Managing exchange rate risk by cross-hedging using commodity futures and by diversification

• Autor(a) principal: Lee, Stefan Colza
• Data de Publicação: 2020
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: https://hdl.handle.net/10438/29356
• topic: Cross-hedging; Exchange rate risk; Commodity futures; Risco cambial; Futuros de commodities; Administração de empresas; Câmbio; Administração de risco; Mercado futuro; Hedging (Finanças)

This thesis examines alternative strategies, or strategies that do not rely on currency future contracts, to manage exchange rate risk. In chapters 1 and 2, I explore cross-hedging using commodity futures to manage the exchange rate risk of commodity currencies. Recent studies have revealed that commodity currencies provide currency risk premiums, attracting carry traders. However, currency risk premiums may disincentivize financial and nonfinancial institutions from investing in countries with commodity currencies due to the negative expected returns of hedging exchange rate risk with currency future contracts. Alternative strategies to manage exchange rate risk that have better expected returns may increase investments in these countries justifying research in this area. I start by investigating four different hedging strategies to manage exchange rate risk for nine commodity currencies: full, partial, no and cross-hedging. The cross-hedging strategy consists of using commodity future contracts to hedge exchange rate risk. The commodity future contracts used for cross-hedging are determined through the analysis of the export basket of each individual commodity country. My main finding is that for many risk aversions, cross-hedging is the optimal hedging strategy for exchange rate risk. I then utilize quantitative portfolio optimization methods to build the commodity futures basket used for cross-hedging. I use three different covariance matrixes and mean variance optimization. The covariance matrixes are based on historical variance and covariance, on the exponentially weighted moving average (EWMA) method and on the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model. Based on the empirical results and Brazilian data, my main finding is that the best strategy to build the commodity basket for cross-hedging is to use export data, compared to quantitative portfolio optimization methods. In chapter 3, I explore reducing exchange rate risk through diversification and simple portfolio construction. Globally diversified investors may seek investing in currencies with high relative to low idiosyncratic risk, as idiosyncratic risk can be minimized by diversification. I use the two-factor model proposed by Lustig et al. (2011) to assess the idiosyncratic risk of an exchange rate. Among 54 exchange rates, I identify 15 that may be particularly interesting for diversified investors.