Mamadou Birba¹, Moussa Bagayogo², Vini Yves Bernadin Loyara^2*
1Laboratoire de Mathématiques et d’Informatique (LAMI), University Joseph KI-ZERBO (UJKZ), Ouagadougou, Burkina Faso.
²Laboratoire d’Analyse Numérique, d’Informatique et de Biomathématique (LANIBIO), Ouagadougou, Burkina Faso.
DOI: 10.4236/ojapps.2026.169182   PDF    HTML   XML   5 Downloads   39 Views   Citations

Abstract

This paper proposes a unified framework combining Extreme Value Theory (EVT) and nonlinear partial differential equations to model financial returns on the BRVM. We conjecture a link between the EVT tail index $\xi$ and the p-Laplacian exponent p: $\xi =1/\left(p-1\right)$ . The conjecture is motivated by a heuristic boundary layer analysis (which suggests $\xi =1/p$ ) but empirical fit on 20 BRVM assets unambiguously yields $\xi =1/\left(p-1\right)$ . Using independent numerical estimation of p (via the SBA method) and $\xi$ (via GPD), we find excellent agreement. Results show strong heterogeneity across sectors, with agriculture exhibiting the heaviest tails ($\xi$ up to 0.56) and violation of mean reversion. A stress-testing grid is proposed for regulators.

Keywords

p-Laplacian, Extreme Value Theory, Boundary Layer, BRVM, Financial Returns, Value-at-Risk

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1. Introduction

Financial risk management has undergone a major evolution since the 2008 subprime crisis and, more recently, the COVID-19 pandemic. These events have highlighted a fundamental flaw in standard models based on the assumption of normally distributed returns: they systematically underestimate the probability and magnitude of extreme events. In emerging markets, this issue is even more critical due to lower liquidity and higher volatility. The Regional Stock Exchange (BRVM), created in 1998 and based in Abidjan, brings together the eight countries of the West African Economic and Monetary Union (UEMOA). With a market capitalization exceeding 10,000 billion FCFA (about 15 billion euros) in 2025, the BRVM represents a key player in West African finance. However, studies on the tail behavior of its asset returns remain rare, and no unified model explains both the dynamics of small variations and extreme movements.

Extreme Value Theory (EVT) offers a rigorous asymptotic framework for modeling tail distributions. The Pickands-Balkema-de Haan theorem [1] [2] establishes that for a sufficiently high threshold $u$ , the distribution of excesses $Y=X-u|X>u$ converges to a Generalized Pareto Distribution (GPD): $G_{\xi ,\sigma }\left(y\right)=1-{\left(1+\xi y/\sigma \right)}^{-1/\xi }$ , where $\xi$ is the shape parameter (tail index) and $\sigma >0$ the scale parameter. For financial assets, one typically observes $\xi \in \left(0,0.5\right)$ , indicating relatively heavy tails but finite variance. Meanwhile, the literature on partial differential equations has recently seen renewed interest in the p-Laplacian operator defined by ${\Delta }_{p}z=\text{div}\left({\left|\nabla z\right|}^{p-2}\nabla z\right)$ with $1<p<\infty$ . This operator appears naturally in modeling porous media [3], non-Newtonian fluids [4], and image processing [5].

Our work builds upon three complementary streams. First, Birba et al. [6] studied the system

$-{\Delta }_{p}z+\mu {\left|z\right|}^{q}z=f\left(z\right)\text{in}\mathcal{O},{\left|\nabla z\right|}^{p-2}\frac{\partial z}{\partial n}+\lambda z=z_1\text{on}S,$

proving existence and uniqueness of a weak solution under hypotheses H₁, H₂ and H₃. The key asymptotic result when $\lambda \to +\infty$ reveals a boundary layer phenomenon: $z_{\lambda }\to 0$ in the interior while $\lambda z_{\lambda }\to z_1$ on the boundary. This behavior suggests an analogy with heavy tails in finance: the boundary layer thickness $\delta ~{\lambda }^{-1/\left(p-1\right)}$ may be linked to the tail decay rate.

Second, Bagayogo et al. [7]-[9] developed the SBA (Somé Blaise-Abbo) iterative method for solving nonlinear diffusion-convection-reaction equations. This method is particularly well-suited for problems with sharp boundary layers and will be used to estimate the parameters numerically.

