Search results

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

Capital markets thrive on information, and the information revolution has transformed these markets all over the world. Investors can now keep track of the movements of capital markets in real-time and they react to the flow of information from around the world. One of the concerns of stock market investors is whether the markets operate efficiently, independently, and with sound fundamentals. However, real market movements tend to exhibit a link as is evident from recent market movements across the world.

The assessment of interdependence between stock markets is an important aspect of international portfolio management. The aim of this chapter is to examine the shock and volatility spillover between the Standard and Poor’s 500 (S&P500) index from the United States (US) Stock Exchange and the Istanbul Stock Exchange 100 (BIST100) index from the Stock Exchange Istanbul.

S&P500 index, which is the most important index representing US markets, and BIST100 index, which is the index representing the Turkish market, were used as variables in this study. In the analysis, the causality in variance test was applied to determine the volatility spillover between these two markets. Later, multivariate GARCH (MGARCH) models were used to measure the volatility spillover in the markets. VAR(1)-GARCH (1,1)-Diagonal BEKK model was applied to the daily data to determine the shock and volatility spillover in the markets.

As a result of the variance causality test, it was found that there is a bi-directional volatility spillover between S&P500 index and BIST100 index. When the return spillover between the markets is examined, a one-way spillover from the S&P500 index to the BIST100 index emerged. Diagonal BEKK model results show that each market is affected by its own news (unexpected shocks) and volatility. Furthermore, the volatility is persistent for both markets. These findings demonstrate that the US market and the Turkish market interact with each other.

Introduction – Increases in prices of commodity markets may be associated with increased volatility in financial markets. That is why analysing time-varying co-movements of commodity prices can be of great importance for investors who take into consideration optimal asset allocation.

Purpose – The aim of this study is to investigate the volatility spillover from oil to precious metals under high-volatility and low-volatility regimes.

Methodology – The data covered daily closing prices of assets such as oil, palladium, and platinum for the period January 2010–December 2018. GARCH models were analysed in order to determine the most appropriate volatility structure, and it was determined that GARCH (1,1) model was the most suitable model for all commodities. Markov Switching model was used to analyse the volatility spillover from oil to precious metals.

Findings – According to the analyses, the results showed that there were volatility spillovers from oil to palladium and platinum in low-volatility regimes and from oil to platinum in high-volatility regimes. On the other hand, there was no volatility spillover from oil to palladium in high-volatility regimes. Investing into oil and palladium in the same portfolio can provide diversification benefits for investors in high-volatility regimes. On the other hand, investing into oil and palladium in the same portfolio may not provide diversification benefits for investors in low-volatility regimes. The findings of the analyses can be beneficial for investors, market participants, and portfolio managers to make an accurate portfolio management.