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:
[1]

Y. Achdou and O. Pironneau, Computational Methods for Option Pricing SIAM, 2005. doi: 10.1137/1. 9780898717495.  Google Scholar

[2]

Y. Achdou and O. Pironneau, Numerical procedure for calibration of volatility with American options, Applied Mathematical Finance, 12 (2007), 201-241.  doi: 10.1080/1350486042000297252.  Google Scholar

[3]

V. Albani, U. Ascher and J. Zubelli, Local volatility models in commodity markets and online calibration, J. Computational Finance 2017. Accepted, to appear. Google Scholar

[4]

V. Albani and J. P. Zubelli, Online local volatility calibration by convex regularization, Appl. Anal. Discrete Math., 8 (2014), 243-268.  doi: 10.2298/AADM140811012A.  Google Scholar

[5]

U. AscherH. Huang and K. van den Doel, Artificial time integration, BIT, 47 (2007), 3-25.  doi: 10.1007/s10543-006-0112-x.  Google Scholar

[6]

F. Black and M. Scholes, The pricing of options and corporate liabilities, J. Pol. Econ., 81 (1973), 637-654.  doi: 10.1086/260062.  Google Scholar

[7]

P. Boyle and D. Thangaraj, Volatility estimation from observed option prices, Decisions in Economics and Finance, 23 (2000), 31-52.  doi: 10.1007/s102030050004.  Google Scholar

[8]

D. Calvetti, O. Ernst and E. Somersalo, Dynamic updating of numerical model discrepancy using sequential sampling Inverse Problems 30 (2014), 114019, 19pp. doi: 10.1088/0266-5611/30/11/114019.  Google Scholar

[9]

A. De CezaroO. Scherzer and J. Zubelli, Convex regularization of local volatility models from option prices: convergence analysis and rates, Nonlinear Analysis, 75 (2012), 2398-2415.  doi: 10.1016/j.na.2011.10.037.  Google Scholar

[10]

A. De Cezaro and J. P. Zubelli, The tangential cone condition for the iterative calibration of local volatility surfaces, IMA Journal of Applied Mathematics, 80 (2015), 212-232.  doi: 10.1093/imamat/hxt037.  Google Scholar

[11]

B. Dupire, Pricing with a smile, Risk, 7 (1994), 18-20.   Google Scholar

[12]

H. Egger and H. Engl, Tikhonov regularization applied to the inverse problem of option pricing: convergence analysis and rates, Inverse Problems, 21 (2005), 1027-1045.  doi: 10.1088/0266-5611/21/3/014.  Google Scholar

[13]

H. W. Engl, M. Hanke and A. Neubauer, Regularization of Inverse Problems Kluwer, 1996. doi: 10.1007/978-94-009-1740-8.  Google Scholar

[14]

J. Gatheral, The Volatility Surface: A Practitioner's Guide Wiley Finance. John Wiley & Sons, 2006. doi: 10.1002/9781119202073.  Google Scholar

[15]

J. Granek and E. Haber, Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties Proc. SIAM Conference on Data Mining (2015), 9pp. doi: 10.1137/1.9781611974010.17.  Google Scholar

[16]

E. HaberU. Ascher and D. Oldenburg, Inversion of 3D electromagnetic data in frequency and time domain using an inexact all-at-once approach, Geophysics, 69 (2004), 1216-1228.  doi: 10.1190/1.1801938.  Google Scholar

[17]

B. Hofmann and R. Krämer, On maximum entropy regularization for a specific inverse problem of option pricing, J. Inverse Ill-Posed Problems, 13 (2005), 41-63.  doi: 10.1515/1569394053583739.  Google Scholar

[18]

B. HofmannB. KaltenbacherC. Pöschl and O. Scherzer, A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators, Inverse Problems, 23 (2007), 987-1010.  doi: 10.1088/0266-5611/23/3/009.  Google Scholar

[19]

H. Huang and U. Ascher, Fast denoising of surface meshes with intrinsic texture Inverse Problems 24 (2008), 034003, 18pp. doi: 10.1088/0266-5611/24/3/034003.  Google Scholar

[20]

M. Iglesias, K. Law and A. Stuart, Ensemble Kalman methods for inverse problems Inverse Problems 29 (2013), 045001, 20pp. doi: 10.1088/0266-5611/29/4/045001.  Google Scholar

[21]

R. JarrowY. Kchia and P. Protter, How to detect an asset bubble, SIAM J. Financial Mathematics, 2 (2011), 839-865.  doi: 10.1137/10079673X.  Google Scholar

[22]

