March  2022, 7(1): 31-44. doi: 10.3934/puqr.2022003

Threshold reweighted Nadaraya–Watson estimation of jump-diffusion models

1. 

Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan 250100, Shandong, China

2. 

Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam 999077, Hong Kong, China

3. 

School of Finance and Business, Shanghai Normal University, Shanghai 200234, China

wanghanchao@sdu.edu.cn

Received  February 15, 2022 Accepted  March 11, 2022 Published  March 2022 Early access  March 2022

Fund Project: The research of Kunyang Song and Hanchao Wang is supported by the National Natural Science Foundation of China (Grant Nos.12071257 and 11971267), National Key R&D Program of China (Grant No. 2018YFA0703900), Shandong Provincial Natural Science Foundation (Grant No. ZR2019ZD41), and the Young Scholars Program of Shandong University. Yuping Song’s research is supported by the National Natural Science Foundation of China (Grant No. 11901397), Ministry of Education, Humanities and Social Sciences project (Grant No. 18YJCZH153), National Statistical Science Research Project (Grant No. 2018LZ05), Youth Academic Backbone Cultivation Project of Shanghai Normal University (Grant No. 310-AC7031-19-003021), General Research Fund of Shanghai Normal University (Grant No. SK201720), Key Subject of Quantitative Economics (Grant No. 310-AC7031-19-004221), and Academic Innovation Team of Shanghai Normal University (Grant No. 310-AC7031-19-004228).

In this paper, we propose a new method to estimate the diffusion function in the jump-diffusion model. First, a threshold reweighted Nadaraya–Watson-type estimator is introduced. Then, we establish asymptotic normality for the estimator and conduct Monte Carlo simulations through two examples to verify the better finite-sampling properties. Finally, our estimator is demonstrated through the actual data of the Shanghai Interbank Offered Rate in China.

Citation: Kunyang Song, Yuping Song, Hanchao Wang. Threshold reweighted Nadaraya–Watson estimation of jump-diffusion models. Probability, Uncertainty and Quantitative Risk, 2022, 7 (1) : 31-44. doi: 10.3934/puqr.2022003
References:
[1]

Aït-Sahalia, Y. and Jacod, J., High-Frequency Financial Economet rics, Princeton University Press, 2014.

[2]

Bandi, F. M. and Nguyen, T. H., On the functional estimation of jump-diffusion models, J. Econometrics, 2003, 116(1/2): 293−328.

[3]

Bandi, F. M. and Phillips, P. C. B., Fully nonparametric estimation of scalar diffusion models, Econometrica, 2003, 71(1): 241−283. doi: 10.1111/1468-0262.00395.

[4]

Barndorff-Nielsen, O. E. and Shephard, N., Econometrics of testing for jumps in financial economics using bipower variation, J. Financial Econometrics, 2006, 4(1): 1−30.

[5]

Fan, J., Fan, Y. and Jiang, J., Dynamic integration of time- and state-domain methods for volatility estimation, Journal of American Statistical Association, 2007, 102(478): 618−631. doi: 10.1198/016214507000000176.

[6]

Florens-Zmirou, D., On estimating the diffusion coefficient from discrete observations, J. Appl. Probab., 1993, 30(4): 790−804. doi: 10.2307/3214513.

[7]

Hanif, M., Wang, H. and Lin, Z., Reweighted Nadaraya-Watson estimation of jump-diffusion models, Science China Mathematics, 2012, 55(5): 1005−1016. doi: 10.1007/s11425-011-4340-4.

[8]

Jacod, J., Statistics and high-frequency data, Statistical Methods for Stochastic Differential Equations, 2012, 191–310, MR2976984.

[9]

Jacod, J. and Shiryaev, A., Limit Theorems for Stochastic Processes, Grundlehren der Mathematischen Wissenschaften Springer, 2003.

[10]

Jiang, G. and Knight, J., A nonparametric approach to the estimation of diffusion processes, with an application to a short-term interest rate model, Econometric Theory, 1997, 13(5): 615−645.

[11]

Lin, Z. and Wang, H., Empirical likelihood inference for diffusion processes with jumps, Science China Mathematics, 2010, 53(7): 1805−1816. doi: 10.1007/s11425-010-4027-2.

