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Volatility forecasting with garch models and recurrent neural networks
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| 3.66 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
The three main ways to estimate future volatilities include the implied volatility of option
prices, time-series volatility models, and neural network models. This project investigates
whether there are economically meaningful differences between those approaches. Seminal
time-series models like the GARCH, as well as recurrent neural network models like the LSTM
are investigated to forecast volatilities. An eventual informational advantage over the market’s
expectation of future volatility in the form of implied volatility is sought after. Through trading
strategies involving options, as well as investment vehicles that emulate the VIX, it is attempted
to trade volatility in a profitable way.
Descrição
Palavras-chave
Volatility forecasting Implied volatility Realized volatility Volatility risk premium Machine learning Neural networks Garch Lstm Gru Vix