Marco Piña

Marco Piña

Economics PhD Student

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Market risk modeling with option-implied covariances and score-driven dynamics

Published in The North American Journal of Economics and Finance, 2024 (with Rodrigo Herrera)

In this paper we make use of option-implied volatilities to build a time-varying implied correlation matrix. Then, we use this matrix to estimate jointly both the covariance matrix of the returns and the implied covariance matrix dynamics. Finally, we do a backtest and show that the proposed model can effectively use the risk-neutral information to model the variance of the returns and to forecast the Value-at-Risk. Our results show that, in general, the proposed model outperforms the benchmark while considerably reducing the number of parameters to be estimated.

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Exchange rate volatility and the effectiveness of FX interventions: The case of Chile

Published in Latin American Journal of Central Banking, 2023 (with Alejandro Jara)

In this paper, we study the effectiveness of FX interventions in Chile since adopting a fully flexible exchange rate regime in the late 1990s. In particular, we ask whether these interventions have dumped excess exchange rate volatility and reduced its probability of being in a high volatility state. To do so, we rely on a high-frequency GARCH(1,1) volatility model with Markov-Switching regimes and evaluate the effectiveness of FX interventions within a local projection setting. We show that FX interventions in Chile tend to occur during high exchange rate volatility periods, which correlate with domestic and foreign financial factors. Moreover, we show that the FX intervention that started by the end of 2019–the latest intervention included in our study–effectively reduced the exchange rate volatility and the probability of being at a high volatility state.

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Estimates of the US shadow-rate

Published in Latin American Journal of Central Banking, 2023 (with Rodrigo Alfaro)

This article provides several estimates for the shadow rate (SR) of the short-term interest rate in US. We assume maximal models with two and three Gaussian factors, and we use forward rates to estimate the model’s parameters. Based on that, we conclude that point estimates of SR should be taken with caution because they depend on the characteristics of the data set, including the sample size, maturities, and smoothness. The latter is even more crucial than other settings discussed previously in the literature, such as the number of factors.

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