[12]

Mancini, C., Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps, Scand. J. Stat., 2009, 36(2): 270−296. doi: 10.1111/j.1467-9469.2008.00622.x.

[13]

Mancini, C. and Renò, R., Threshold estimation of Markov models with jumps and interest rate modeling, J. Econometrics, 2011, 160(1): 77−92. doi: 10.1016/j.jeconom.2010.03.019.

[14]

Revuz, D. and Yor, M., Continuous Martingales and Brownian Motion, Grundlehren der Mathematischen Wissenschaften, Springer, 1999.

[15]

Rogers, L. and Williams, D., Diffusions, Markov Processes and Martingales, Volume 2: Itô Calculus, Cambridge University Press, 2000.

[16]

Situ, R., Theory of Stochastic Differential Equations with Jumps and Applications: Mathematical and Analytical Techniques with Applications to Engineering, Springer Science & Business Media, 2005.

[17]

Song, Y. and Wang, H., Central limit theorems of local polynomial threshold estimator for diffusion processes with jumps, Scand. J. Stat., 2018, 45(3): 644−681. doi: 10.1111/sjos.12318.

[18]

Xu, K. L., Empirical likelihood based inference for nonparametric recurrent diffusions, J. Econometrics., 2009, 153(1): 65−82. doi: 10.1016/j.jeconom.2009.04.006.

[19]

Xu, K. L., Re-weighted functional estimation of diffusion models, Econometric Theory, 2010, 26(2): 541−563. doi: 10.1017/S0266466609100087.

show all references

References:
[1]

Aït-Sahalia, Y. and Jacod, J., High-Frequency Financial Economet rics, Princeton University Press, 2014.

[2]

Bandi, F. M. and Nguyen, T. H., On the functional estimation of jump-diffusion models, J. Econometrics, 2003, 116(1/2): 293−328.

[3]

Bandi, F. M. and Phillips, P. C. B., Fully nonparametric estimation of scalar diffusion models, Econometrica, 2003, 71(1): 241−283. doi: 10.1111/1468-0262.00395.

[4]

Barndorff-Nielsen, O. E. and Shephard, N., Econometrics of testing for jumps in financial economics using bipower variation, J. Financial Econometrics, 2006, 4(1): 1−30.

[5]

Fan, J., Fan, Y. and Jiang, J., Dynamic integration of time- and state-domain methods for volatility estimation, Journal of American Statistical Association, 2007, 102(478): 618−631. doi: 10.1198/016214507000000176.

[6]

Florens-Zmirou, D., On estimating the diffusion coefficient from discrete observations, J. Appl. Probab., 1993, 30(4): 790−804. doi: 10.2307/3214513.

[7]

Hanif, M., Wang, H. and Lin, Z., Reweighted Nadaraya-Watson estimation of jump-diffusion models, Science China Mathematics, 2012, 55(5): 1005−1016. doi: 10.1007/s11425-011-4340-4.

[8]

Jacod, J., Statistics and high-frequency data, Statistical Methods for Stochastic Differential Equations, 2012, 191–310, MR2976984.

[9]

Jacod, J. and Shiryaev, A., Limit Theorems for Stochastic Processes, Grundlehren der Mathematischen Wissenschaften Springer, 2003.

[10]

Jiang, G. and Knight, J., A nonparametric approach to the estimation of diffusion processes, with an application to a short-term interest rate model, Econometric Theory, 1997, 13(5): 615−645.

[11]

Lin, Z. and Wang, H., Empirical likelihood inference for diffusion processes with jumps, Science China Mathematics, 2010, 53(7): 1805−1816. doi: 10.1007/s11425-010-4027-2.

[12]

Mancini, C., Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps, Scand. J. Stat., 2009, 36(2): 270−296. doi: 10.1111/j.1467-9469.2008.00622.x.

[13]

Mancini, C. and Renò, R., Threshold estimation of Markov models with jumps and interest rate modeling, J. Econometrics, 2011, 160(1): 77−92. doi: 10.1016/j.jeconom.2010.03.019.

[14]

Revuz, D. and Yor, M., Continuous Martingales and Brownian Motion, Grundlehren der Mathematischen Wissenschaften, Springer, 1999.

[15]

Rogers, L. and Williams, D., Diffusions, Markov Processes and Martingales, Volume 2: Itô Calculus, Cambridge University Press, 2000.

