Search results

Long-term interest rates of small open economies (SOE) correlate strongly with the USA long-term rate. Can central banks in those countries decouple from the United States? An estimated Dynamic Stochastic General Equilibrium (DSGE) model for the UK (vis-á-vis the USA) establishes three structural empirical results: (1) Comovement arises due to nominal fluctuations, not through real rates or term premia; (2) the cause of comovement is the central bank of the SOE accommodating foreign inflation trends, rather than systematically curbing them; and (3) SOE may find themselves much more affected by changes in USA inflation trends than the United States itself. All three results are shown to be intuitive and backed by off-model evidence.

The purpose of this paper is to propose an empirical procedure for examining the time‐varying features of cross‐market correlations in selected Gulf stock markets.

The paper directly infers the cross‐market linkages from the stock data using a multivariate dynamic conditional correlation GARCH model (DCC‐GARCH). The paper attempts to date the structural breaks in the time‐paths of the conditional correlation indices to investigate whether the cross‐market comovement encompasses significant changes in nature or not.

Conditional cross‐market correlations between studied markets are shown to be time‐varying, past‐dependent and subject to structural breaks. However, the comovements are still small within the Gulf region and insignificant between the Gulf stock markets and the world market.

Even though the paper attempted to relate the observed changes in market linkages to major economic and political events that the Gulf region experienced during the sample period, a more careful, in‐depth analysis is needed since the primary objectives of this paper consist only of measuring stock market comovements and detecting their possible structure changes.

For global investors, there is still room for international and regional diversification in Gulf markets, given the low degree of comovements documented in the study.

The application of the DCC‐GARCH model and structural change test in a linear framework appears to be suitable for studying the time‐varying properties of cross‐market linkages between markets in the Gulf region. It also provides information about the degree of financial integration of the studied markets with the world stock market through an analysis of the conditional correlation coefficients.

Using intraday prices for the S&P 500 and Nikkei Stock Average stock indexes and aggregate trading volume for the New York and Tokyo Stock Exchanges, we show how short-run comovements between national stock market returns vary over time in a way related to the trading volume and liquidity in those markets. We frame our analysis in the context of the heterogeneous-agent models of trading developed by Campbell, Grossman and Wang (1993) and Blume, Easley and O’Hara (1994) and Wang (1994) which predict that trading volume acts as a signal of the information content of a given price move. While we find that there exists significant short-run dependence in returns and volatility between Japan and the U.S., we offer new evidence that these return “spillovers” are sensitive to interactions with trading volume in those markets. The cross-market effects with volume are revealed in both close-to-open and open-to-close returns and often exhibit non-linear patterns that are not predicted by theory.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.

The purpose of this paper is to examine the short‐term stock market interactions between US and six major Asian markets – China, India, Hong Kong, Singapore, South Korea and Taiwan. These six economies along with Japan and Australia have the largest stock exchanges in the Asia‐Pacific region. The importance of the US market to the Asian economies is the prime motivation for a quantitative assessment of its role in this region. The objective of this study is to measure the dynamic stock market interdependence of US and Asian newly industrialized economies (NIEs) (Hong Kong, Singapore, South Korea and Taiwan) and emerging market economies (EMEs) (China and India) post Asian crisis of 1997 and also to capture the market interactions during the sub‐prime crisis.

The study has employed Granger causality tests and generalized forecast error variance decomposition (FEVD) analysis to analyze the fluctuations in and the extent of short‐term interdependence between the US and Asian economies. VAR model was estimated to run the simulations for FEVD analysis.

The empirical results from FEVD analysis revealed the dominance of US stock market on Asian markets; the USA being a large economy of the world, an important trading partner and major supplier of capital to Asian region. Stock markets of Asia are not immune to the shocks originating in the USA although the effects of shocks vary considerably across markets. Further, an important implication is that major crisis events can influence the relationship among stock markets.

This is one of the first papers in the Asian context examining the interdependence with the US markets. Hence, even though most of the Asian economies went through liberalization, the macroeconomic and financial circumstances were very different before, after and during the process. This motivated the examination of the interactions between US and other Asian markets.

