Forecasting value at risk and conditional value at risk using option market data

Abstract

We forecast monthly value at risk (VaR) and conditional value at risk (CVaR) using option market data and four different econometric techniques. Independent from the econometric approach used, all models produce quick to estimate forward-looking risk measures that do not depend from the amount of historical data used and that, through the implied moments of options, better reflect the ever-changing market scenario. All proposed option-based approaches outperform or are equally good to different "traditional" forecasts that use historical returns as input. The extensive robustness of our results shows that the real driver of the better forecasts is the use of option market data as inputs for the analysis, more than the type of econometric approach implemented.

Implementation of Jump Diffusion to Predict Stock Prices and Risk Analysis Using Value At Risk and Expected Shortfall (Case Study: PT. Indofood Sukses Makmur Tbk)

“…Penelitian-penelitian untuk menghitung nilai risiko telah dilakukan oleh beberapa peneliti diantaranya Molino dan Carlo pada tahun 2020 [12]. Pada penelitiannya Molino dan Carlo memprediksi VaR dan CVaR menggunakan data pasar opsi dan menggunakan empat teknik ekonometrik yang berbeda.…”