TITLE:
Time Series Forecasting Models for S&P 500 Financial Turbulence
AUTHORS:
Hugo Gobato Souto
KEYWORDS:
Financial Time Series, Bayesian Forecasting, Financial Turbulence, S&P 500, Time Series Forecasting
JOURNAL NAME:
Journal of Mathematical Finance,
Vol.13 No.1,
February
24,
2023
ABSTRACT: Although it has already been proven many times that the use of the risk parameter Financial Turbulence yields significant positive results in risk and portfolio management, there is currently no research regarding its predictability through the use of time series forecasting methods. Accurately forecasting the Financial Turbulence of a certain financial asset index or portfolio could be a great advantage for portfolio management for financial institutions given the positive results found by various research of the use of the Financial Turbulence in portfolio management. Therefore, this paper explores the predictability of the S&P 500 Financial Turbulence with the use of common time series forecasting methods, namely Autoregressive model (AR(p)), Moving Average model (MA(q)), Autoregressive Integrated Moving Average model (ARIMA(p, d, q)), and Normal Dynamic Linear Model (NDLM(k)). This paper makes use of in-sample data (from November 2017 until November 2021) and out-sample data (from November 2021 until November 2022) to evaluate the forecasting performance of these forecasting methods in both quantitative and qualitative manners. The results of this study indicate that regarding the S&P 500 Financial Turbulence, AR(7) is the best forecasting method for one-step ahead forecast, whereas NDLM(7) is the best forecasting method for one business year forecast.