论文标题
通过机器学习量化的Vstoxx期货价格动态的库存影响
Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning
论文作者
论文摘要
VSTOXX指数跟踪Euro Stoxx 50股票指数的预期30天波动率。因此，VSTOXX指数上的未来可以用于对冲经济不确定性。我们通过随机过程和机器学习方法的结合研究了交易者库存对VSTOXX期货价格的影响。我们为VSTOXX期货制定了一种简单有效的定价方法，该方法假设了基础欧元STOXX 50市场的Heston型随机过程。在这些动力学下，最近得出了隐含波动性微笑和VSTOXX指数的近似分析公式。我们使用Euro STOXX 50选项隐含波动率和VSTOXX索引值来估计此Heston模型的参数。校准后，我们计算理论VSTOXX未来价格，并将其与实际市场价格进行比较。尽管理论和市场价格通常是一致的，但我们也观察到时间段，在此期间，市场价格与我们的赫斯顿模式不一致。我们收集了各种市场功能，这些市场可能有可能解释价格偏差，并将两个机器学习模型校准给价格差异：正规化的线性模型和一个随机的森林。我们发现这两个模型都表明累积交易者头寸对Vstoxx期货价格产生了强烈的影响。
The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX future prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.
