# American Institute of Mathematical Sciences

October  2017, 11(5): 799-823. doi: 10.3934/ipi.2017038

## Data driven recovery of local volatility surfaces

 1 Dept. of Mathematics, UFSC, Florianopolis, Brazil 2 Dept. of Computer Science, University of British Columbia, Canada 3 IMPA, Rio de Janeiro, Brazil

Received  September 2016 Revised  May 2017 Published  June 2017

Fund Project: VA acknowledges and thanks CNPq through grant 201644/2014-2. UMA and XY acknowledge with thanks a Ciencias Sem Fronteiras (visiting scientist / postdoc) grant from CAPES, Brazil. JPZ thanks the support of CNPq grant 307873 and FAPERJ grant 201.288/2014.

This paper examines issues of data completion and location uncertainty, popular in many practical PDE-based inverse problems, in the context of option calibration via recovery of local volatility surfaces. While real data is usually more accessible for this application than for many others, the data is often given only at a restricted set of locations. We show that attempts to "complete missing data" by approximation or interpolation, proposed and applied in the literature, may produce results that are inferior to treating the data as scarce. Furthermore, model uncertainties may arise which translate to uncertainty in data locations, and we show how a model-based adjustment of the asset price may prove advantageous in such situations. We further compare a carefully calibrated Tikhonov-type regularization approach against a similarly adapted EnKF method, in an attempt to fine-tune the data assimilation process. The EnKF method offers reassurance as a different method for assessing the solution in a problem where information about the true solution is difficult to come by. However, additional advantage in the latter approach turns out to be limited in our context.

Citation: Vinicius Albani, Uri M. Ascher, Xu Yang, Jorge P. Zubelli. Data driven recovery of local volatility surfaces. Inverse Problems & Imaging, 2017, 11 (5) : 799-823. doi: 10.3934/ipi.2017038
##### References:

