SETAR (Self-exciting Threshold Autoregressive) Non-linear Currency Modelling in EUR/USD, EUR/TRY and USD/TRY Parities

Abstract

In economies that are open to foreign markets the numerical value of the currencies as a macroeconomic variable is of great importance especially when the mutual dependency among the economies is concerned. When it is considered in terms of political economy, the targeted level of the currencies have vital importance especially in economies that have the characteristics of export-driven growth and in economies that struggle not to disrupt the picture in macroeconomic design. When it is considered that each time series has a structure that is sensitive to its own internal dynamics (sometimes these dynamics are expressed as the time series components), these dynamics provide us with coordinates for estimations and may eliminate the compulsory dependency on the outsourced variables at a serious level. This is exactly what has been done in this study. First of all, the non-linear time series analyses are examined in terms of linearity tests, and the linearity tests are applied for all parties and for different time periods. Then, the SETAR Modelling, which is the title of the study, has been applied in order to explain the non-linear pattern in detail. The SETAR Modelling process and other definitions statistical analyses of this model have been applied in relevant parities for separate time periods. The SETAR model, which is one of the TAR Group modeling, shows a better performance than many other linear and non-linear modeling. In this study, the secondary purpose is to express that the SETAR model performance is superior to the other models by considering the observation values of the parities.

Estimation of Right-censored SETAR-type Nonlinear Time-series Model

“…Similarly, σ 2 ε 1 and σ 2 ε 2 are the variances of these regimes. It should also be noted that the selection of m, d is realized in three steps that are discussed by [19]. Thus, after the selection of m, d using Eq.…”

Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas

“…In this study, air pollution data were analysed using four time series models: ARIMA, ARIMAX, the self-exciting threshold autoregressive (SETAR) and neural network nonlinear autoregressive (NNNAR). For more details on these models, see [36][37][38]. Time series model development and application consist of three main steps [39]:…”

“…The nonlinear models are not discussed further. For more details on SETAR and NNNAR, readers are referred to Fırat [36] and Waheeb et al [37]. Here, we first discuss the univariate ARIMA model and then compare its performance with ARIMAX.…”

Time-series forecasting through recurrent topology

“…However, whether increasing model complexity actually increases forecasting performance has been challenged 15 . Moreover, increasing complexity brings with it a variety of drawbacks including various model assumptions, hypothesized parametric equations, and/or vulnerability to overfitting 6,[15][16][17][18][19][20][21][22] . There are also often numerous hyperparameters/parameters that require optimization and tuning in high-dimensional parameter space which can have an impact on both a model's carbon footprint and the cost of machine learning projects 6,[15][16][17][18][19][20][21][22][23] .…”

“…Moreover, increasing complexity brings with it a variety of drawbacks including various model assumptions, hypothesized parametric equations, and/or vulnerability to overfitting 6,[15][16][17][18][19][20][21][22] . There are also often numerous hyperparameters/parameters that require optimization and tuning in high-dimensional parameter space which can have an impact on both a model's carbon footprint and the cost of machine learning projects 6,[15][16][17][18][19][20][21][22][23] . Complex models have also created another problem; a need to create methods of interpreting/ explaining these complex models rather than creating methods that are interpretable/explainable in the first place 24 .…”