Incorporating daily market uncertainty data into a conventional short-run dynamic model: the case of the black-market exchange rate in Iran

• Published in: Applied economics Vol. 51; no. 45; pp. 4982 - 4991
• Main Authors: Valadkhani, Abbas, Nguyen, Jeremy, Hajargasht, Reza
• Format: Journal Article
• Language: English
• Published: London Routledge 26.09.2019; Taylor & Francis Ltd
• Subjects: Bias; black market exchange rate; Black markets; Economic analysis; Economic models; Economic theory; Foreign exchange rates; market uncertainty; mixed-data sampling models; Money; PPP; Prices; Purchasing power parity; Sampling; Sanctions; Stabilization; Uncertainty; Iran
• ISSN: 0003-6846; 1466-4283
• DOI: 10.1080/00036846.2019.1607245

The effect of market uncertainty on a country's currency, while widely recognized, is either omitted from mainstream models or addressed using low-frequency series, which are typically subject to aggregation bias and substantial lags. In this article, we propose a new mixed-data sampling (MIDAS) modelling framework that enables us to incorporate the asymmetric daily effects of market uncertainty in a conventional monthly error correction model. We achieve this by proxying market uncertainty via the value of a 'safe haven' asset (gold) that investors reallocate towards in the face of heightened market risk. We apply the model to the Iranian black-market exchange rate, using a mix of the daily price of gold (28 June 2010-19 August 2018) and monthly data (July 2010-July 2018) on relative prices. Our results indicate that purchasing power parity (PPP) holds despite the recent unprecedented depreciations in the Iranian currency arising from several rounds of international sanctions. We also find that increased uncertainty can lead to instantaneous and substantial depreciations, whereas stabilization back towards the PPP path is much more sluggish.