Analytically Computing the Moments of a Conic Combination of Independent Noncentral Chi-Square Random Variables and Its Application for the Extended Cox–Ingersoll–Ross Process with Time-Varying Dimension - Sanae Rujivan; Athinan Sutchada; Kittisak Chumpong; Napat Rujeerapaiboon (Mathematics, 2023)
This paper focuses mainly on the problem of computing the <semantics>γth</semantics>, <semantics>γ>0</semantics>, moment of a random variable <semantics>Yn:=<msubsup>∑i=1n</msubsup>αiXi</semantics> in which the <semantics>αi</semantics>’s are positive real numbers and the <semantics>Xi</semantics>’s are independent and distributed according to noncentral chi-square distributions. Finding an analytical approach for solving such a problem has remained a challenge due to the lack of understanding of the probability distribution of <semantics>Yn</semantics>, especially when not all <semantics>αi</semantics>’s are equal. We analytically solve this problem by showing that the <semantics>γth</semantics> moment of <semantics>Yn</semantics> can be expressed in terms of generalized hypergeometric functions. Additionally, we extend our result to computing the <semantics>γth</semantics> moment of <semantics>Yn</semantics> when <semantics>Xi</semantics> is a combination of statistically independent <semantics><msubsup>Zi2</msubsup></semantics> and <semantics>Gi</semantics> in which the <semantics>Zi</semantics>’s are distributed according to normal or Maxwell–Boltzmann distributions and the <semantics>Gi</semantics>’s are distributed according to gamma, Erlang, or exponential distributions. Our paper has an immediate application in interest rate modeling, where we can explicitly provide the exact transition probability density function of the extended Cox–Ingersoll–Ross (ECIR) process with time-varying dimension as well as the corresponding <semantics>γth</semantics> conditional moment. Finally, we conduct Monte Carlo simulations to demonstrate the accuracy and efficiency of our explicit formulas through several numerical tests.