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Wavelet coherence analysis and exchange rate movements

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Abstract

In this study, we examine the volatility of exchange rates to exogenous monetary shocks by dividing the volatility into subsections of “interdependence” and “contagion” during FED’s easing for the period from 2010:11 to 2018:7. Conventional methods are neither suitable to differentiate those components nor to identify the co-movements concerning time and frequency analysis, which are critical for timing in asset management and policymaking. Therefore, wavelet analysis is introduced to differentiate such components.

The contribution of the study is threefold: First, this study compensates for the lack of research capturing the volatility structure of exchange rates during exogenous shocks. Second, we show both the long-term and short-term impact with associated frequency ranges. Third, we show that identifying the impact of a shock in different components and frequency ranges can provide valuable insights for the timing of market preventions and asset allocation.

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Notes

  1. The colour code bar next to Fig. 6, shows the range from blue colour (weak consistency) to red colour (strong consistency).

  2. The results of the robustness checks of the paper are included in the appendix section.

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Funding

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Author information

Authors and Affiliations

  1. Justice Vocational College, Erciyes University, Kayseri, Turkey

    Sevda Kuşkaya

  2. School of Applied Disciplines, Esenyurt University, İstanbul, Turkey

    Nurhan Toğuç

  3. FEAS, Economics, Erciyes University, Kayseri, Turkey

    Faik Bilgili

Authors
  1. Sevda Kuşkaya
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  2. Nurhan Toğuç
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  3. Faik Bilgili
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Correspondence to Nurhan Toğuç.

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Appendix

Appendix

As robustness checks, this work followed first cointegration analyses through VAR with lag = 2. Johansen cointegration model estimation indicates that there exists a long-run cointegration equilibrium between USDTRY and DXY. Following the cointegration equilibrium given below, one might indicate that DXY has a significant positive impact on USDTRY.

$$\begin{aligned} {\text{USDTRY}} = & 0.0{\text{14878 DXY}} \\ {\text{s}}.{\text{e }}\left( {0.00{399}} \right) \\ \end{aligned}$$

This work launched later Markov Regime Switching model (MRSM) estimations. The below MRSM outputs reveal that DXY has a significant positive effect on USDTRY during both Regime 0 and Regime 1 while one lagged DXY has a significant negative impact on USDTRY during both regimes. Regime 0 denotes high volatile periods as Regime 1 refers to less volatile periods. Regime 0 periods include 794 days (39.70%) of the total sample with an average duration of 61.08 days while Regime 1 periods include 1206 days (60.30%) of the total sample with an average duration of 92.77 days. The top figure reveals fitted USDTRY through MRSM. The green area refers to Regime 0 data points while the white area denotes the Regime 1 data points. The middle figure and the figure at the bottom exhibit the probability of smoothed Regime 0 (green area) and probability of smoothed Regime 1 (gray area), respectively. Both analyzes give results that support wavelet analysis observing significant co-movements between the variables.

 

Coefficient

Std.Error

t-value

t-prob

Constant(0)

−0.0176051

0.009001

−1.96

0.051

Constant(1)

0.0179128

0.002038

8.79

0.000

USDTRY_1(0)

0.993207

0.002089

476

0.000

USDTRY_1(1)

0.989534

0.001368

724

0.000

DXY(0)

0.0186713

0.002450

7.62

0.000

DXY(1)

0.0160715

0.001337

12.0

0.000

DXY_1(0)

−0.0185133

0.002466

−7.51

0.000

DXY_1(1)

−0.0160839

0.001337

−12.0

0.000

Trend(0)

1.85572e-005

9.155e-006

2.03

0.043

Trend(1)

7.42172e-006

6.345e-006

1.17

0.242

figure a

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Kuşkaya, S., Toğuç, N. & Bilgili, F. Wavelet coherence analysis and exchange rate movements. Qual Quant 56, 4675–4692 (2022). https://doi.org/10.1007/s11135-022-01327-7

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