Evidence of Large Fluctuations of Stock Return and Financial Crises from Turkey: Using Wavelet Coherency and Varma Modeling to Forecast Stock Return

Abstract

Shocks, jumps, booms and busts are typical large fluctuation markers which appear in crisis. Models and leading indicators vary according to crisis type in spite of the fact that there are a lot of different models and leading indicators in literature to determine structure of crisis. In this paper, we investigate structure of dynamic correlation of stock return, interest rate, exchange rate and trade balance differences in crisis periods in Turkey over the period between October 1990 and March 2015 by applying wavelet coherency methodologies to determine nature of crises. The time period includes the Turkeys currency and banking crises; US sub-prime mortgage crisis and the European sovereign debt crisis occurred in 1994, 2001, 2008 and 2009, respectively. Empirical results showed that stock return, interest rate, exchange rate and trade balance differences are significantly linked during the financial crises in Turkey. The cross wavelet power, the wavelet coherency, the multiple wavelet coherency and the quadruple wavelet coherency methodologies have been used to examine structure of dynamic correlation. Moreover, in consequence of quadruple and multiple wavelet coherence, strongly correlated large scales indicate linear behavior and, hence VARMA (vector autoregressive moving average) gives better fitting and forecasting performance. In addition, increasing the dimensions of the model for strongly correlated scales leads to more accurate results compared to scalar counterparts.

Quadruple wavelet coherence and pairwise cointegration: comparative time-frequency analysis of bank deposit rate

“…The squared quadruple wavelet coherency between series y and other series x 1 , x 2 and x 3 will be denoted by Ry(x1x2x3)2 (or Ry(q)2 or R1(234)2) and is given by the formula (Oygur and Unal, 2017):where M denotes the 4 x 4 matrix of the all the smoothed cross-wavelet spectra S ij , which is the smoothed version of W ij : where S is a certain smoothing operator.Note that the matrix M is an Hermitian matrix, i.e. M = M H , with the symbol H denoting conjugate transpose; in fact, we have Sij=Sji*, for all i ≠ j and S ii = S (| W i | 2 ) is a real (positive) number for all i .…”

Volatility spillovers among leading cryptocurrencies and US energy and technology companies

“…The impact of spillover among markets can change due to significant policy changes and events (Wang and Guo, 2018). For example, the US subprime mortgage crisis that precipitated the 2008 financial crisis affected stock markets globally, to varying degrees (Oygur and Unal 2017). The COVID-19 pandemic also significantly impacted financial markets, resulting in simultaneous price declines and other effects for financial assets and commodities (Fang et al 2022).…”