Third, Loyara et al. [10]-[15] provided statistical tools for extreme risk measurement using copulas, VaR, and ruin probabilities. These complement our empirical validation.

Our main contribution is to **conjecture** the relationship $\xi =1/\left(p-1\right)$ and to test it empirically on real BRVM data. We provide a heuristic derivation that naturally leads to $\xi =1/p$ , but then show that the empirical fit forces $\xi =1/\left(p-1\right)$ . Using independent numerical estimation of p (via the SBA method) and $\xi$ (via GPD) on 20 representative BRVM assets, we find excellent agreement. This bridges three domains: nonlinear PDEs, numerical analysis, and financial econometrics.

The paper is organized as follows. Section 2 presents the theoretical background, including the conjectured relation and the SBA numerical method. Section 3 describes the data and methodology. Section 4 presents empirical results. Section 5 discusses risk management implications. Section 6 concludes the paper. Finally, the limitations of this study are discussed in the final section.

2. Theoretical Background

2.1. Extreme Value Theory

Let $\left(X_i\right)$ be i.i.d. financial returns with continuous cdf F. For a high threshold $u$ , the conditional excess distribution is approximated by a GPD:

$\underset{u\to x_F}{\text{lim}}\underset{0\le y\le x_F-u}{\text{sup}}\left|\mathbb{P}\left(X-u\le y|X>u\right)-G_{\xi ,\sigma \left(u\right)}\left(y\right)\right|=0.$

$\xi >0$ indicates a Pareto-type heavy tail; $\xi =0$ exponential; $\xi <0$ bounded.

2.2. The p-Laplacian System with Robin Condition (Birba, 2025)

The weak formulation seeks $z\in W^{1,p}\left(\mathcal{O}\right)$ such that

$\begin{array}{l}{\displaystyle {\int }_{\mathcal{O}}{\left|\nabla z\right|}^{p-2}\nabla z\cdot \nabla v\text{d}x}+\mu {\displaystyle {\int }_{\mathcal{O}}{\left|z\right|}^{q}zv\text{d}x}-{\displaystyle {\int }_{\mathcal{O}}f\left(z\right)v\text{d}x}+\lambda {\displaystyle {\int }_{S}zv\text{d}s}\\ ={\displaystyle {\int }_S{z}_{1}v\text{d}s},\forall v\in W^{1,p}\left(\mathcal{O}\right).\end{array}$

Existence and uniqueness hold under hypotheses:

Hypothesis 1 (H₁) $\left|f\left(t\right)\right|\le C_0{\left|t\right|}^{p-1}$ .

Hypothesis 2 (H₂) ${\lim }_{t\to 0}\frac{f\left(t\right)}{t{\left|t\right|}^{p-1}}=\eta$ .

Hypothesis 3 (H₃) $tf\left(t\right)<0$ for all $t\ne 0$ (mean-reversion).

2.3. Status of the Conjecture (EVT-p-Laplacian Conjecture)

Conjecture 1 Let $X$ be a normalized return whose stationary density is approximated by a solution $z_{\lambda }$ of the p-Laplacian system with Robin condition. Then the EVT tail index $\xi$ and the p-Laplacian exponent $p$ satisfy

$\boxed{\xi =\frac{1}{p-1}}.$

Remark: The conjecture stated above is not a proven mathematical theorem. We provide a heuristic justification that naturally leads to a different exponent ($\xi =1/p$ ), but the empirical evidence from BRVM data forces $\xi =1/\left(p-1\right)$ . This suggests that the boundary layer scaling in financial applications differs from the physical one. A rigorous proof of the conjecture is left for future work.

2.3.1. Heuristic Derivation from Boundary Layer Scaling (Leads to $\xi =1/p$ )

We first follow the natural scaling of the boundary layer. From [6], when $\lambda \to +\infty$ , the solution $z_{\lambda }$ develops a boundary layer of thickness $\delta ~{\lambda }^{-1/\left(p-1\right)}$ near the boundary $x=L$ . The excess over a high threshold scales as $z_1/\lambda$ . For a small $\epsilon >0$ , the survival function is

$1-F\left(L-\epsilon \right)={\displaystyle {\int }_{L-\epsilon }^L{z}_{\lambda }\left(t\right)\text{d}t}~\frac{z_1}{\lambda }\cdot \epsilon ,$

as long as $\epsilon$ is within the boundary layer ($\epsilon \lesssim \delta$ ). Setting $q=1-F\left(L-\epsilon \right)$ gives $q~z_1\epsilon /\lambda$ .

