Cox-Ingersoll-Ross Research Articles

Time Scale Transformation in Bivariate Pearson Diffusions: A Shift from Light to Heavy Tails

Heavy-tailed Pearson diffusions provide a natural alternative to well-known Ornstein–Uhlenbeck and Cox–Ingersoll–Ross processes in applications that require addressing heavy-tailed behavior. In this paper, all three heavy-tailed Pearson diffusions, having inverse gamma, Fisher–Snedecor and Student stationary distributions, are constructed via an absolutely continuous time-change process employed in a specific functional transformation of CIR or OU. Moreover, time-change rates in stochastic clocks are continuous functionals of the CIR process.

Revisiting key mortality rate models: novel findings and application of CIR processes to describe mortality trends

Revisiting key mortality rate models: novel findings and application of CIR processes to describe mortality trends

Application of semiclassical approximation to stochastic differential equations

Abstract A method for calculating the characteristics of stochastic differential equations using semiclassical approximation is proposed. For a stochastic differential equation arising in the study of the pure birth process (Yule process) and the Cox Ingersoll Ross (CIR) model, an analysis of the accuracy of the semiclassical approximation was carried out. This analysis is based on a comparison of approximate values with exact values for the mathematical expectation of a solution to the equation.

An adaptive positive preserving numerical scheme based on splitting method for the solution of the CIR model

This paper aims to investigate an adaptive numerical method based on a splitting scheme for the Cox-Ingersoll-Ross (CIR) model. The main challenge associated with numerically simulating the CIR process lies in the fact that most existing numerical methods fail to uphold the positive nature of the solution. Within this article, we present an innovative adaptive splitting scheme. Due to the existence of a square root in the CIR model, the step size is adaptively selected to ensure that, at each step, the value under the square-root does not fall under a given positive level and it is bounded. Moreover, an alternate numerical method is employed if the chosen step size becomes excessively small or the solution derived from the splitting scheme turns negative. This alternative approach, characterized by convergence and positivity preservation, is called the “backstop method”.Furthermore, we prove the proposed adaptive splitting method ensures the positivity of solutions in the sense that it would be possible to find an interval such that for all stepsizes belong, the probability of using the backstop method can be small. Therefore, the proposed adaptive splitting scheme avoids using the backstop method with arbitrarily high probability. We prove the convergence of the scheme and analyze the convergence rate. Finally, we demonstrate the applicability of the scheme through some numerical simulations, thereby corroborating our theoretical findings.

Optimal strategy in CIR model and modified constant elasticity of variance

Optimal strategy in CIR model and modified constant elasticity of variance

A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.

On testing the changes in trends of stock market index and rates

Calibration of interest rate models benefits from grouping data to homogenous classes. Such an approach is typical in many financial time series. Preliminaries have been developed for Cox–Ingersoll–Ross models but this issue remains an open problem for many more realistic interest rate models. Here we develop such a strategy for general class interest rate and classes are based on p-value thresholds for testing for normality and gamma distributions. We use as the benchmark financial series of Chilean stock market index IPSA (Indice de precios selectivo de acciones) and its log-returns. We also study the relationship between interest rate and the market returns represented by the IPSA indicator, with positive correlation in some lags which reveals some interesting facts in the contrary to the conventional theory.

A Stochastically Correlated Bivariate Square-Root Model

We introduce a novel stochastically correlated two-factor (i.e., bivariate) diffusion process under the square-root format, for which we analytically obtain the corresponding solutions for the conditional moment-generating functions and conditional characteristic functions. Such solutions recover verbatim those of the uncorrelated case which encompasses a range of processes similar to those produced by a bivariate square-root process in which entries are correlated in the standard way, that is, via a constant correlation coefficient. Note that closed-form solutions for the conditional characteristic and moment-generating functions are not available for the latter. We focus on the financial scenario of obtaining closed-form expressions for the exact price of a zero-coupon bond and Asian option prices using a Fourier cosine series method.

Volterra square-root process: Stationarity and regularity of the law

Volterra square-root process: Stationarity and regularity of the law

Modeling financial leasing by optimal stopping approach

Leasing valuation is a topic that has aroused considerable interest in business circles. This paper examines leasing from the point of view of the lessor who can decide to leave the contract due to default. We analyze in introducing a model in which the lessor decides whether or not to terminate the contract at a given point in time, comparing it with the cost of capital of alternative investments. The proposed model is stochastic, and it is strongly based on correlated random walks, making it more adaptable to real-world circumstances. Furthermore, we propose a recombinant binomial tree based on correlated random walks, performing numerical simulations starting from CIR and Vasicek models. We will point out that as the rate of cost of capital of an alternative investment increases, the optimal boundary curve decreases, so the lessor leaves, while as the past interest rates increases, the curve rises and the lessor will have a concrete interest in maintaining the contract.