[16]

Situ, R., Theory of Stochastic Differential Equations with Jumps and Applications: Mathematical and Analytical Techniques with Applications to Engineering, Springer Science & Business Media, 2005.

[17]

Song, Y. and Wang, H., Central limit theorems of local polynomial threshold estimator for diffusion processes with jumps, Scand. J. Stat., 2018, 45(3): 644−681. doi: 10.1111/sjos.12318.

[18]

Xu, K. L., Empirical likelihood based inference for nonparametric recurrent diffusions, J. Econometrics., 2009, 153(1): 65−82. doi: 10.1016/j.jeconom.2009.04.006.

[19]

Xu, K. L., Re-weighted functional estimation of diffusion models, Econometric Theory, 2010, 26(2): 541−563. doi: 10.1017/S0266466609100087.

Figure 1.  One path of process $ X_t $ in model (3.1)
Figure 2.  RNW and NW estimators for values of $ x $ of the path $ X_t $ in model (3.1)
Figure 3.  The QQ plot for $ \sigma^2(x) $ in model (3.1)
Figure 4.  One path of process $ X_t $ in model (3.2)
Figure 5.  RNW and NW estimators for different point $ x $ of the path $ X_t $ in model (3.2)
Figure 6.  The QQ plot for $ \sigma^2(x) $ in model (3.2)
Figure 7.  Time Series of the one-month Shibor
Table 1.  Values of various measures and estimators under different time spans and sampling numbers for the diffusion function in model (3.1)
Measures Estimators $ T = 5 $ $ T = 10 $ $ T = 20 $
$ n = 500 $ MSE NW 1.32E-08 6.91E-09 5.20E-08
RNW 6.50E-09 6.40E-09 5.11E-08
RMSE NW 1.15E-04 8.31E-05 2.28E-04
RNW 8.06E-05 8.00E-05 2.26E-04
MADE NW 8.98E-05 7.91E-05 2.20E-04
RNW 7.13E-05 7.51E-05 2.17E-04
$ n = 1000 $ MSE NW 2.02E-09 1.19E-09 1.15E-08
RNW 1.54E-09 9.32E-10 1.11E-08
RMSE NW 4.50E-05 3.44E-05 1.07E-04
RNW 3.92E-05 3.05E-05 1.05E-04
MADE NW 3.03E-05 2.56E-05 1.05E-04
RNW 3.21E-05 2.18E-05 1.03E-04
$ n = 2000 $ MSE NW 1.35E-09 3.84E-09 7.23E-09
RNW 1.19E-09 3.80E-09 7.15E-09
RMSE NW 3.67E-05 6.19E-05 8.51E-05
RNW 3.45E-05 6.16E-05 8.45E-05
MADE NW 3.27E-05 5.55E-05 8.11E-05
RNW 2.69E-05 5.59E-05 8.07E-05
  1 Note: 1.32E-08 denotes 1.32×10−8
Measures Estimators $ T = 5 $ $ T = 10 $ $ T = 20 $
$ n = 500 $ MSE NW 1.32E-08 6.91E-09 5.20E-08
RNW 6.50E-09 6.40E-09 5.11E-08
RMSE NW 1.15E-04 8.31E-05 2.28E-04
RNW 8.06E-05 8.00E-05 2.26E-04
MADE NW 8.98E-05 7.91E-05 2.20E-04
RNW 7.13E-05 7.51E-05 2.17E-04
$ n = 1000 $ MSE NW 2.02E-09 1.19E-09 1.15E-08
RNW 1.54E-09 9.32E-10 1.11E-08
RMSE NW 4.50E-05 3.44E-05 1.07E-04
RNW 3.92E-05 3.05E-05 1.05E-04
MADE NW 3.03E-05 2.56E-05 1.05E-04
RNW 3.21E-05 2.18E-05 1.03E-04
$ n = 2000 $ MSE NW 1.35E-09 3.84E-09 7.23E-09
RNW 1.19E-09 3.80E-09 7.15E-09
