GARCH vs. stochastic volatility: Option pricing and risk management |
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| Institution: | 1. Department of Business Studies, University of Vienna, Brünnerstrasse 72, 1210 Vienna, Austria;2. Central Bank of Austria, Otto-Wagner Platz 3, 1011 Vienna, Austria;3. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, 1010 Vienna, Austria;1. Edinburgh Business School, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom;2. Cairo University Business School, Egypt;3. School of Business, Jiangnan University, China;1. Research Institute, Shenzhen Stock Exchange, China;2. School of Economics, Singapore Management University, Singapore;1. School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China;2. Department of Accountancy and Finance, Otago Business School, University of Otago, Dunedin 9054, New Zealand;3. Department of Applied Finance, Macquarie University, North Ryde, NSW 2109, Australia;1. Indian Institute of Management, Lucknow, Prabandh Nagar, Off Sitapur Road, Lucknow 226013, India;2. Indian Institute of Management, Calcutta, Diamond Harbour Road, Joka, Kolkata 700104, India |
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| Abstract: | In this paper we compare the out-of-sample performance of two common extensions of the Black–Scholes option pricing model, namely GARCH and stochastic volatility (SV). We calibrate the three models to intraday FTSE 100 option prices and apply two sets of performance criteria, namely out-of-sample valuation errors and Value-at-Risk (VaR) oriented measures. When we analyze the fit to observed prices, GARCH clearly dominates both SV and the benchmark Black–Scholes model. However, the predictions of the market risk from hypothetical derivative positions show sizable errors. The fit to the realized profits and losses is poor and there are no notable differences between the models. Overall, we therefore observe that the more complex option pricing models can improve on the Black–Scholes methodology only for the purpose of pricing, but not for the VaR forecasts. |
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