Pricing fixed income derivatives under a three-factor CIR model with unspanned stochastic volatility

Pricing fixed income derivatives under a three-factor CIR model with unspanned stochastic volatility

Low-dimensional Cox-Ingersoll-Ross process

The present paper investigates Cox-Ingersoll-Ross (CIR) processes of dimension less than 1, with a focus on obtaining an equation of a new type including local times for the square root of the CIR process. To derive this equation, we utilize the fact that non-negative diffusion processes can be obtained by the transformation of time and scale of a certain reflected Brownian motion. The equation mentioned above turns out to contain a term characterized by the local time of the corresponding reflected Brownian motion. Additionally, we establish a new connection between low-dimensional CIR processes and reflected Ornstein-Uhlenbeck (ROU) processes, providing a new representation of Skorokhod reflection functions.

The Trapezoidal Numerical Method for the CIR Model Driven by Standard Brownian Motion

The Trapezoidal Numerical Method for the CIR Model Driven by Standard Brownian Motion

Some remarks on the links between the VIR and Gompertz diffusion models and the links between the CIR and Rayleigh diffusion models

The main purpose of this work is to establish new links between the stochastic Cox-Ingersoll-Ross (CIR) model and the stochastic Rayleigh diffusion process (SRDP) and the links between the Vasicek Interest Rate (VIR) process and the stochastic Gompertz diffusion process (SGDP). These links focus on elementary stochastic calculus and Itô’s calculus. Firstly, we prove that the square root of the CIR model is a SRDP. Secondly, we prove that the square of the SRDP is a CIR model. Thirdly, we prove that the exponential of the VIR model is a SGDP. Finally, we prove that the logarithm of the SGDP is a VIR model. New computations of the probability transition density function (PTDF) and the trend functions of the processes have quite simple formulations.

Research on option pricing of Tesla based on Black-Scholes-Merton, Binomial Tree, and Cox-Ingersoll-Ross Models

This study examines the predictive accuracy of three option pricing models—Black-Scholes-Merton (BSM), Binomial Tree, and Cox-Ingersoll-Ross (CIR)—using Tesla Inc's real data. The research surveys the evolution of option pricing models and their significance in risk management and speculation. By applying these models to Tesla's volatile stock, the paper compares their predictions against actual prices. Results indicate that while outcome yielded from all three models resembles the real price to a reasonable extent, the Binomial Tree model's superior performance offers closer mean values and correlations with real prices. This research advances the practical understanding of option pricing, contributing valuable insights for traders and investors facing dynamic market conditions.

COMPARISON OF PROJECTED UNIT CREDIT AND ENTRY AGE NORMAL METHODS IN PENSION FUND VASICEK AND COX-INGERSOLL-ROSS MODELS

The pension program is one of the pension fund's efforts to anticipate the risks that will be experienced by participants in old age. Actuarial calculations help to determine the benefits that participants will receive by considering life chances, interest rates, age when becoming a participant, and normal retirement age. This study aims to determine normal contributions and actuarial liabilities with the Projected Unit Credit and Entry Age Normal methods using stochastic interest rates, namely Vasicek and Cox-Ingersoll-Ross (CIR). The data used in this study are civil servants who work at the Natural Resources Management Office, Bulukumba Regency. The results of the calculation analysis showed that normal cost using the Projected Unit Credit (PUC) method with the Vasicek and Cox-Ingersoll-Ross (CIR) model interest rates increased as the length of service increased, and at the end of the working period the Cox-Ingersoll-Ross (CIR) model interest rate reached Rp14.773.176,- which was higher than Vasicek by Rp3.849.898,-. The results of the calculation of normal cost using the Entry Age Normal (EAN) method with the Vasicek model increase in the period 0-20 years of service, then decrease towards the contribution value at the beginning of the service period of Rp1.499.725,-. At the beginning of the working year, the normal cost using the Entry Age Normal method with the Cox-Ingersoll-Ross (CIR) model interest rate is Rp7.581.593,- then decreases for 24 years of service to Rp5.849.854,- after which it increases again towards the initial contribution value of the working year. The results of the calculation of actuarial liabilities show an increase as the length of service increases, for the Entry Age Normal (EAN) and Projected Unit Credit (PUC) methods with the Cox-Ingersoll-Ross (CIR) interest rate model at the end of the service period, it is found that both are the same value, namely Rp443.195.285,-. By using the Vasicek interest rate model for both methods, the same result is obtained at the end of the service period of Rp115.496.951,-. This shows that the actuarial liabilities for both methods used are affected by interest rates, and the Cox-Ingersoll-Ross (CIR) model is higher than Vasicek

