCH filtered stock returns into subgroups. The standard sector classification is proved to be valid for group identification, though partially. Our method based on community detection can be applicable for clustering other fat-tailed financial asset returns.
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.
PDF
