DYNAMIC CONNECTEDNESS OF FINANCIAL STRESS ACROSS ADVANCED AND EMERGING ECONOMIES: EVIDENCE FROM TIME AND FREQUENCY DOMAINS

This study analyzes the dynamic connectedness (i.e., spillovers and spillbacks) of financial stress across advanced and emerging economies. As proxy for financial stress, we reconstruct the financial stress index (FSI) for 16 advanced economies and 15 emerging economies from January 1997 to August 2020. The constructed FSIs reflect combined stress level in banking sectors, equity markets, capital markets and exchange rate markets. Using frameworks proposed by Diebold and Yilmaz (Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66) and Baruník and Křehlík (Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271–296), we find that there is strong connectedness of financial stress across economies. Moreover, the connectedness of the financial stress is stronger after the global financial crisis and during the COVID-19 pandemic. Although the spillover of shocks is strongest in the short-term horizon, the spillovers in the longer-term horizons are not trivial. Our results also show that the US is the largest shock transmitter as well as one of the largest shock receivers. Our results also suggest that shocks originating in advanced economies have strong effects on other economies, but shocks originating in emerging economies also play an increasing role. Global factors such as global economic policy uncertainty and geopolitical risks influence the magnitude of the spillover of financial stress.

Contagion, Exchange Rate, and Financial Volatility: Indonesian Case in Global Financial Turbulence

Global turbulence after the financial crisis has hit Indonesia and almost all emerging countries. Quantitative Easing (QE) normalization (tapering of) has caused the capital outflows from emerging countries. Trade war and increasing geopolitical tension together raise the pressure. Argentina and Turkey have been experiencing economic shock. Indonesia should identify the contagion possibility and refer to Thai baht contagion experience in 1997. This paper assesses the contagion, exchange rate, and financial volatility triggered by global turbulence and Argentina-Turkey crisis in 2018. We use vector autoregression (VAR), simple correlation, dynamic conditional correlation (DCC), and regression method. We will investigate the potential contagion both in stock and exchange rate markets and in the rupiah exchange rate determination from both contagion and fundamental factors regarding the balance of payment (BOP) condition. The empirical result shows the potential contagion from Argentina and Turkey’s financial crisis to the Indonesian economy, especially to the stock market and exchange rate. The regression and correlation result also shows that Turkey has a higher financial contagion effect than Argentina to Indonesian financial market. Balance of payment condition also has the significant effect to explain rupiah exchange rate depreciation.

Revealing asymmetric spillover effects in hazelnut, gasoline, and exchange rate markets in Turkey: The VECM-BEKK-MGARCH approach

The study used the VECM-BEKK-MGARCH method to model the volatility transmission between the markets of gasoline, exchange rates, and the hazelnut market for the period of 21.07.2005-20.3.2018. The suitability of the VECM-BEKK-MGARCH method was confirmed by statistical testing. The changes in hazelnut prices were not affected by the changes in the prices or final values in the other two sectors (Granger causality). Moreover, the Granger causality tests revealed that, while the change in the gasoline market was not affected by the other two markets, the change in the exchange rates market was affected by the other two markets. Furthermore, especially the volatilities (long-term uncertainties) of the markets were affected by both their own short- and long-term volatilities and other sectors? short- and long-term volatilities. It was shown that the long-term swings in these three markets were affected by the cross-interaction in the markets. Additionally, as opposed to the case in the positive news, it was observed that pieces of negative news about the markets affected the markets.