C. Johns and J. Mandel, A two-stage ensemble Kalman filter for smooth data assimilation, Environmental and Ecological Statistics, 15 (2008), 101-110.  doi: 10.1007/s10651-007-0033-0.  Google Scholar

[23]

N. Kahale, Smile interpolation and calibration of the local volatility model, Risk Magazine, 1 (2005), 637-654.   Google Scholar

[24]

R. Korn and E. Korn, Option Price and Portfolio Optimization: Modern Methods of Mathematical Finance volume 31 of Graduate Studies in Mathematics, AMS, 2001. doi: 10.1007/978-3-322-83210-8.  Google Scholar

[25]

R. KumarC. da SilvaO. AklainA. AravkinH. MansourB. Recht and F. Herrmann, Efficient matrix completion for seismic data reconstruction, Geophysics, 80 (2015), 97-114.  doi: 10.1190/geo2014-0369.1.  Google Scholar

[26]

G. Nakamura and R. Potthast, Inverse Problems. An Introduction to the Theory and Methods of Inverse Problems and Data Assimilation IOP Publishing, 2015. Google Scholar

[27]

S. Reich and C. Cotter, Probabilistic Forecasting and Bayesian Data Assimilation Cambridge, 2015. doi: 10.1017/CBO9781107706804.  Google Scholar

[28]

F. Roosta-KhorasaniK. van den Doel and U. Ascher, Data completion and stochastic algorithms for PDE inversion problems with many measurements, ETNA, 42 (2014), 177-196.   Google Scholar

[29]

C. Vogel, Computational Methods for Inverse Problem SIAM, Philadelphia, 2002. doi: 10.1137/1.9780898717570.  Google Scholar

show all references

References:
[1]

Y. Achdou and O. Pironneau, Computational Methods for Option Pricing SIAM, 2005. doi: 10.1137/1. 9780898717495.  Google Scholar

[2]

Y. Achdou and O. Pironneau, Numerical procedure for calibration of volatility with American options, Applied Mathematical Finance, 12 (2007), 201-241.  doi: 10.1080/1350486042000297252.  Google Scholar

[3]

V. Albani, U. Ascher and J. Zubelli, Local volatility models in commodity markets and online calibration, J. Computational Finance 2017. Accepted, to appear. Google Scholar

[4]

V. Albani and J. P. Zubelli, Online local volatility calibration by convex regularization, Appl. Anal. Discrete Math., 8 (2014), 243-268.  doi: 10.2298/AADM140811012A.  Google Scholar

[5]

U. AscherH. Huang and K. van den Doel, Artificial time integration, BIT, 47 (2007), 3-25.  doi: 10.1007/s10543-006-0112-x.  Google Scholar

[6]

F. Black and M. Scholes, The pricing of options and corporate liabilities, J. Pol. Econ., 81 (1973), 637-654.  doi: 10.1086/260062.  Google Scholar

[7]

P. Boyle and D. Thangaraj, Volatility estimation from observed option prices, Decisions in Economics and Finance, 23 (2000), 31-52.  doi: 10.1007/s102030050004.  Google Scholar

[8]

D. Calvetti, O. Ernst and E. Somersalo, Dynamic updating of numerical model discrepancy using sequential sampling Inverse Problems 30 (2014), 114019, 19pp. doi: 10.1088/0266-5611/30/11/114019.  Google Scholar

[9]

A. De CezaroO. Scherzer and J. Zubelli, Convex regularization of local volatility models from option prices: convergence analysis and rates, Nonlinear Analysis, 75 (2012), 2398-2415.  doi: 10.1016/j.na.2011.10.037.  Google Scholar

[10]

A. De Cezaro and J. P. Zubelli, The tangential cone condition for the iterative calibration of local volatility surfaces, IMA Journal of Applied Mathematics, 80 (2015), 212-232.  doi: 10.1093/imamat/hxt037.  Google Scholar

[11]

B. Dupire, Pricing with a smile, Risk, 7 (1994), 18-20.   Google Scholar

[12]

H. Egger and H. Engl, Tikhonov regularization applied to the inverse problem of option pricing: convergence analysis and rates, Inverse Problems, 21 (2005), 1027-1045.  doi: 10.1088/0266-5611/21/3/014.  Google Scholar

[13]

H. W. Engl, M. Hanke and A. Neubauer, Regularization of Inverse Problems Kluwer, 1996. doi: 10.1007/978-94-009-1740-8.  Google Scholar

[14]

J. Gatheral, The Volatility Surface: A Practitioner's Guide Wiley Finance. John Wiley & Sons, 2006. doi: 10.1002/9781119202073.  Google Scholar

[15]

J. Granek and E. Haber, Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties Proc. SIAM Conference on Data Mining (2015), 9pp. doi: 10.1137/1.9781611974010.17.  Google Scholar

