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NOWCASTING GROWTH AT RISK IN INDONESIA: APPLICATION OF MIDAS-QUANTILE REGRESSION MODEL Latifah, Turfah; Akbar, Muhammad Sjahid; Prastyo, Dedy Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0673-0690

Abstract

One of the main problems faced by policymakers in economic monitoring is the limited availability of predictive tools that can comprehensively and in real time measure economic growth risks, particularly amid financial market volatility and rapid changes in economic indicators. This study aims to nowcast Indonesian economic growth using the Growth at Risk (GaR) approach by applying the Mixed Data Sampling-Quantile Regression (MIDAS-QR) model. This approach predicts economic risks across different quantiles, capturing best- and worst-case scenarios by integrating multi-frequency indicators, namely the Financial Conditions Index (FCI), External Financial Environment Index (EFEI), and Macroeconomic Prosperity Leading Index (MPLI), summarized using Principal Component Analysis (PCA). Prediction accuracy is evaluated using Quantile Mean Absolute Error (QMAE), Quantile Root Mean Squared Error (QRMSE), and Clark-West (CW) test metrics. The analysis utilizes a dataset of Indonesia covering the period from January 2001 to March 2025, combining quarterly GDP growth data as the dependent variable and monthly predictor variables sourced from the Central Statistics Agency (BPS), Bank Indonesia, and the Indonesia Stock Exchange. The findings show that the MIDAS-QR model significantly improves the accuracy of GaR forecasting in Indonesia relative to conventional approaches. It effectively captures risk asymmetries across quantiles, minimizes predictive errors, and facilitates the timely detection of economic downturns, offering valuable insights for early action. This study highlights the strategic role of high-frequency data in enhancing forecast precision and real-time economic risk monitoring in Indonesia. The application of the MIDAS-QR model presents a valuable tool for policymakers in formulating proactive responses to global economic uncertainty and fostering resilient economic growth.
Identification of Banking Stock Risk Factors through Stochastic Search Variable Selection in a CoVaR Models Based on Quantile Regression and Quantile Autoregressive Almas, Luqyana; Prastyo, Dedy Dwi; Rahayu, Santi Puteri
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The stability of the banking sector is crucial for maintaining economic balance, particularly in Indonesia where banks play a central role in the financial system. Conventional risk measures such as Value-at-Risk (VaR) mainly capture individual bank risk and are limited in assessing systemic risk arising from interbank spillovers. This study proposes an integrated systemic risk framework that combines Quantile Autoregressive (QAR) based VaR estimation with Conditional Value-at-Risk (CoVaR) derived from quantile regression, while incorporating Stochastic Search Variable Selection (SSVS) to identify key risk factors. The QAR approach accommodates asymmetry and heavy-tailed characteristics of bank return distributions, whereas CoVaR measures the conditional impact of bank distress on the overall financial system.  The SSVS is implemented within a Bayesian framework to select significant market and macroeconomic variables based on posterior inclusion probabilities. Model performance is evaluated using the Kupiec Proportion of Failures (POF) test. The results show that QAR-based VaR effectively captures tail risk at the 5% and 1% quantiles. CoVaR estimates reveal heterogeneity in systemic risk exposure, with medium-sized and digital banks exhibiting greater sensitivity to systemic stress than large banks. Overall, the CoVaR–SSVS model demonstrates superior validation performance and estimation stability compared to the conventional CoVaR approach.