English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this research paper, I have applied various econometric time series andtwo machine learning models to forecast the daily data on the yield spread.First, I decomposed the yield curve into its principal components, thensimulated various paths of the yield spread using the Vasicek model. Afterconstructing univariate ARIMA models, and multivariate models such as ARIMAX,VAR, and Long Short Term Memory, I calibrated the root mean squared error tomeasure how far the results deviate from the current values. Through impulseresponse functions, I measured the impact of various shocks on the differenceyield spread. The results indicate that the parsimonious univariate ARIMA modeloutperforms the richly parameterized VAR method, and the complex LSTM withmultivariate data performs equally well as the simple ARIMA model.

Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield Spread