Pathwise asymptotics for Volterra processes conditioned to a noisy version of the Brownian motion        
        
    
        Volume 7, Issue 1 (2020), pp. 17–41
            
    
                    Pub. online: 27 February 2020
                    
        Type: Research Article
            
                
            
Open Access
        
            
    
                Received
30 July 2019
                                    30 July 2019
                Revised
30 November 2019
                                    30 November 2019
                Accepted
10 February 2020
                                    10 February 2020
                Published
27 February 2020
                    27 February 2020
Abstract
In this paper we investigate a problem of large deviations for continuous Volterra processes under the influence of model disturbances. More precisely, we study the behavior, in the near future after T, of a Volterra process driven by a Brownian motion in a case where the Brownian motion is not directly observable, but only a noisy version is observed or some linear functionals of the noisy version are observed. Some examples are discussed in both cases.
            References
 Azencott, R.: Grande Déviations et applications. In: École d’été de probabilités de St. Flour VIII. L.N.M., vol. 774. Springer, Berlin/Heidelberg/New York (1980). MR0590626
 Azmoodeh, E., Sottinen, T., Viitasaari, L., Yazigi, A.: Necessary and sufficient conditions for Hölder continuity of Gaussian processes. Stat. Probab. Lett. 94, 230–235 (2014). MR3257384. https://doi.org/10.1016/j.spl.2014.07.030
 Berlinet, A., Thomas-Agnan, C.: Reproducing Kernel Hilbert Spaces in Probability and Statistics. Kluwer Academic Publishers, Norwell (2004). MR2239907. https://doi.org/10.1007/978-1-4419-9096-9
 Caramellino, L., Pacchiarotti, B.: Large deviation estimates of the crossing probability for pinned Gaussian processes. Adv. Appl. Probab. 40, 424–453 (2008). MR2431304. https://doi.org/10.1239/aap/1214950211
 Dembo, A., Zeitouni, O.: Large Deviations Techniques and Applications. Jones and Bartlett, Boston, MA (1998). MR1202429
 Deuschel, J.D., Stroock, D.W.: Large Deviations. Academic Press, Boston, MA (1989). MR0997938
 Dudley, R.M.: The sizes of compact subsets of Hilbert space and continuity of Gaussian processes. J. Funct. Anal. 1, 290–330 (1967). MR0220340. https://doi.org/10.1016/0022-1236(67)90017-1
 Dudley, R.M.: Sample functions of the Gaussian process. Ann. Probab. 1(1), 66–103 (1973). MR0346884. https://doi.org/10.1214/aop/1176997026
 Giorgi, F., Pacchiarotti, B.: Large deviations for conditional Volterra processes. Stoch. Anal. Appl. 35(2), 191–210 (2017). MR3597612. https://doi.org/10.1080/07362994.2016.1237291
 Hida, T., Hitsuda, M.: Gaussian Processes. AMS Translations (1993). MR1216518
 La Gatta, T.: Continuous disintegrations of Gaussian processes. Theory Probab. Appl. 57(1), 151–162 (2013). MR3201645. https://doi.org/10.1137/S0040585X9798587X
 Macci, C., Pacchiarotti, B.: Exponential tightness for Gaussian processes with applications to some sequences of weighted means. Stochastics 89(2), 469–484 (2017). MR3590430. https://doi.org/10.1080/17442508.2016.1248968
 Pacchiarotti, B.: Large deviations for generalized conditioned Gaussian processes and their bridges. Probab. Math. Stat. 39(1), 159–181 (2019). MR3964390. https://doi.org/10.19195/0208-4147.39.1.11
 Pfanzagl, J.: On the existence of regular conditional probabilities. Z. Wahrscheinlichkeitstheor. Verw. Geb. 11, 244–256 (1969). MR0248892. https://doi.org/10.1007/BF00536383
 Shiryaev, A.: Probability, 2nd edn. Graduate Texts in Mathematics, vol. 95. Springer, New York (1996). MR1368405. https://doi.org/10.1007/978-1-4757-2539-1
 Sottinen, T., Viitasaari, L.: Prediction law of mixed Gaussian Volterra processes. Stat. Probab. Lett. 156, 108594 (2020). MR3997195. https://doi.org/10.1016/j.spl.2019.108594
 Sottinen, T., Yazigi, A.: Generalized Gaussian bridges. Stoch. Process. Appl. 124(9), 3084–3105 (2014). MR3217434. https://doi.org/10.1016/j.spa.2014.04.002
 Yazigi, A.: Representation of self-similar Gaussian processes. Stat. Probab. Lett. 99, 94–100 (2015). MR3321501. https://doi.org/10.1016/j.spl.2015.01.012