show all references

##### References:
Data locations for a PBR (Petrobras, an oil company) set in the $(\tau, y)$ domain with our coarsest mesh in the background
Reconstructed (continuous line) and true (line with circles) local volatility surfaces at the five different maturities. The reconstructed local volatility surface corresponds to the one obtained with the adjustment algorithm of the underlying asset $S_0$
Calibration of the local volatility in 5 iterations. Shown, starting from the upper left, are the 1st, 3rd, and 5th iterations, as well as the ground truth (bottom right)
The estimated spot price converges to the true price
Locations of the SPX data in the $(\tau, y)$ domain with our coarsest mesh in the background
Reconstructed SPX local volatility surfaces at different maturities obtained with three method variants using scarce data
">Figure 7.  Reconstructed SPX local volatility surfaces at different maturities obtained with Tikhonov-type and EnKF methods using completed data. These results are inferior to the corresponding ones for scarce data, displayed in Figure 6
, 7 and 10">Figure 8.  Reconstructed SPX local volatility surfaces obtained with six method variants. See legends in Figures 6, 7 and 10
Reconstructed SPX local volatility surfaces obtained with six method variants for different maturities in the at-the-money ($y=0$) neighbourhood
Implied (Black-Scholes) volatility corresponding to the local volatility surfaces, obtained with the six method variants (Tikhonov, EnKF and "no $a_0$" applied to real and completed data) and compared to the market one
Reconstructed local volatility for different maturity dates for Henry Hub call option prices, comparing between completed data (green line with pentagram) and scarce data (blue line) results
Reconstructed local volatility for different maturity dates for WTI call option prices, comparing between completed data (green line with pentagram) and scarce data (blue line) results
Henry Hub prices: completed data (green line with pentagram), scarce data (blue continuous line), and market (red squares) implied volatilities
WTI prices: completed data (green line with pentagram), sparse data (blue continuous line), and market (red squares) implied volatilities
Normalized $\ell_2$-distance between the true and the reconstructed local volatility surfaces and the value of $S_0$ at each step of the algorithm for adjusting $S_0$
 Iteration 1 2 3 4 5 6 7 8 Normalized Distance $5.55$ $3.41$ $2.39$ $1.22$ $0.78$ $0.47$ $0.21$ $0.13$ $S_0$ $0.950$ $0.963$ $0.977$ $0.985$ $0.989$ $0.994$ $0.997$ $0.999$
 Iteration 1 2 3 4 5 6 7 8 Normalized Distance $5.55$ $3.41$ $2.39$ $1.22$ $0.78$ $0.47$ $0.21$ $0.13$ $S_0$ $0.950$ $0.963$ $0.977$ $0.985$ $0.989$ $0.994$ $0.997$ $0.999$
Parameters for the example of Figure 3
 $\widehat{S}_0$ initial spot price 2500 $S_{\mathrm{true}}$ optimal spot price 2200 $r$ interest rate 0.25% the maximum maturity 1.8 Minimum $y$ -3.5 Maximum $y$ 3.5 $\Delta \tau$ 0.1 $\Delta y$ 0.1 a priori surface $a_0$ $0.4^2/2$
 $\widehat{S}_0$ initial spot price 2500 $S_{\mathrm{true}}$ optimal spot price 2200 $r$ interest rate 0.25% the maximum maturity 1.8 Minimum $y$ -3.5 Maximum $y$ 3.5 $\Delta \tau$ 0.1 $\Delta y$ 0.1 a priori surface $a_0$ $0.4^2/2$
Parameters for the equity data examples
 $S_0$ initial spot price 2112.7 $S_0$ optimal spot price 2095.6 $r$ interest rate 0.25% the maximum maturity 2.5 Minimum $y$ -4.5 Maximum $y$ 1.5 $\Delta \tau$ 0.05 $\Delta y$ 0.1 initial $a_0$ $0.14^2/2$
 $S_0$ initial spot price 2112.7 $S_0$ optimal spot price 2095.6 $r$ interest rate 0.25% the maximum maturity 2.5 Minimum $y$ -4.5 Maximum $y$ 1.5 $\Delta \tau$ 0.05 $\Delta y$ 0.1 initial $a_0$ $0.14^2/2$
Parameters of the penalty functional (10) or (12) with SPX data
 Parameter $\alpha_0$ $\alpha_1$ $\alpha_2$ $\alpha_3$ Value 4.e+8 1.e+6 or 0 1.e+6 1.e+6
 Parameter $\alpha_0$ $\alpha_1$ $\alpha_2$ $\alpha_3$ Value 4.e+8 1.e+6 or 0 1.e+6 1.e+6
Residuals of the 6 method variants
 Tikhonov-type EnKF Scarce Comp. Scarce (no $a_0$) Comp. (no $a_0$) Scarce Comp. Residual 0.0196 0.0314 0.0247 0.0289 0.0198 0.0294
 Tikhonov-type EnKF Scarce Comp. Scarce (no $a_0$) Comp. (no $a_0$) Scarce Comp. Residual 0.0196 0.0314 0.0247 0.0289 0.0198 0.0294
Measures of data misfit of the 6 models
 Tikhonov-type EnKF Scarce Comp. Scarce (no $a_0$) Comp. (no $a_0$) Scarce Comp. RMSE 0.0195 0.0321 0.0290 0.0325 0.0255 0.0324 RWMSE 0.0175 0.0241 0.0252 0.0242 0.0241 0.0242 RR 0.1407 0.1987 0.2292 0.2186 0.1766 0.2186
 Tikhonov-type EnKF Scarce Comp. Scarce (no $a_0$) Comp. (no $a_0$) Scarce Comp. RMSE 0.0195 0.0321 0.0290 0.0325 0.0255 0.0324 RWMSE 0.0175 0.0241 0.0252 0.0242 0.0241 0.0242 RR 0.1407 0.1987 0.2292 0.2186 0.1766 0.2186
