![]() Using nonlinear time series data, we aim to develop a trading model that refutes the efficient market hypothesis (Fama, 1970) that it should be Data collection and preprocessing Deriving the correlation dimension using chaos analysis in R The overall experimental process is shown in Fig. Brief summary of the proposed modelīased on the phenomenon that when the CCS-IRS basis widens (more negative), the USDKRW exchange rate rises and when the CCS-IRS basis gets tightened (less negative), the USDKRW exchange rate falls, we propose to use the KRW CCS-IRS to build a foreign exchange trading model of the USDKRW exchange rate. A CDS is a derivative that transfers the credit risk of a reference entity (corporate or sovereign) from one party to another (Angelini, 2012). Section snippets Introduction and backgroundĪlong with traditional macroeconomic indicators such as GDP, current account surplus/deficit, and inflation, the credit default swap (CDS) premium played a key role as a leading indicator for predicting the USDKRW exchange rates until 2013, as the risk of sovereign default and exchange rate fluctuations were inextricably linked (Chernov et al., 2018). The foreign exchange trading model established in this paper not only enables foreign exchange traders to obtain sustainable economic profits, but also contributes to the efficiency of the financial market, thereby contributing to sustainable economic growth. The experimental results show that our NNs trading model of the USDKRW exchange rate outperforms XGBoost and LR model, generating an annual return of 4.888%∼8.464%, whereas the buy-and-hold and sell-and-hold strategies in the USDKRW exchange rate market produces an annual return of 2.2% and −1.15%, respectively. Based on the phenomenon in which the USDKRW exchange rate rises (falls) when the CCS-IRS basis widens (tightens), this study proposes to use the CCS-IRS basis to build a foreign exchange trading model of the USDKRW exchange rate. The CCS-IRS basis is the spread between the cross-currency swap (CCS) rate and interest rate swap (IRS) rate and is often used as an indicator for foreign exchange soundness along with the CDS premium in Korea. We derive the correlation dimension from the chaos analysis, use Neural Networks (NNs) to optimize foreign exchange trading, and then compare the performance of the NNs model with XGBoost and Logistic Regression model (LR). This study aims to develop a foreign exchange trading model with a new leading indicator based on cross-currency swap (CCS) and interest rate swap (IRS) rates.
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