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Innocent Uchenna Amadi

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Innocent Uchenna Amadi is a registered researcher in their academic field.

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5 research works linked to this profile

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stochastic processes; bank stress testing; geometric Brownian motion; exponential decay; stock prices · 2026 · Ktrend – Nigerian Journal of Mathematical and Computational Sciences

Stress Testing Nigerian Banks: An Exponential-Decay Geometric Brownian Motion Model

This paper applies an exponential-decay geometric Brownian motion (GBM) model to illustrate how shocks and sustained downward pressure may affect Nigerian bank stock prices. The standard GBM drift is adjusted from $\mu$ to $\mu-k$, where $k>0$ is the decay rate. Brownian-motion paths illustrate the source of uncertainty, while simulated stock-price paths demonstrate how assets with identical initial values can diverge under volatility. Sensitivity analysis shows that the relationship between $k$ and $\mu$ determines the behavior of the expected price: the mean grows when $\mu>k$, remains constant when $\mu=k$, and declines exponentially when $\mu\mu$, the mean-price half-life is $\ln(2)/(k-\mu)$. The model therefore offers a transparent scenario-generation tool for regulators and risk managers. It is an illustrative stress-testing model rather than an empirically calibrated model of any named Nigerian bank.

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stochastic fluctuations; bank valuation; geometric Brownian motion; inflation; long-term growth; stock prices. · 2026 · African Journal of Mathematics, Statistics and Computer Science

Solution of a Linear Stochastic Differential Equation for the Impact of Inflation and Long-Term Growth Trends on Bank Valuation

This study investigates the dynamics of Nigerian bank stock prices using an exponential-growth geometric Brownian motion framework. The model extends the classical geometric Brownian motion (GBM) by incorporating a growth-adjustment parameter $k$ in the drift term, allowing it to capture both deterministic long-term growth and stochastic fluctuations. Three Brownian-motion paths are first presented to illustrate the underlying randomness, followed by sample price paths demonstrating how the same growth rate can produce both upward and downward trajectories depending on the shock realizations. A sensitivity analysis is then conducted to assess the effect of $k$ on the simulated paths. The results show that higher values of $k$ amplify exponential growth or decay and increase dispersion in absolute valuation. This framework provides insight into how long-term growth expectations interact with short-term volatility in the Nigerian banking sector.

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Crank-Nicolson, Black-Scholes, Put Option, Finite Difference, Volatility, NonCentral F, Numerical PDE · 2026 · Ktrend – Nigerian Journal of Mathematical and Computational Sciences

Computational Accuracy of Crank-Nicolson Method for European Put Option Pricing Under Varying Volatility

Option pricing models are central to financial risk management and derivatives trading. The Black-Scholes-Merton (BSM) model provides closed-form solutions but relies on constant parameters, while numerical methods such as the Crank-Nicolson (CN) finite difference scheme offer flexibility for complex payoffs. This study investigates the computational accuracy of the Crank-Nicolson method in pricing European put options relative to the Black-Scholes exact values under varying volatility levels. Using $T=1$ year, $K=100$, $r=0.2$, and two initial stock prices $S_0=40$ and $S_0=50$, put prices were computed for volatilities ranging from $0.25$ to $0.95$. Also, figure results were obtained to validate the performances of BS and CN respectively. Four statistical error metrics: MAE, RMSE, MAPE, and Max Error were employed to quantify deviation. Present value variations across initial stock prices were also analyzed. Furthermore, Non-Central F Analysis was conducted to test the statistical significance of differences in mean put prices across stock price levels. Results show that CN closely approximates BSM at low to moderate volatilities $V \leq 0.55$ with $\mathrm{MAPE}0.65$, with Max Error of $7.19$. Present value differences decrease as $S_0$ increases, and the effect of $S_0$ on put prices is statistically significant with $F_{\mathrm{CAL}}=62.97$, $p

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Research · 2026 · Ktrend - International Journal of Mathematics and Statistics (IJMS)

Volatility Sensitivity of Deep In-The-Money European Call Options: A Black-Scholes and Non-Central F Analysis

This study investigates the sensitivity of European call option prices to volatility under the Black-Scholes framework for deep in-the-money contracts. Using the stock quantity parameter values to compute, the results show a monotonic and convex increase in call value with volatility, with total increases of $2.33 and $1.84 for S? = 60 and S? = 70, respectively. A Fisher non-central F analysis confirms that volatility explains a statistically significant proportion of price variation, with a strong effect size after controlling for the level of the initial stock price. The findings underscore the critical role of vega for deep in-the-money options and the importance of accurate volatility estimation in high-rate environments.

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CTMCs; Stocks; Equity Prices; Stochastic Analysis; NGX; Regime-Switching · 2026 · African Journal of Mathematics, Statistics and Computer Science

Regime-Switching Dynamics of Nigerian Group Exchange Cement Equities: A Continuous-Time Markov Chain Analysis

We model daily price dynamics of BUA Cement and BZU Cement on the Nigerian Group Exchange (NGX) as three-state Continuous-Time Markov Chains (CTMCs) representing Low, Mid, and High regimes. Using nine months of data, estimated generator matrices show that BZU exhibits 2.4 times higher total transition intensity than BUA, with expected holding times of 1.5 months versus 3.0 months in bear states. Stationary distributions reveal that BUA spends 30% of time in the absorbing bull state, while BZU is uniformly distributed across regimes, indicating higher mean reversion. The CTMC results highlight BUA's relative stability compared with BZU's higher regime volatility. One-month transition forecasts show that BUA remains in the High regime with probability 100%, whereas BZU has only 51.3% probability of remaining High and a 28.3% chance of dropping directly to Low. These findings indicate that BUA is trend-persistent, while BZU is regime-unstable and more exposed to short-term downside switching risk.