Financial market integration: Theory and empirical results

In this article, we introduce a new theoretical international asset pricing model which accounts for partial financial market segmentation. We show that if some investors do not hold all international assets because of implicit and/or explicit segmentation factors, the world market portfolio is not efficient and the classic ICAPM must be augmented by a new factor reflecting the local risk undiversifiable internationally. We test this model empirically for a sample of emerging markets. Our findings show that the degree of market integration is time-varying and that the premium associated with the domestic risk factors is the most important component of the total risk premium. However, our results also show that most of the emerging markets we study have become more integrated in the end of our sample period as a result of liberalization and reforms.     

China's liberalizing stock market,crude oil,and safe-haven assets: A linkage study based on a novel multivariate wavelet-vine copula approach

China is the world's largest oil importer, and therefore the correlations between stock indices and highly volatile oil prices deserve close examination when investing in China's gradually liberalizing stock market. Another concern for international investors is whether safe-haven assets can reduce portfolio risks for investment in China. The paper makes two main contributions. First, we develop a novel method of examining a multivariate dependence structure by combining wavelet analysis with the vine copula model. Second, we apply the proposed methodology to study the correlations between China's liberalizing stock market, petroleum, and safe-haven assets at different frequencies. We find that the multidimensional dependence of these assets has been altered as a result of the 2008 global financial crisis. Moreover, the vine structures exhibit dependence patterns that vary over time horizons, indicating that the multidimensional dependence is sensitive to time scales.     

Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors

Cryptocurrencies are gradually establishing themselves as a new class of assets with unique features, although there remains skepticism and a lack of understanding of their nature. In this study, we compare the financial properties of these new digital assets and investigate their dynamic relationship with major financial securities and commodities. Furthermore, we evaluate the economic and financial potential benefits of cryptocurrencies for financial investors. Using different time-varying copula approaches and bivariate dynamic conditional correlation GARCH models, we find that the cross-correlation with conventional assets is changing over time but weak, supporting the idea that these cryptocurrencies can be suitable for financial diversification. However, our analysis of portfolios shows that cryptocurrencies are poor hedging instruments in most of the considered cases. Moreover, we find that the relationship between cryptocurrencies and conventional assets is sensitive to external economic and financial shocks.     

Structural change and international stock market interdependence: Evidence from Asian emerging markets

This study investigates the evolving pattern of the interdependence among selected Asian emerging markets and three major stock markets (Japan, UK and US). Using rolling cointegration methods and the recently developed algorithms of inductive causation, we found that time-varying cointegration relationships exist among these stock markets. The results indicate that the wave of financial liberalization policies in the early 1990s led to a significant increase in market linkages which was later weakened during the 1997 Asian financial crisis. Furthermore, the data indicate that Japan and the US have the greatest influence on the emerging markets while the influence of Singapore and Thailand has increased since the Asian financial crisis.     

Systemic risk contagion in FX market: A frequency connectedness and network analysis

In this study, we analyse systemic risk contagion between a set of most actively traded currencies (EURO, JPY, GBP, AUD, CAD and CHF) by application of VAR based frequency connectedness proposed by Baruník and K?ehlík. By using this novel approach, we gauge foreign exchange (FX) market connectedness in 200‐day frequency band using spectral representation of variance decompositions of VAR and identify directional spillovers between the most actively traded foreign exchange rates. Dynamics of the overall spillover index reveals that the index capture well‐known financial stress incidents properly. Finally, network topology of directional spillovers between currency pairs is provided for visulalization interconnectedness between them.     

The dependence structure between the Canadian stock market and the USD/CAD exchange rate: a copula approach

Abstract This paper investigates the dependence structure between the real Canadian stock returns and the real USD/CAD exchange rate returns, using the Symmetrized Joe‐Clayton (SJC) copula function. We estimate the SJC copula with monthly data over the period 1995:1 to 2006:12. Our results show significant asymmetric static and dynamic tail dependence between the real stock returns and the real exchange rate returns, such that the two returns are more dependent in the left than in the right tail of their joint distribution. We explain this asymmetric dependence in terms of an asymmetric interest rate policy by Canadian monetary authorities in response to changes in the real exchange rate during sub‐periods of falling and rising commodity prices.     

