Modern Stochastics: Theory and Applications

Bayer, C., Breneis, S.: Markovian approximations of stochastic Volterra equations with the fractional kernel. Quant. Finance 23(1), 53–70 (2023). MR4521278. https://doi.org/10.1080/14697688.2022.2139193

Benth, F.E., Harang, F.A.: Infinite dimensional pathwise Volterra processes driven by Gaussian noise–Probabilistic properties and applications. Electron. J. Probab. 26, 1–42 (2021). MR4309972. https://doi.org/10.1214/21-ejp683

Cass, T., Lim, N.: Skorohod and rough integration for stochastic differential equations driven by Volterra processes. Ann. Inst. Henri Poincaré B, Probab. Stat. 57, 132–168 (2021). MR4255171. https://doi.org/10.1214/20-aihp1074

Coupek, P., Maslowski, B.: Stochastic evolution equations with Volterra noise. Stoch. Process. Appl. 127(3), 877–900 (2017). MR3605714. https://doi.org/10.1016/j.spa.2016.07.003

Duncan, T.E., Maslowski, B., Pasik-Duncan, B.: Linear stochastic differential equations driven by Gauss-Volterra processes and related linear-quadratic control problems. Appl. Math. Optim. 80, 369–389 (2019). MR4008669. https://doi.org/10.1007/s00245-017-9468-3

El Omari, M.: On the Gaussian Volterra processes with power-type kernels. Stoch. Models 40(1), 152–165 (2024). MR4694530. https://doi.org/10.1080/15326349.2023.2212763

Fan, X., Jiang, X.: Stochastic control problem for distribution dependent SDE driven by a Gauss Volterra process. Discrete Contin. Dyn. Syst., Ser. S 16(5), 1041–1061 (2023). MR4571171. https://doi.org/10.3934/dcdss.2023031

Gehringer, J., Li, X.M., Sieber, J.: Functional limit theorems for Volterra processes and applications to homogenization. Nonlinearity 35(4), 1521 (2022). MR4399494. https://doi.org/10.1088/1361-6544/ac4818

Malyarenko, A., Mishura, Y., Ralchenko, K., Shklyar, S.: Entropy and alternative entropy functionals of fractional Gaussian noise as the functions of Hurst index. Fract. Calc. Appl. Anal. 26(3), 1052–1081 (2023). MR4599227. https://doi.org/10.1007/s13540-023-00155-2

Mishura, Y., Shklyar, S.: Gaussian Volterra processes with power-type kernels. part II. Mod. Stoch. Theory Appl. 9(4), 431–452 (2022). MR4510382. https://doi.org/10.15559/22-VMSTA211

Mishura, Y., Ottaviano, S., Vargiolu, T.: Gaussian Volterra processes as models of electricity markets. arXiv:2311.09384. https://doi.org/10.48550/arXiv.2311.09384

Mishura, Y., Shklyar, S.: Gaussian Volterra processes with power-type kernels. part I. Mod. Stoch. Theory Appl. 9(3), 313–338 (2022). MR4462026. https://doi.org/10.15559/22-VMSTA205

Mishura, Y., Ralchenko, K., Schilling, R.L.: Analytical and computational problems related to fractional gaussian noise. Fractal Fract. 6(11), 1–22 (2022). https://doi.org/10.3390/fractalfract6110620

Mishura, Y., Ralchenko, K., Shklyar, S.: General conditions of weak convergence of discrete-time multiplicative scheme to asset price with memory. Risks 8(1), 1–29 (2020). https://doi.org/10.3390/risks8010011

Mishura, Y., Ralchenko, K., Shklyar, S.: Gaussian volterra processes: Asymptotic growth and statistical estimation. Theory Probab. Math. Stat. 108, 149–167 (2023). MR4588243. https://doi.org/10.1090/tpms/1190

Mishura, Y., Shevchenko, G., Shklyar, S.: Gaussian processes with Volterra Kernels. In: Malyarenko, A., Ni, Y., Rančić, M., Silvestrov, S. (eds.) Stochastic Processes, Statistical Methods, and Engineering Mathematics. SPAS 2019. Springer Proceedings in Mathematics & Statistics, vol. 408, pp. 249–276. Springer, Cham (2022). MR4607849. https://doi.org/10.1007/978-3-031-17820-7_13

Norros, I., Valkeila, E., Virtamo, J.: An elementary approach to a Girsanov formula and other analytical results on fractional Brownian motions. Bernoulli 5(4), 571–587 (1999). MR1704556. https://doi.org/10.2307/3318691

Perestyuk, M., Vyshenskyi, V.: Combinatorics: First Steps. Nova Science Publishers, Incorporated (2021). https://doi.org/10.52305/FIZC1542

Ross, M., Smith, M.T., Álvarez, M.: Learning nonparametric Volterra kernels with Gaussian processes. In: Beygelzimer, A., Dauphin, Y., Liang, P., Wortman Vaughan, J. (eds.) Advances in Neural Information Processing Systems. NeurIPS 2021, vol. 34, pp. 24099–24110. Curran Associates, Inc. (2021).

Valdivia, A.: Information loss on Gaussian Volterra process. Electron. Commun. Probab. 22, 1–5 (2017). MR3724558. https://doi.org/10.1214/17-ECP79