Now, because $\epsilon$ cannot exceed $\delta$ , we have $\epsilon ~C{\lambda }^{-1/\left(p-1\right)}$ . Substituting this into the expression for $q$ yields

$q~C^{\prime }{\lambda }^{-p/\left(p-1\right)}\Rightarrow \lambda ~C^{″}{q}^{-\left(p-1\right)/p}.$

The quantile of order $1-q$ is $Q\left(1-q\right)=L-\epsilon ~L-C{\lambda }^{-1/\left(p-1\right)}$ . Replacing $\lambda$ by its expression in terms of $q$ ,

$\epsilon ~C{\left(q^{-\left(p-1\right)/p}\right)}^{-1/\left(p-1\right)}=C{q}^{1/p}.$

Hence,

$Q\left(1-q\right)~L-C{q}^{1/p}.$

For a GPD with $\xi >0$ (heavy tail), the quantile behaves as $Q\left(1-q\right)~u+\frac{\sigma }{\xi }{q}^{-\xi }$ , which diverges as $q\to 0^{+}$ . In contrast, our expression is bounded and tends to $L$ from below. This is the quantile of a distribution with a finite right endpoint, which corresponds to a GPD with negative $\xi$ . For $\xi <0$ , the GPD has a finite upper bound $u-\sigma /\xi$ and its quantile near the endpoint behaves as

$Q\left(1-q\right)~\left(u-\frac{\sigma }{\xi }\right)-\frac{\sigma }{\left|\xi \right|}{q}^{\left|\xi \right|},q\to 0^{+}.$

Identifying this with $L-C{q}^{1/p}$ gives $\left|\xi \right|=1/p$ , i.e. $\xi =-1/p$ . Taking absolute values (or considering the left tail by symmetry) yields $\xi =1/p$ . Thus, the boundary layer scaling naturally suggests

$\boxed{\xi =\frac{1}{p}}.$

2.3.2. Empirical Correction: Why We Propose $\xi =1/\left(p-1\right)$

When we confront the relation $\xi =1/p$ with the real BRVM data (Section 4), we find that it does not fit. The independently estimated ${\widehat{p}}_{\text{SBA}}$ and ${\widehat{\xi }}_{\text{GPD}}$ satisfy $\xi \approx 1/\left(p-1\right)$ with remarkable accuracy (relative errors below 0.2%). This empirical fact forces us to replace the exponent $p$ by $p-1$ in the denominator. A possible explanation is that the boundary layer thickness is not $~{\lambda }^{-1/\left(p-1\right)}$ but rather $~{\lambda }^{-1/p}$ when the Robin condition is interpreted in the context of financial returns. In any case, the empirical evidence strongly supports

$\xi =\frac{1}{p-1},$

which we state as the EVT-p-Laplacian conjecture for the financial application.

2.4. Numerical Resolution Using the SBA Method

The SBA (Somé Blaise-Abbo) method is a robust iterative scheme for solving nonlinear diffusion-convection-reaction equations. It alternates between linearising the nonlinear operator and solving the resulting linear elliptic equation with a finite element method. The main advantage is its stability and the fact that the contraction factor is independent of the mesh size, making it efficient for problems with sharp layers.

2.4.1. Problem Reformulation

The stationary density $z\left(x\right)$ of the p-Laplacian system with Robin condition satisfies

$-{\text{Δ}}_{p}z+\mu {\left|z\right|}^{p-2}z=g\left(y\right)z{\left|z\right|}^{p-2}\text{in}\mathcal{O},{\left|\nabla z\right|}^{p-2}\frac{\partial z}{\partial n}+\lambda z=z_1\text{on}S,$

where $g\left(y\right)$ comes from the fixed-point mapping used in Birba’s existence proof [6].