This paper aims to examine the effects of adding non-tradable sector and trade in intermediate goods sector and their impact on the “Backus-Smith” (BS) puzzle and the features of the non-tradable output. Conventional international real business cycle models show that the real exchange rate and the terms of trade are positively correlated to the relative consumption movement between the home and foreign economies when there is a total factor productivity shock, whereas the correlation in the data is negative. The author develops a two-country, dynamic, stochastic and general equilibrium (DSGE) model with staggered price setting in the non-tradable sector and international trade in intermediate goods sector because of product differentiation in a high-asset market frictions situation.

In this paper, DGSE simulation and calibration are performed using Matlab with Dynare.

When the world economy has positive country-specific productivity shock, the benchmark model with non-tradable sector and intermediate goods sector successfully solves the BS puzzle and is able to match several features of the data. The dynamic responses to productivity shock show that integrating product differentiation is necessary to generate a more volatile and counter-cyclical non-tradable output.

The paper investigates the effects of incorporating non-tradable sector and trade in interemediate goods sector to standard two-country DSGE model through simulation and calibration.

The purpose of this paper is to study the co-movement and market integration dynamics of the emerging/frontier stock markets in South Asia (India, Pakistan and Sri Lanka) with a portfolio management perspective.

Scholars in the past have documented the limitation of standard econometric techniques such as co-integration analysis to capture this phenomenon. The other econometric technique widely used in integration and comovement literature is dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity. This method captivates the time-varying correlations, although frequency information is absent. The wavelet-based analysis decomposes the time-series data in a time-frequency domain, which is largely useful to fund managers and policy makers. This study examines the regional integration in selected South Asian markets using wavelet analysis.

The results suggest some degree of market integration, however weak as compared to regional integrations in developed markets. Pakistan and India were found to be the potential leaders at varying time scales in the region. Weaker co-movement phenomena may offer ample arbitrage opportunities to investors in this region. In addition, the authors also find that the structure of correlation changes after some of the major macroeconomic events.

This study is among the first to examine co-movement and integration of stock returns in a time-frequency domain for South Asia. In addition, the authors also highlight weak integration in these markets, which may be beneficial for portfolio diversification.

This study examines the tracking error (TE) of a sample of sector exchange traded funds (ETFs) using spectral techniques.

TE is examined by computing its power spectrum using the wavelet transform. The wavelet transform maps the TE time series from the time domain to the time–frequency domain. Albeit the wavelet transform is a more complicated mathematical tool compared with the Fourier transform, it also has important advantages such as that it allows to analyze non-stationary data and to detect transient behavior.

Results show that changes in the TE of a sample of sector ETFs are captured by the wavelet transform. Moreover, the authors also find that the wavelet coherence function can be used as a measure of TE in the time–frequency domain.

The study shows that the wavelet coherence function can be used as a reliable measure of TE.

Standard measures of business cycle comovement, based on correlation coefficients, are very sensitive to the phase of the business cycle, as well as to regional crises. Adjusting for these factors overturns the empirical result that Asia-Pacific economies are becoming decoupled from the United States over time. An alternative, intuitive, measure of business cycle comovement is proposed, based on the difference between output growth rates adjusted for its long-run average. The new measure suggests that Asia-Pacific economies are becoming more strongly coupled with the United States over time.

The purpose of this paper is to examine the degree of dependence and extreme correlation (i.e. tail dependence) among US industry sectors.

This paper makes use of both conventional measures of dependence (the Pearson’s correlation coefficient, Spearman’s rho and Kendall’s tau) and copula measures of extreme correlations (including the same-direction and cross-tail dependence coefficients) to explore sector diversification opportunities. The paper splits the full sample in three periods, namely, 1995 to 2000, 2001 to 2006 and 2007 to 2012, to access the extent to which the dependence results change through time.

This research provides three important findings. First, the degree of dependence and same-direction extreme correlations are high, whereas the cross-extreme correlations are considerably low. Second, the sector pairs offering the best and worst tail diversification change across sample periods. Third, the traditional dependence measures suggest that benefits for sector diversification have decreased over all sample periods, while the potential for sector diversification during extreme events has just started to disappear in the most recent period.

An investor should consider both the normal co-movements and extreme co-movements among sector indices to maximize diversification benefits.

Given the limited empirical investigations of the degree of dependence and extreme correlation at a sector level, the results from this research should provide additional and valuable information for both investors and empirical researchers about portfolio diversification and risk management.