Parameters obtained in the local volatility calibration with Henry Hub and WTI call prices using sparse data and completed data
 WTI Henry Hub Comp. Data Sparse Data Comp. Data Sparse Data Running Time (sec.) $1.40\times10^{3}$ $3.07\times10^{2}$ $1.41\times10^{3}$ $1.02\times10^{3}$ $\alpha_0$ 1.0e4 1.0e3 1.0e3 1.0e3 $\alpha_1=\alpha_2=\alpha_3$ 4.5 1.0 1.3 1.0 Price Residual 2.16e-2 3.21e-3 3.47e-2 2.14e-2 Implied Vol. Residual 1.26e-1 2.66e-2 9.61e-2 5.98e-2
 WTI Henry Hub Comp. Data Sparse Data Comp. Data Sparse Data Running Time (sec.) $1.40\times10^{3}$ $3.07\times10^{2}$ $1.41\times10^{3}$ $1.02\times10^{3}$ $\alpha_0$ 1.0e4 1.0e3 1.0e3 1.0e3 $\alpha_1=\alpha_2=\alpha_3$ 4.5 1.0 1.3 1.0 Price Residual 2.16e-2 3.21e-3 3.47e-2 2.14e-2 Implied Vol. Residual 1.26e-1 2.66e-2 9.61e-2 5.98e-2
 [1] Andreas Bock, Colin J. Cotter. Learning landmark geodesics using the ensemble Kalman filter. Foundations of Data Science, 2021  doi: 10.3934/fods.2021020 [2] Wenxiu Gong, Zuoliang Xu. An alternative tree method for calibration of the local volatility. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020146 [3] Junyoung Jang, Kihoon Jang, Hee-Dae Kwon, Jeehyun Lee. Feedback control of an HBV model based on ensemble kalman filter and differential evolution. Mathematical Biosciences & Engineering, 2018, 15 (3) : 667-691. doi: 10.3934/mbe.2018030 [4] Weihong Guo, Yifei Lou, Jing Qin, Ming Yan. IPI special issue on "mathematical/statistical approaches in data science" in the Inverse Problem and Imaging. Inverse Problems & Imaging, 2021, 15 (1) : I-I. doi: 10.3934/ipi.2021007 [5] Pedro Caro. On an inverse problem in electromagnetism with local data: stability and uniqueness. Inverse Problems & Imaging, 2011, 5 (2) : 297-322. doi: 10.3934/ipi.2011.5.297 [6] Victor Isakov. On uniqueness in the inverse conductivity problem with local data. Inverse Problems & Imaging, 2007, 1 (1) : 95-105. doi: 10.3934/ipi.2007.1.95 [7] Jiangqi Wu, Linjie Wen, Jinglai Li. Resampled ensemble Kalman inversion for Bayesian parameter estimation with sequential data. Discrete & Continuous Dynamical Systems - S, 2021  doi: 10.3934/dcdss.2021045 [8] Ian Knowles, Ajay Mahato. The inverse volatility problem for American options. Discrete & Continuous Dynamical Systems - S, 2020, 13 (12) : 3473-3489. doi: 10.3934/dcdss.2020235 [9] Qinghua Ma, Zuoliang Xu, Liping Wang. Recovery of the local volatility function using regularization and a gradient projection method. Journal of Industrial & Management Optimization, 2015, 11 (2) : 421-437. doi: 10.3934/jimo.2015.11.421 [10] Gabriel Peyré, Sébastien Bougleux, Laurent Cohen. Non-local regularization of inverse problems. Inverse Problems & Imaging, 2011, 5 (2) : 511-530. doi: 10.3934/ipi.2011.5.511 [11] Alexander Bibov, Heikki Haario, Antti Solonen. Stabilized BFGS approximate Kalman filter. Inverse Problems & Imaging, 2015, 9 (4) : 1003-1024. doi: 10.3934/ipi.2015.9.1003 [12] Russell Johnson, Carmen Núñez. The Kalman-Bucy filter revisited. Discrete & Continuous Dynamical Systems, 2014, 34 (10) : 4139-4153. doi: 10.3934/dcds.2014.34.4139 [13] Sebastian Reich, Seoleun Shin. On the consistency of ensemble transform filter formulations. Journal of Computational Dynamics, 2014, 1 (1) : 177-189. doi: 10.3934/jcd.2014.1.177 [14] Luca Rondi. On the regularization of the inverse conductivity problem with discontinuous conductivities. Inverse Problems & Imaging, 2008, 2 (3) : 397-409. doi: 10.3934/ipi.2008.2.397 [15] Laurent Devineau, Pierre-Edouard Arrouy, Paul Bonnefoy, Alexandre Boumezoued. Fast calibration of the Libor market model with stochastic volatility and displaced diffusion. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1699-1729. doi: 10.3934/jimo.2019025 [16] Neil K. Chada, Claudia Schillings, Simon Weissmann. On the incorporation of box-constraints for ensemble Kalman inversion. Foundations of Data Science, 2019, 1 (4) : 433-456. doi: 10.3934/fods.2019018 [17] Håkon Hoel, Gaukhar Shaimerdenova, Raúl Tempone. Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators. Foundations of Data Science, 2020, 2 (4) : 351-390. doi: 10.3934/fods.2020017 [18] Le Yin, Ioannis Sgouralis, Vasileios Maroulas. Topological reconstruction of sub-cellular motion with Ensemble Kalman velocimetry. Foundations of Data Science, 2020, 2 (2) : 101-121. doi: 10.3934/fods.2020007 [19] Marc Bocquet, Alban Farchi, Quentin Malartic. Online learning of both state and dynamics using ensemble Kalman filters. Foundations of Data Science, 2021, 3 (3) : 305-330. doi: 10.3934/fods.2020015 [20] Zhiyan Ding, Qin Li, Jianfeng Lu. Ensemble Kalman Inversion for nonlinear problems: Weights, consistency, and variance bounds. Foundations of Data Science, 2021, 3 (3) : 371-411. doi: 10.3934/fods.2020018

2020 Impact Factor: 1.639