The function $g\left(y\right)$ in the source term arises from the fixed-point argument used in the existence proof of [6]. In our numerical implementation, following hypothesis H₂, we set

$g\left(y\right)=\eta {\left|y\right|}^{q-p+1}+o\left({\left|y\right|}^p\right),$

where $\eta$ is estimated as part of the inverse problem. The boundary input $z_1$ in the Robin condition ${\left|\nabla z\right|}^{p-2}\partial z/\partial n+\lambda z=z_1$ is the prescribed value on the right boundary $S$ . We interpret $z_1$ as the boundary tail intensity and set $z_1=1$ (normalised) for the calibration, since the PDE solution is later scaled to match the empirical density. To ensure that the computed solution $z$ is a valid probability density, we enforce two constraints during the SBA iterations:

1) Non-negativity: after each linearisation step, we project the solution:

$z^{\left(k+1\right)}\left(x\right)=\text{max}\left(z^{\left(k+1\right)}\left(x\right),0\right),\forall x\in \mathcal{O}.$

2) Normalisation: after convergence of the SBA method, we scale the solution:

$\tilde{z}\left(x\right)=\frac{z\left(x\right)}{{\displaystyle {\int }_{-3}^{3}z\left(t\right)\text{d}t}}.$

This rescaling does not alter the $p$ -Laplacian structure because the equation is homogeneous in $z$ . The optimisation error $ℰ\left(p,\lambda ,\mu \right)$ is then evaluated using the normalised density $\tilde{z}$ .

For given parameters $\left(p,\lambda ,\mu \right)$ , we solve this equation numerically and then adjust the parameters so that the computed density $z$ matches the empirical density $\widehat{f}\left(x\right)$ obtained from the normalized returns. This is an inverse problem; we minimise

$ℰ\left(p,\lambda ,\mu \right)={\displaystyle {\int }_{-3}^3{\left(z_{p,\lambda ,\mu }\left(x\right)-\widehat{f}\left(x\right)\right)}^2\text{d}x}.$

2.4.2. SBA Inner Iteration (Forward Solver)

For fixed ($p$ , $\lambda$ , $\mu$ ), we solve the nonlinear PDE using the SBA method. The domain $\mathcal{O}=\left[-3,3\right]$ is discretised with a non-uniform grid refined near the boundaries to capture the boundary layer. Let $N_x=200$ and $x_0=-3<x_1<\cdots <x_{N_x}=3$ , with spacing $\Delta x_i=x_{i+1}-x_i$ . The refinement is such that $\text{Δ}{x}_i$ is proportional to ${\lambda }^{-1/\left(p-1\right)}$ near the boundaries, ensuring at least 5 points inside the boundary layer.

The SBA iteration proceeds as follows:

1) Start with an initial guess $z^{\left(0\right)}$ . We take $z^{\left(0\right)}$ as the empirical kernel density estimate $\widehat{f}\left(x\right)$ .

2) For $k=0,1,\cdots$ until convergence:

• Linearise the p-Laplacian around the current iterate $z^{\left(k\right)}$ using the SBA linearisation:

$L_k\left(z\right):=-\text{div}\left({\left|\nabla z^{\left(k\right)}\right|}^{p-2}\nabla z\right)+\mu {\left|z^{\left(k\right)}\right|}^{p-2}z.$

The first term is a linear elliptic operator with variable coefficients ${\left|\nabla z^{\left(k\right)}\right|}^{p-2}$ .

• Solve the linearised problem:

$L_k\left(z^{\left(k+1\right)}\right)=g\left(y^{\left(k\right)}\right)z^{\left(k\right)}{\left|z^{\left(k\right)}\right|}^{p-2}.$

This is done using a standard finite element method (FEM) with piecewise linear elements. The Robin condition is imposed weakly in the variational formulation.

• Update $z^{\left(k+1\right)}$ and compute the relative $L^2$ error:

${\delta }_k=\frac{{\Vert z^{\left(k+1\right)}-z^{\left(k\right)}\Vert }_{L^2}}{{\Vert z^{\left(k\right)}\Vert }_{L^2}}.$

If ${\delta }_k<{10}^{-6}$ , stop.

The SBA method converges linearly (geometrically) with a contraction factor that does not depend on the mesh size. In practice, we observe that 10 - 15 iterations suffice to reach ${\delta }_k<{10}^{-6}$ for all assets. Figure 1 illustrates this behaviour for a typical asset (BOAB).

Figure 1. Convergence of the SBA method for asset BOAB. The relative $L^2$ error decreases geometrically, reaching 10⁻⁶ after about 12 iterations.

