Importance of CDS Prediction

Oct 13, 2025 by wbrum.org 146 views

Prediction of Indonesian Credit Default Swap (CDS) Value with Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average Average With Exogenation (ARIMAX) methods

Introduction

The Indonesian Credit Default Swap (CDS) market has been growing rapidly in recent years, with many investors and financial institutions seeking to understand the value of CDS in predicting credit risk. In this study, we aim to compare the performance of two popular time series forecasting methods, Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average Average With Exogenation (ARIMAX), in predicting the value of Indonesian CDS. Our goal is to determine which method is more accurate in predicting CDS values and to identify the factors that contribute to the accuracy of these predictions.

Methodology

This study used secondary data in the form of Daily CDS Indonesia data from September 20, 2018 to November 30, 2020. For the exogenous variable, the ARIMAX method uses the dummy variable for the period before and during Pandemi Covid-19. The sampling technique used is purposive sampling. After the data is collected, the researcher analyzes it using the ARIMA and ARIMAX prediction method with the help of EViews 12 software. Then, the results of RMSE and Mape are compared as an evaluation model to see the best model accuracy in predictions.

Results

The results showed that the ARIMA method was superior to predictions compared to the ARIMAX method. This is because the RMSE and Mape values in the ARIMA method are smaller than the ARIMAX method.

Why is ARIMA's better method?

ARIMA Method (Autoregressive Integrated Moving Average) is superior in predicting CDS Indonesia due to several factors:

The ability to capture data patterns: ARIMA is designed to capture autocorrelation patterns in time series data, which means that the current data value is influenced by the value of data in the past. Indonesian CDS tends to have a strong autocorrelation pattern, so ARIMA is more effective in capturing market trends and fluctuations.

The simplicity of the model: ARIMA MODEL is relatively simple and easy to understand. This facilitates the interpretation of prediction results and helps in identifying the main factors that affect the value of CDS Indonesia.

Limitations of Exogenous Data:* Exogenous data such as the Dummy Covid-19 variable may not always be effective in increasing prediction accuracy. ARIMA, who focuses on internal data, is able to produce more accurate predictions in this case.

Importance of CDS Prediction

The prediction of the value of CDS Indonesia is very important because:

Risk Assessment: CDS is a financial instrument used to measure a country's credit risk or issuer. CDS predictions help investors to assess their investment risk and make more appropriate investment decisions.

Risk Management: Banks and financial institutions use CDS predictions to manage their credit risk. Accurate predictions allow them to take the right steps to protect themselves from potential losses.

Monitoring Economic Stability:* The movement of Indonesian CDS values can demonstrate the economic and financial conditions of the state. Accurate predictions help in monitoring state economic and financial stability.

CONCLUSIONS AND SUGGESTIONS

This study shows that the ARIMA method is superior in predicting the value of CDS Indonesia compared to the ARIMAX method. However, this research only uses limited data and needs to be expanded using more complete data and other exogenous variables. Further research can also be done to study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia.

The development of more accurate prediction models is very important to help investors, banks, and financial institutions in making more informed decisions and manage risks more effectively.

Future Research Directions

Future research can be conducted to:

Expand the dataset to include more recent data and other exogenous variables.
Compare the performance of ARIMA and ARIMAX methods with other time series forecasting methods, such as LSTM and Prophet.
Study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia.
Develop more accurate prediction models that can be used by investors, banks, and financial institutions.

Conclusion

In conclusion, this study shows that the ARIMA method is superior in predicting the value of CDS Indonesia compared to the ARIMAX method. The ARIMA method is able to capture autocorrelation patterns in time series data and is relatively simple and easy to understand. The limitations of exogenous data also contribute to the accuracy of ARIMA predictions. The development of more accurate prediction models is very important to help investors, banks, and financial institutions in making more informed decisions and manage risks more effectively.

References

[1] Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
[2] Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
[3] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
[4] Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer.

Keywords

ARIMA
ARIMAX
CDS
Time series forecasting
Pandemi Covid-19
Risk assessment
Risk management
Economic stability
Q&A: Prediction of Indonesian Credit Default Swap (CDS) Value with Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average Average With Exogenation (ARIMAX) methods

Q: What is the purpose of this study?

A: The purpose of this study is to compare the performance of two popular time series forecasting methods, Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average Average With Exogenation (ARIMAX), in predicting the value of Indonesian Credit Default Swap (CDS).

Q: What are the main differences between ARIMA and ARIMAX methods?

A: The main difference between ARIMA and ARIMAX methods is that ARIMA focuses on internal data, while ARIMAX includes exogenous variables to improve the accuracy of predictions.

Q: Why is ARIMA considered a better method in this study?

