Analyzing Herd Behavior in Global Stock Markets: An Intercontinental Comparison

Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new herd behavior index and demonstrate its desirable properties through simple theoretical models. As for empirical analysis, we use global stock market data from Morgan Stanley Capital International to study herd behavior especially during periods of financial crises in detail

Dynamic Relationships among Composite Property Prices of Major Chinese Cities: Contemporaneous Causality through Vector Error Corrections and Directed Acyclic Graphs

The present study is the first one that investigates dynamic relations among composite real estate price indices of ten different cities in China during the years from 2005 to 2021. Utilizing the data recorded on a monthly basis, we apply VECM (vector error-correction modeling) and DAGs (directed acyclic graphs) in order to characterize contemporaneous causal relations among the ten real estate price indices. We use the PC algorithm to identify a pattern with non-directed edges and the LiNGAM algorithm to determine the causal ordering, based on which we calculate the results of innovation accounting. The LiNGAM algorithm adopted here effectively utilizes non-normality for facilitating the arrival of complete causal orderings. Our results show that price dynamics revealed through processes of price adjustments due to shocks to prices are rather sophisticated and such dynamics are, in general, dominated by price indices of Shanghai and Shenzhen, which are two top-tier cities among the four top-tier cities in China. This indicates that policy design on composite property prices should be focusing on price indices of Shanghai and Shenzhen

Financial stress and crude oil implied volatility: New evidence from continuous wavelet transformation framework

This study explores the theoretical possibility of co-movement and causality between crude oil implied volatility (OVX) and financial stress in a wavelet framework. The paper contributes to the existing literature in at least three possible ways: (a) First, the study considers not only composite financial stress indicators but also uses the categorical stress components such as Credit, Equity Valuation, Funding, Safe Assets and Volatility. (b) Second, the study employs a wavelet-based approach in tracking the co-movement and causality between oil and financial stress in a continuous time-frequency space. Lastly, (c) while previous studies mainly use oil price changes to assess the relationship with financial stress, the present study evaluates the role of forward-looking (30-days ahead) oil price uncertainty (proxied by OVX). The findings indicate the existence of co-movement between oil volatility and financial stress, mainly around the phases of economic turbulence. The patterns and strength of such co-movements are time-variant. The direction of the relationship is mostly positive, and the lead-lag relationship reveals that OVX tends to drive the relationship. It is further observed that the causalities between the variables are mostly bi-directional. However, relatively stronger causalities are transmitted from OVX towards FSI. Furthermore, the association between OVX and stress indicators is assessed in two different states of the economy, i.e., state of distress and tranquillity. The findings suggest that the causal co-movement intensifies majorly during the state of distress. Overall, the outcome of this study could be useful to policymakers and investors to anticipate the impending changes in the relationship to mitigate its potential adverse impact.© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

How financial products organize spatial networks: Analyzing collateralized debt obligations and collateralized loan obligations as “networked products”

During the 2010s, collateralized loan obligations rapidly became a trillion-dollar industry, mirroring the growth profile and peak value of its cousin—collateralized debt obligations—in the 2000s. Yet, despite similarities in product form and growth trajectory, surprisingly little is known about how these markets evolved spatially and relationally. This paper fills that knowledge gap by asking two questions: how did each network adapt to achieve scale at speed across different jurisdictions; and to what extent does the spatial and relational organization of today's collateralized loan obligation structuration network, mirror that of collateralized debt obligations pre-crisis? To answer those questions, we draw on the global financial networks approach, developing our own concept of the networked product to explore the agentic qualities of collateralized debt obligations and collateralized loan obligations—specifically how their technical and regulatory “needs” shape the roles and jurisdictions enrolled in a global financial network. We use social network analysis to map and analyze the evolving spatial and relational organization that nurtured this growth, drawing on data harvested from offering circulars. We find that collateralized debt obligations spread from the US to Europe through a process of transduplication—that similar role-based network relations were reproduced from one regulatory regime to another. We also find a strong correlation between pre-crisis collateralized debt obligation- and post-crisis collateralized loan obligation-global financial networks in both US$- and €-denominations, with often the same network participants involved in each. We conclude by reflecting on the prosaic way financial markets for ostensibly complex products reproduce and the capacity for network stabilities to produce market instabilities

Behaviour of futures markets and implication for portfolio choice

First, we document the co-existence of the time series momentum and of the term structure factors in the global commodity futures market. We demonstrate that the strategies based on the joint time series momentum and term structure trading signal outperform time series momentum only strategies and term structure only strategies. Second, we propose a Multivariate Volatility Regulated Kelly strategy, which imposes extra variance penalization compared to the Kelly criterion. We furthermore demonstrate the superiority of our method in relatively low correlated portfolios, relative to the fractional Kelly and full Kelly strategies. The simulation results and Chinese commodity future empirical results strongly support our method. Third, we combine the shrinkage theory and CUSUM change point detection in order to improve the covariance estimators. The change point embedded covariance estimator can pe1jorm better than any shrinking covariance estimators in the portfolio management. We empirically test different shrinkage estimators based portfolios in global futures markets

The determinants of pollution levels: Firm-level evidence from Chinese manufacturing

Using a large, unique, firm-level dataset from the Chinese manufacturing sector, we study important factors that are related to emission intensity for three pollutants in China – sulfur dioxide, wastewater, and soot. Our main findings are as follows: (1) compared to state-owned enterprises (SOEs), both foreign-owned firms and domestic public-listed firms exhibit less intensive pollutant emissions; (2) firms in regions with less local protection have lower pollution intensity; (3) better property rights protection is negatively correlated with pollutant discharge over and beyond the national standards; and (4) larger firms, firms in industries that export more, and firms with more educated employees pollute less. These results suggest that China should not target foreign firms more harshly in its effort to reduce industrial pollution. Better institutions in the form of more effective law enforcement and lower entry barriers across regional markets are also means of curbing China’s pressing environmental problems during its current stage of economic development.postprin

Development of Vietnamese Stock Market : Influence of Domestic Macroeconomic Environment and Regional Markets

This study has analysed the role of the domestic economic environment and regional markets (Thailand, Japan, Hong Kong and China) in explaining the dynamics of Vietnamese stock market. In so doing, we employed a TVSVAR framework which accounts for time variations (in coefficients as well as in the variance-covariance matrix of innovations) on the data from July 2000 to December 2016. Our key findings suggested that the easing of monetary and credit conditions, stable and stronger currency and economic growth have played a significant and positive role in the development of the stock market in Vietnam. Inflation shocks did have a negative impact which implied that in policy setting the price stability is very important for the financial stability in Vietnam. The Vietnamese stock market is also heavily influenced by the regional markets, as there is strong evidence of co-movement. However, it was also witnessed that despite having a similar direction of impact and co-movement, different markets have an influence of different degrees and intensity on the Vietnamese stock market. Lastly, we also witnessed that as compared to the Global Financial Crisis, the recent periods showed comparatively lesser responsiveness. This could be associated with the intensive reaction during the period of financial turmoil as well as with an increase in the stability of the Vietnamese stock market as it matures