In this work, we develop a methodology to detect structural breaks in multivariate time series data using the t-distributed stochastic neighbour embedding (t-SNE) technique and non-parametric spectral density estimates. By applying the proposed algorithm to the exchange rates of Indian rupee against four primary currencies, we establish that the coronavirus pandemic (COVID-19) has indeed caused a structural break in the volatility dynamics. Next, to study the effect of the pandemic on the Indian currency market, we provide a compact and efficient way of combining three models, each with a specific objective, to explain and forecast the exchange rate volatility. We find that a forward-looking regime change makes a drop in persistence, while an exogenous shock like COVID-19 makes the market highly persistent. Our analysis shows that although all exchange rates are found to be exposed to common structural breaks, the degrees of impact vary across the four series. Finally, we develop an ensemble approach to combine predictions from multiple models in the context of volatility forecasting. Using model confidence set procedure, we show that the proposed approach improves the accuracy from benchmark models. Relevant economic explanations to our findings are provided as well.