jobrown-coder/Namibia-Exchange-Rate-Volatility

This project investigates the behaviour, persistence, asymmetry, structural changes, and forecastability of exchange-rate volatility for the **Namibia Dollar (NAD)** against three major international currencies. # Exchange Rate Volatility Modelling of the Namibia Dollar (2010-2026) ## Overview This repository presents a comprehensive financial time series and volatility analysis of the **Namibia Dollar (NAD)** against three major international currencies using daily data from **January 2010 to July 2026**. The study examines how exchange-rate volatility evolves over time, whether volatility shocks persist, whether positive and negative shocks affect volatility differently, and how accurately volatility can be forecast. The analysis integrates descriptive statistics, return analysis, stationarity testing, normality testing, volatility clustering diagnostics, GARCH-family modelling, structural break analysis, Value-at-Risk, out-of-sample forecasting, and model validation within a fully reproducible **Quarto** workflow. The project demonstrates the application of quantitative techniques commonly used in treasury management, financial risk analysis, banking, investment research, and macro-financial analysis. --- ## Research Objectives The study aims to: - Examine the long-term behaviour of the Namibia Dollar against the US Dollar, Euro, and British Pound. - Analyse daily exchange-rate returns and historical volatility. - Determine whether exchange-rate levels and returns are stationary. - Test for volatility clustering and ARCH effects. - Compare standard GARCH, EGARCH, and GJR-GARCH volatility models. - Determine whether exchange-rate shocks affect volatility asymmetrically. - Measure volatility persistence and the half-life of volatility shocks. - Evaluate the out-of-sample forecasting performance of the selected models. - Estimate downside exchange-rate risk using Value-at-Risk. - Identify structural breaks in conditional exchange-rate volatility. - Generate short-term volatility forecasts. --- ## Full Document - jobrown-coder.github.io --- ## Dataset **Source** - Cirrus **Frequency** - Daily **Period** - January 2 …