A: ARIMA is considered a better method in this study because it is able to capture autocorrelation patterns in time series data, which means that the current data value is influenced by the value of data in the past. Indonesian CDS tends to have a strong autocorrelation pattern, so ARIMA is more effective in capturing market trends and fluctuations.

Q: What are the limitations of exogenous data in ARIMAX method?

A: Exogenous data such as the Dummy Covid-19 variable may not always be effective in increasing prediction accuracy. ARIMA, who focuses on internal data, is able to produce more accurate predictions in this case.

Q: Why is the prediction of CDS value important?

A: The prediction of CDS value is important because it helps investors to assess their investment risk and make more appropriate investment decisions. It also helps banks and financial institutions to manage their credit risk and take the right steps to protect themselves from potential losses.

Q: What are the future research directions for this study?

A: Future research can be conducted to expand the dataset to include more recent data and other exogenous variables, compare the performance of ARIMA and ARIMAX methods with other time series forecasting methods, study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia, and develop more accurate prediction models.

Q: What are the implications of this study for investors, banks, and financial institutions?

A: The implications of this study for investors, banks, and financial institutions are that they can use ARIMA method to make more informed decisions and manage risks more effectively. They can also use the results of this study to develop more accurate prediction models that can be used to predict the value of CDS Indonesia.

Q: What are the limitations of this study?

A: The limitations of this study are that it only uses limited data and needs to be expanded using more complete data and other exogenous variables. Further research can also be done to study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia.

Q: What are the future applications of this study?

A: The future applications of this study are that it can be used to predict the value of CDS Indonesia in real-time, which can help investors, banks, and financial institutions to make more informed decisions and manage risks more effectively. It can also be used to develop more accurate prediction models that can be used to predict the value of CDS Indonesia.

Q: What are the contributions of this study to the field of finance?

A: The contributions of this study to the field of finance are that it provides a new method for predicting the value of CDS Indonesia, which can be used to make more informed decisions and manage risks more effectively. It also provides a new understanding of the factors that affect the value of CDS Indonesia, which can be used to develop more accurate prediction models.

Q: What are the future research directions for this study in the field of finance?

A: The future research directions for this study in the field of finance are that it can be used to develop more accurate prediction models that can be used to predict the value of CDS Indonesia, study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia, and compare the performance of ARIMA and ARIMAX methods with other time series forecasting methods.

Q: What are the implications of this study for policymakers?

A: The implications of this study for policymakers are that they can use the results of this study to develop policies that can help to manage the risks associated with CDS Indonesia, such as policies that can help to reduce the volatility of CDS Indonesia.

Q: What are the limitations of this study for policymakers?

A: The limitations of this study for policymakers are that it only uses limited data and needs to be expanded using more complete data and other exogenous variables. Further research can also be done to study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia.

Q: What are the future applications of this study for policymakers?

A: The future applications of this study for policymakers are that it can be used to develop policies that can help to manage the risks associated with CDS Indonesia, such as policies that can help to reduce the volatility of CDS Indonesia. It can also be used to develop more accurate prediction models that can be used to predict the value of CDS Indonesia.

Q: What are the contributions of this study to the field of economics?

A: The contributions of this study to the field of economics are that it provides a new method for predicting the value of CDS Indonesia, which can be used to make more informed decisions and manage risks more effectively. It also provides a new understanding of the factors that affect the value of CDS Indonesia, which can be used to develop more accurate prediction models.

Q: What are the future research directions for this study in the field of economics?

A: The future research directions for this study in the field of economics are that it can be used to develop more accurate prediction models that can be used to predict the value of CDS Indonesia, study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia, and compare the performance of ARIMA and ARIMAX methods with other time series forecasting methods.

Q: What are the implications of this study for the Indonesian economy?

A: The implications of this study for the Indonesian economy are that it can be used to develop policies that can help to manage the risks associated with CDS Indonesia, such as policies that can help to reduce the volatility of CDS Indonesia. It can also be used to develop more accurate prediction models that can be used to predict the value of CDS Indonesia.

Q: What are the limitations of this study for the Indonesian economy?

A: The limitations of this study for the Indonesian economy are that it only uses limited data and needs to be expanded using more complete data and other exogenous variables. Further research can also be done to study the long-term effects of Pandemi Covid-19 on the value of CDS Indonesia.

Q: What are the future applications of this study for the Indonesian economy?

A: The future applications of this study for the Indonesian economy are that it can be used to develop policies that can help to manage the risks associated with CDS Indonesia, such as policies that can help to reduce the volatility of CDS Indonesia. It can also be used to develop more accurate prediction models that can be used to predict the value of CDS Indonesia.