RMSE NW 3.67E-05 6.19E-05 8.51E-05
RNW 3.45E-05 6.16E-05 8.45E-05
MADE NW 3.27E-05 5.55E-05 8.11E-05
RNW 2.69E-05 5.59E-05 8.07E-05
  1 Note: 1.32E-08 denotes 1.32×10−8
Table 2.  Values of various measures and estimators under different time spans and sampling numbers for the diffusion function in model (3.2)
Measures Estimators $ T = 5 $ $ T = 10 $ $ T = 20 $
$ n = 500 $ MSE NW 3.58E-05 2.03E-05 1.86E-02
RNW 2.31E-05 1.73E-05 7.69E-03
RMSE NW 5.98E-03 4.50E-03 1.36E-01
RNW 4.81E-03 4.17E-03 8.77E-02
MADE NW 5.79E-03 3.77E-03 9.82E-02
RNW 4.70E-03 3.57E-03 6.06E-02
$ n = 1000 $ MSE NW 6.62E-06 6.75E-05 8.89E-04
RNW 5.21E-06 2.82E-05 6.86E-04
RMSE NW 2.57E-03 8.22E-03 2.98E-02
RNW 2.28E-03 5.31E-03 2.62E-02
MADE NW 2.19E-03 6.27E-03 2.59E-02
RNW 1.87E-03 4.10E-03 2.04E-02
$ n = 2000 $ MSE NW 1.40E-06 1.38E-04 1.31E-05
RNW 1.10E-06 4.57E-05 1.81E-06
RMSE NW 1.18E-03 1.18E-02 3.62E-03
RNW 1.05E-03 6.76E-03 1.35E-03
MADE NW 8.52E-04 1.02E-02 3.01E-03
RNW 7.56E-04 4.34E-03 1.11E-03
  1 Note: 3.58E-05 denotes 3.58×10−5.
Measures Estimators $ T = 5 $ $ T = 10 $ $ T = 20 $
$ n = 500 $ MSE NW 3.58E-05 2.03E-05 1.86E-02
RNW 2.31E-05 1.73E-05 7.69E-03
RMSE NW 5.98E-03 4.50E-03 1.36E-01
RNW 4.81E-03 4.17E-03 8.77E-02
MADE NW 5.79E-03 3.77E-03 9.82E-02
RNW 4.70E-03 3.57E-03 6.06E-02
$ n = 1000 $ MSE NW 6.62E-06 6.75E-05 8.89E-04
RNW 5.21E-06 2.82E-05 6.86E-04
RMSE NW 2.57E-03 8.22E-03 2.98E-02
RNW 2.28E-03 5.31E-03 2.62E-02
MADE NW 2.19E-03 6.27E-03 2.59E-02
RNW 1.87E-03 4.10E-03 2.04E-02
$ n = 2000 $ MSE NW 1.40E-06 1.38E-04 1.31E-05
RNW 1.10E-06 4.57E-05 1.81E-06
RMSE NW 1.18E-03 1.18E-02 3.62E-03
RNW 1.05E-03 6.76E-03 1.35E-03
MADE NW 8.52E-04 1.02E-02 3.01E-03
RNW 7.56E-04 4.34E-03 1.11E-03
  1 Note: 3.58E-05 denotes 3.58×10−5.
Table 3.  Comparisons between NW and RNW estimators for $ \sigma^{2}(x) $ with various measures and different bandwidths
Measure $ h = c \cdot \hat{S} \cdot n^{-\frac{1}{5}} $
$ c = 1.06 $ $ c = 3 $ $ c = 5 $
NW RNW NW RNW NW RNW
MSE 0.999917 0.999910 0.999902 0.999900 0.999909 0.999900
RMSE 0.999959 0.999955 0.999951 0.999950 0.999954 0.999950
MADE 0.999959 0.999955 0.999951 0.999950 0.999954 0.999950
RADE 0.999979 0.999977 0.999976 0.999975 0.999977 0.999975
Measure $ h = c \cdot \hat{S} \cdot n^{-\frac{1}{5}} $
$ c = 1.06 $ $ c = 3 $ $ c = 5 $
NW RNW NW RNW NW RNW
MSE 0.999917 0.999910 0.999902 0.999900 0.999909 0.999900
RMSE 0.999959 0.999955 0.999951 0.999950 0.999954 0.999950
MADE 0.999959 0.999955 0.999951 0.999950 0.999954 0.999950
RADE 0.999979 0.999977 0.999976 0.999975 0.999977 0.999975
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