Measuring and Optimizing Portfolio Credit Risk: A Copula‐based Approach*

In this work, we present a methodology for measuring and optimizing the credit risk of a loan portfolio taking into account the non‐normality of the credit loss distribution. In particular, we aim at modelling accurately joint default events for credit assets. In order to achieve this goal, we build the loss distribution of the loan portfolio by Monte Carlo simulation. The times until default of each obligor in portfolio are simulated following a copula‐based approach. In particular, we study four different types of dependence structure for the credit assets in portfolio: the Gaussian copula, the Student's t‐copula, the grouped t‐copula and the Clayton n‐copula (or Cook–Johnson copula). Our aim is to assess the impact of each type of copula on the value of different portfolio risk measures, such as expected loss, maximum loss, credit value at risk and expected shortfall. In addition, we want to verify whether and how the optimal portfolio composition may change utilizing various types of copula for describing the default dependence structure. In order to optimize portfolio credit risk, we minimize the conditional value at risk, a risk measure both relevant and tractable, by solving a simple linear programming problem subject to the traditional constraints of balance, portfolio expected return and trading. The outcomes, in terms of optimal portfolio compositions, obtained assuming different default dependence structures are compared with each other. The solution of the risk minimization problem may suggest us how to restructure the inefficient loan portfolios in order to obtain their best risk/return profile. In the absence of a developed secondary market for loans, we may follow the investment strategies indicated by the solution vector by utilizing credit default swaps.     

Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence,information transmission and market co-movement

This paper examines the price and volatility dynamics between China and major stock markets in the Asia-Pacific, investigating the effects of the Chinese stock market crash (2015–2016) for the first time. Employing the Bayesian VAR and BEKK GARCH, we observe that price and volatility spillover behaviours are different during the stable and stress periods. Particularly, price spillovers from China to other regional markets are more significant during a bullish period, showing that ‘good news’ emanating from China has strong impacts on its neighbours during better market condition. In the turbulent period, we observe strong shock spillover effects and enhanced volatility spillovers from China to most Asia-Pacific stock markets. This is because China, as an important trading partner and strategic financial centre shows to exert significant influence on the Asia-Pacific region through various economic channels. We also find that the Asia-Pacific stock markets spill over their shocks to China during the crisis, indicating that China is becoming more integrated with the regional financial markets.     

Global predictive power of the upside and downside variances of the U.S. equity market

Given the pace of increasing globalization and the pioneering role of the U.S. economy, we anlayze the global impact of the U.S. equity market’s uncertainty. The asymmetric impact of upside (downside) uncertainty, related with the upward (downward) movements of the underlying assets, has raised substantial concerns recently. We comprehensively analyze the global predictability of the upside and downside variances of the U.S. equity market, implied by S&P-500 calls and puts, respectively. We contribute to the literature on the asymmetric impacts of the upside and downside variances of the U.S. equity market in an international setting. Our study also complements the study on predicting international stock returns. Moreover, substantial economic value can be generated from the perspective of asset allocation. The main channel for the positive (negative) predictability of upside (downside) variance stems from its positive (negative) impacts on international investment, highlighting the leading role of the U.S. economy.     

The impact of news,oil prices,and global market developments on Russian financial markets1

This paper analyses the impact of news, oil prices, and international financial market developments on daily returns on Russian bond and stock markets. First, regarding returns, energy news affects returns, while news from the war in Chechnya is not significant. Market volatility does not appear to be sensitive to either type of news. Second, a significant effect of the growth in oil prices on Russian stock returns is detected. Third, the international influence on Russian financial markets depends upon the degree of financial liberalization. The higher the degree of financial liberalization, the stronger is the impact of US stock returns on Russian financial markets. In addition, banking reform and interest rate liberalization efforts seem to dictate the globalization of Russian stock markets, while it is the progress in liberalizing securities markets and non‐bank financial institutions that matters more for the globalization of Russian bond markets.