[16]

E. HaberU. Ascher and D. Oldenburg, Inversion of 3D electromagnetic data in frequency and time domain using an inexact all-at-once approach, Geophysics, 69 (2004), 1216-1228.  doi: 10.1190/1.1801938.  Google Scholar

[17]

B. Hofmann and R. Krämer, On maximum entropy regularization for a specific inverse problem of option pricing, J. Inverse Ill-Posed Problems, 13 (2005), 41-63.  doi: 10.1515/1569394053583739.  Google Scholar

[18]

B. HofmannB. KaltenbacherC. Pöschl and O. Scherzer, A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators, Inverse Problems, 23 (2007), 987-1010.  doi: 10.1088/0266-5611/23/3/009.  Google Scholar

[19]

H. Huang and U. Ascher, Fast denoising of surface meshes with intrinsic texture Inverse Problems 24 (2008), 034003, 18pp. doi: 10.1088/0266-5611/24/3/034003.  Google Scholar

[20]

M. Iglesias, K. Law and A. Stuart, Ensemble Kalman methods for inverse problems Inverse Problems 29 (2013), 045001, 20pp. doi: 10.1088/0266-5611/29/4/045001.  Google Scholar

[21]

R. JarrowY. Kchia and P. Protter, How to detect an asset bubble, SIAM J. Financial Mathematics, 2 (2011), 839-865.  doi: 10.1137/10079673X.  Google Scholar

[22]

C. Johns and J. Mandel, A two-stage ensemble Kalman filter for smooth data assimilation, Environmental and Ecological Statistics, 15 (2008), 101-110.  doi: 10.1007/s10651-007-0033-0.  Google Scholar

[23]

N. Kahale, Smile interpolation and calibration of the local volatility model, Risk Magazine, 1 (2005), 637-654.   Google Scholar

[24]

R. Korn and E. Korn, Option Price and Portfolio Optimization: Modern Methods of Mathematical Finance volume 31 of Graduate Studies in Mathematics, AMS, 2001. doi: 10.1007/978-3-322-83210-8.  Google Scholar

[25]

R. KumarC. da SilvaO. AklainA. AravkinH. MansourB. Recht and F. Herrmann, Efficient matrix completion for seismic data reconstruction, Geophysics, 80 (2015), 97-114.  doi: 10.1190/geo2014-0369.1.  Google Scholar

[26]

G. Nakamura and R. Potthast, Inverse Problems. An Introduction to the Theory and Methods of Inverse Problems and Data Assimilation IOP Publishing, 2015. Google Scholar

[27]

S. Reich and C. Cotter, Probabilistic Forecasting and Bayesian Data Assimilation Cambridge, 2015. doi: 10.1017/CBO9781107706804.  Google Scholar

[28]

F. Roosta-KhorasaniK. van den Doel and U. Ascher, Data completion and stochastic algorithms for PDE inversion problems with many measurements, ETNA, 42 (2014), 177-196.   Google Scholar

[29]

C. Vogel, Computational Methods for Inverse Problem SIAM, Philadelphia, 2002. doi: 10.1137/1.9780898717570.  Google Scholar

Figure 1.  Data locations for a PBR (Petrobras, an oil company) set in the $(\tau, y)$ domain with our coarsest mesh in the background
Figure 2.  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 $S_0$
Figure 3.  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)
Figure 4.  The estimated spot price converges to the true price
Figure 5.  Locations of the SPX data in the $(\tau, y)$ domain with our coarsest mesh in the background
Figure 6.  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
Figure 8.  Reconstructed SPX local volatility surfaces obtained with six method variants. See legends in Figures 6, 7 and 10
Figure 9.  Reconstructed SPX local volatility surfaces obtained with six method variants for different maturities in the at-the-money ($y=0$) neighbourhood
Figure 10.  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
Figure 11.  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
Figure 12.  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
Figure 13.  Henry Hub prices: completed data (green line with pentagram), scarce data (blue continuous line), and market (red squares) implied volatilities
Figure 14.  WTI prices: completed data (green line with pentagram), sparse data (blue continuous line), and market (red squares) implied volatilities
Table 1.  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 $5.55$ $3.41$ $2.39$ $1.22$ $0.78$ $0.47$ $0.21$ $0.13$
$S_0$ $0.950$ $0.963$ $0.977$ $0.985$ $0.989$ $0.994$ $0.997$ $0.999$
Table 2.  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 $a_0$ $0.4^2/2$
Table 3.  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 $a_0$ $0.14^2/2$
Table 4.  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
Table 5.  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
Table 6.  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
Table 7.  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
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