Search results

Purpose: Through globalization, financial markets have become more integrated and their tendency to act together has increased. The majority of the literature states that there is a cointegration between developed and emerging markets. How do positive or negative shocks in developed markets affect emerging markets? And how do positive or negative shocks in emerging markets affect developed markets? For this reason, the aim of the study is to investigate the asymmetric causality relationship between developed and emerging markets with Hatemi-J asymmetric causality test.

Design/methodology/approach: In this study, the Dow Jones Industrial Average (DJIA) index was used to represent developed markets and the Morgan Stanley Capital International (MSCI) Emerging Market Index was used to represent emerging markets. The asymmetric causality relationship between the DJIA Index and the MSCI Emerging Market Index was investigated using monthly data between January 2009 and April 2019. In the first step of the study, the Johansen Cointegration Test was used to determine whether there is a cointegration between the markets. In the next step, the Hatemi-J asymmetric causality test was applied to see the asymmetric causality relationship between the markets.

Findings: There is a weak correlation between developed and emerging markets. This result is important for international investors who want to diversify their portfolios. As a result of the Johansen Cointegration Test, it was found that there is a long-term relationship between the MSCI Emerging Market Index and the DJIA Index. Therefore, investors who make long-term investment plans should not forget that these markets act together and take into account the causal relationship between them. According to the asymmetric causality test results, a unidirectional causality relationship from the MSCI Emerging Market Index to the DJIA Index was determined. This causality shows that negative shocks in the MSCI Emerging Market Index have positive effects on the DJIA Index.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the asymmetrical relationship between developed and emerging markets. This study is also useful in predicting the short- and long-term relationship between markets. In addition, this study helps investors, portfolio managers, company managers, policymakers, etc., to understand the integration of financial markets.

Introduction – Emerging markets are under the influence of many external factors in global market conditions. International interest rates and price fluctuations in major stock market indices are also among these factors. The FED policies shape the international capital movements in particular, which significantly affects the emerging markets. For this reason, emerging stock markets may show different reactions especially in times of crisis.

Purpose – The purpose of this study is to investigate whether the BIST30 index acted in accordance with the overreaction hypothesis (ORH) against the return changes in the Dow Jones Industrial Average (DJIA) index in the process of the 2008 global financial crisis.

Methodology – The data set of the study was analysed by dividing it into two periods. The first period is the monetary expansion period between 17 August 2007, when the Federal Reserve (FED) reduced the interest rate for the first time, until 22 May 2013 when the FED announced that it would reduce the bond purchases. The second period is the monetary contraction period including the dates between 23 May 2013 and 1 June 2017. An error correction model (ECM) was established in both periods for the indices, determined as cointegrated. The validity of the ORH was tested by Cumulative Abnormal Return (CAR) Analysis.

Findings – According to the ECM, the authors identified that the effect of short-term changes in the DJIA return in the monetary expansion period on BIST30 index return was higher than that in the monetary contraction period. However, according to the findings obtained from the CAR analysis results, the BIST30 index did not generally act in accordance with the ORH against the DJIA. Findings can be appreciated as a decision-making tool especially for investment specialists and investors interested in securities investments.

This chapter examines the impact of price fluctuations in foreign stock markets on the stock prices of domestic banks in Korea, Malaysia, Singapore, and Thailand. Some studies have argued that the 2007–2009 global financial crisis (GFC) affected domestic banks less in East Asia, even though the supporting evidence is rather limited. Employing a multinomial logit model, we estimate how changes in the United States and Japanese stock markets affected the banking sectors in the sampled countries before the 1997 Asian financial crisis, and before and during the more recent GFC. We interpret the number of banks in a given country that experienced a large price shock on the same day (or “coexceedance”) as shocks to the domestic banking sector. The results suggest that fluctuations in foreign stock market indices exerted a larger impact on the prices of East Asian banking stocks during the 2000s than during the 1990s. In addition, although the shocks brought about by the deterioration of foreign stock markets were significant before the GFC, both increases and decreases in foreign stock prices significantly affected the banking sectors of the respective countries during the crisis. Lastly, we conclude that increasing foreign capital flows and foreign assets and liabilities greatly influenced domestic banking systems in East Asia during the 2000s.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

This paper examines stock price and volume effects associated with a change in the composition of the Nikkei 225 index in Japan in April 2000. Our results include the following: first, we show that newly added firms experience significant positive excess returns of 19% in the five-day period after the announcement of the change; in contrast, deleted and remaining firms’ returns are negatively affected, −36 and −14%, respectively; second, volume tests show significant increase in trading activity after the announcement for both added and deleted firms; third, cross-sectional analysis provides evidence that higher arbitrage risk and demand shocks increase the absolute value of excess returns.

In this chapter, we outline the statistical consequences of neglecting structural breaks and regime switches in autoregressive and GARCH models and propose two strategies to approach the problem. The first strategy is to identify regimes of constant unconditional volatility using a change point detector and estimate a separate GARCH model on the resulting segments. The second approach is to use a multiple-regime GARCH model, such as the Flexible Coefficient GARCH (FCGARCH) specification, where the regime-switches are governed by an observable variable. We apply both alternatives to an array of financial time series and compare their forecast performance.

Historical stock prices have long been used to evaluate a stock’s future returns as well as the risks associated with those returns. Similarly, historical dividends have been used to evaluate the intrinsic value of a stock using, among other methods, a dividend discount model. In this chapter, we propose an alternate use of the dividend discount model to enable an investor to assess the risks associated with a particular stock based on its dividend history. In traditional applications of the dividend discount model for stock valuation, the value of a stock is the net present value of its future cash dividends. We propose an alternative approach in which we calculate the internal rate of return for a stream of future cash dividends assuming the current stock price. We use a bootstrapping approach to generate a stream of future cash dividends, and use a Monte Carlo simulation approach to run multiple trials of the model. The probability distribution of the internal rates of return obtained from the simulation model provides an investor with an expected percentage return and the standard deviation of the return for the stock. This allows an investor to not only compare the expected internal rates of return for a group of stocks but to also evaluate the associated risks. We illustrate this internal rate of return approach using stocks that make up the Dow Jones Industrial Average.