APPRAISAL OF STOCHASTIC PRICING FORMULA FOR HYBRID EQUITY WARRANTS

An evaluator of warrant models is essential for enhancing the current procedures used in the country's stock market and economy. This would provide additional insight for investors to make informed investment decisions and help to better understand the dynamics of warrant pricing in Malaysia. It has been established that the conventional process for valuing equity warrants using call option pricing models, like the Black-Scholes model, has numerous flaws, including the presumption of constant volatility and interest rates. As a matter of fact, existing alternative models were more concerned with providing techniques for pricing than with conducting empirical tests of warrant pricing models. Consequently, it is crucial to have a mathematical model that takes into account stochastic volatility and stochastic interest rates when pricing and analyzing equity warrants. Recent research by Roslan, Ibrahim, Jameel, and Ibrahim (2020) and Roslan, Ibrahim, Karim (2020) examined the pricing of hybrid equity warrants in the presence of stochastic factors. There was no comparative analysis conducted in these studies to ascertain the relative efficacy of the two methods used. This study's main goal is to empirically investigate how well equity warrant pricing models perform in the Malaysian market. This evaluation will take into account the impact of stochastic interest rates, using the Cox-Ingersoll-Ross (CIR) stochastic interest rate model, and stochastic volatility, using the Heston stochastic volatility model. To perform the analysis, the study will make use of a number of mathematical techniques, such as partial differential equations, Fourier transform approaches, and the change of measure method. The research methodology is divided into three main phases. It is anticipated that the research's findings will provide new perspectives that go beyond the domain of warrant pricing, creating chances for more widespread applications in fields like foreign exchange, insurance, and mortgage contracts.

Projection and Contraction Method for Pricing American Bond Options

In this paper, an effective numerical method is proposed for a linear complementarity problem (LCP) arising in the valuation of American bond options under the Cox–Ingersoll–Ross (CIR) model. Firstly, a variable substitution is used to simplify the linear complementary model. Secondly, the finite difference method is adopted to discretize the simplified model, and an equivalent variational form is obtained. Based on the positive definiteness of the discretized matrix, a projection and contraction method (PCM) is adopted for the resulting discretized variational problem. Finally, numerical experiments highlight the effectiveness and performance of the proposed algorithm.

Foreign exchange option pricing under the 4/2 stochastic volatility model with CIR interest rates.

. Foreign exchange (FX) options are studied in a new stochastic volatility model (the 4/2 stochastic volatility model), which includes, as special instances, the Heston model and the 3/2 model, for the exchange rate in combination with the Cox-Ingersoll-Ross (CIR) dynamics for the domestic and foreign stochastic interest rates. We allow the correlation between the instantaneous volatility and the dynamics of the exchange rate, whereas the interest rates in both domestic and foreign markets are assumed to be independent of the dynamics of the exchange rate. We provide a semi-analytical formula for the price of the European FX call option via Fourier inversion. Through the change of measure technique, we derived explicit expressions for conditional characteristic functions. Our model is computationally tractable, thereby the pricing procedure is accurate and illustrates more efficiency in comparison to the Monte Carlo simulation method. Finally, we perform calibrations on market data, demonstrating that our model fits the implied volatilities well and outperforms the Heston model with CIR interest rates.

Lee–Carter model: assessing the potential to capture gender-related mortality dynamics

AbstractWe investigate the ability of the Lee–Carter model to effectively estimate the gender gap ratio (GGR), the ratio between the male death rates over the female ones, by using a Cox–Ingersoll–Ross (CIR) process to provide a stochastic representation of the fitting errors. The novelty consists in the fact that we use the parameters characterizing the CIR process itself (long-term mean and volatility), in their intrinsic meanings, as quantitative measures of the long-term fitting attitude of the Lee–Carter model and synthetic indicators of the overall risk of this model. The analysis encompasses 25 European countries, to provide evidence-based indications about the goodness of fit of the Lee–Carter model in describing the GGR evolution. We highlight some stylized facts, namely systematic evidence about the fitting bias and the risk of the model across ages and countries. Furthermore, we perform a functional cluster analysis, allowing to capture similarities in the fitting performance of the Lee–Carter model among countries.