On the Cumulants of the First Passage Time of the Inhomogeneous Geometric Brownian Motion

We consider the problem of the first passage time T of an inhomogeneous geometric Brownian motion through a constant threshold, for which only limited results are available in the literature. In the case of a strong positive drift, we get an approximation of the cumulants of T of any order using the algebra of formal power series applied to an asymptotic expansion of its Laplace transform. The interest in the cumulants is due to their connection with moments and the accounting of some statistical properties of the density of T like skewness and kurtosis. Some case studies coming from neuronal modeling with reversal potential and mean reversion models of financial markets show the goodness of the approximation of the first moment of T. However hints on the evaluation of higher order moments are also given, together with considerations on the numerical performance of the method.

[1]  Román Baravalle,et al.  Higher-Order Cumulants Drive Neuronal Activity Patterns, Inducing UP-DOWN States in Neural Populations , 2020, Entropy.

[2]  Eduardo S. Schwartz,et al.  A continuous time approach to the pricing of bonds , 1979 .

[3]  A. Buonocore,et al.  Closed-form solutions for the first-passage-time problem and neuronal modeling , 2015 .

[4]  P. Lánský,et al.  On two diffusion neuronal models with multiplicative noise: The mean first-passage time properties. , 2018, Chaos.

[5]  L. Sacerdote,et al.  Stochastic Integrate and Fire Models: a review on mathematical methods and their applications , 2011, 1101.5539.

[6]  Eric Shea-Brown,et al.  From the statistics of connectivity to the statistics of spike times in neuronal networks , 2017, Current Opinion in Neurobiology.

[7]  Fredrik Johansson,et al.  Computing Hypergeometric Functions Rigorously , 2016, ACM Trans. Math. Softw..

[8]  Bo Zhao Inhomogeneous Geometric Brownian Motion , 2009 .

[9]  Virginia Giorno,et al.  AN OUTLINE OF THEORETICAL AND ALGORITHMIC APPROACHES TO FIRST PASSAGE TIME PROBLEMS WITH APPLICATIONS TO BIOLOGICAL MODELING , 1999 .

[10]  P. Lánský,et al.  Statistics of inverse interspike intervals: The instantaneous firing rate revisited. , 2018, Chaos.

[11]  E. Nardo Symbolic Calculus in Mathematical Statistics: A Review , 2015, 1512.08379.

[12]  P Lánský,et al.  Synaptic transmission in a diffusion model for neural activity. , 1994, Journal of theoretical biology.

[13]  Rimjhim Tomar,et al.  Review: Methods of firing rate estimation , 2019, Biosyst..

[14]  S. Grün,et al.  Higher-Order Correlations and Cumulants , 2010 .

[15]  Virginia Giorno,et al.  ON THE ASYMPTOTIC BEHAVIOUR OF FIRST- PASSAGE-TIME DENSITIES FOR ONE-DIMENSIONAL DIFFUSION PROCESSES AND VARYING BOUNDARIES , 1990 .

[16]  Campbell R. Harvey,et al.  An Empirical Comparison of Alternative Models of the Short-Term Interest Rate , 1992 .

[17]  M. Insley A Real Options Approach to the Valuation of a Forestry Investment , 2002 .

[18]  Henrik Rasmussen,et al.  National Centre of Competence in Research Financial Valuation and Risk Management Working Paper No . 107 An Option Pricing Formula for the GARCH Diffusion Model , 2004 .

[19]  A. Swishchuk Explicit Option Pricing Formula for a Mean-Reverting Asset in Energy Market , 2008 .

[20]  Ward Whitt,et al.  An operational calculus for probability distributions via Laplace transforms , 1996, Advances in Applied Probability.

[21]  L. Ricciardi,et al.  First-passage-time densities for time-non-homogeneous diffusion processes , 1997, Journal of Applied Probability.

[22]  Elvira Di Nardo,et al.  A cumulant approach for the first-passage-time problem of the Feller square-root process , 2020, Appl. Math. Comput..

[23]  Wulfram Gerstner,et al.  Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise. , 2006, Chaos.

[24]  J. J. Serrano-Pérez,et al.  Some Notes about Inference for the Lognormal Diffusion Process with Exogenous Factors , 2018 .

[25]  Runhuan Feng,et al.  Geometric Brownian motion with affine drift and its time-integral , 2021, Appl. Math. Comput..

[26]  Valeri Y. Kontorovich,et al.  First-passage time statistics of Markov gamma processes , 2013, J. Frankl. Inst..

[27]  M. Sørensen,et al.  The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes , 2007 .