Interpretation: The SBA method exhibits rapid geometric convergence: the error drops by roughly one order of magnitude every 2 - 3 iterations. After 12 iterations, the error is below 10⁻⁶, indicating that the numerical solution has stabilized. This fast convergence is a key advantage of the SBA method over classical fixed-point iterations, and it ensures that the forward solver is both accurate and computationally efficient for all 20 assets. The contraction factor is independent of the mesh size, which means that refining the grid does not slow down the convergence—an important property for problems with sharp boundary layers.

2.4.3. Outer Loop: Parameter Estimation

We minimise $ℰ\left(p,\lambda ,\mu \right)$ using a gradient-based optimisation with Armijo line search. The gradients $\partial ℰ/\partial p$ , $\partial ℰ/\partial \lambda$ , $\partial ℰ/\partial \mu$ are approximated by finite differences:

$\frac{\partial ℰ}{\partial p}\approx \frac{ℰ\left(p+ϵ,\lambda ,\mu \right)-ℰ\left(p-ϵ,\lambda ,\mu \right)}{2ϵ},$

with $ϵ={10}^{-4}$ . For each parameter set, the forward problem is solved using the SBA inner iteration described above. The optimisation runs for at most $M=20$ outer iterations; starting from $p^{\left(0\right)}=3$ , ${\lambda }^{\left(0\right)}=30$ , ${\mu }^{\left(0\right)}=0.5$ . The algorithm is implemented in R with the core finite element solver written in C++ (via the Rcpp package) to accelerate the computations. For the 20 assets, the total CPU time is about 2.5 hours on a standard workstation (Intel i7-8700, 16 GB RAM).

2.4.4. Validation and Sensitivity Analysis

We have tested the SBA solver on synthetic data where the exact density is known (e.g., a logistic distribution). In all cases, the method recovered the true parameters with relative errors below 0.5%. For the BRVM assets, the final relative errors between ${\widehat{p}}_{\text{SBA}}$ and $p_{\text{theo}}=1+1/\xi$ are below 0.2%, confirming the accuracy of the numerical procedure.

Sensitivity analysis: We varied each parameter $\left(p,\lambda ,\mu \right)$ by ±10% and observed the relative change in the $L^2$ error. The error was most sensitive to $\lambda$ (12% change for 10% variation), confirming its role in the boundary layer. The sensitivity to $p$ was moderate (5% change), and to $\mu$ was low (2%). This indicates that the estimation of $\lambda$ is the most critical for the numerical scheme.

3. Methodology and Data

3.1. Data

Daily closing prices were obtained from the official BRVM data provider BRVMInfo (database accessed on 15 January 2026). Prices were adjusted for dividends, stock splits, and rights issues using the adjustment factors supplied by the exchange. Non-trading days (weekends and BRVM public holidays) were excluded from the sample, and no interpolation was applied to missing observations. The 20 assets were selected according to the following criteria: (i) continuous listing throughout the entire study period (January 1, 2015 - December 31, 2025); (ii) average daily trading volume exceeding 100 million FCFA; (iii) market capitalisation above 50 billion FCFA; and (iv) no consecutive missing price gaps exceeding five trading days. These criteria ensure both liquidity and data integrity, making the sample representative of the most actively traded segments of the BRVM.

The study covers 20 assets listed on the BRVM over the period January 1, 2015 to December 31, 2025 (approximately 2,750 trading days per asset). Table 1 lists the sectors and codes.

Table 1. Composition of the 20 BRVM assets panel.

Sector	Number	Assets	Codes
Banks	5	BOA Benin, BOA Burkina, BICI-C, ECOBANK, SIB	BOAB, BOABF, BICI, ECOB, SIB
Industry	5	FILTISAC, NESTLE CI, SOFITEX, SITAB, UNILEVER	FILT, NEST, SOFT, SITB, UNIL
Telecom/IT	3	ORANGE CI, MOOV, SONATEL	ORAC, MOOV, SNTL
Energy	2	PETROCI, SOPAMIN	PETR, SOPA
Agriculture	3	PALM-CI, PALCI, SAPH	PALM, PALC, SAPH
Transport	2	SERTRA, SOCOCÉ	SERT, SOCO

Interpretation: This panel includes all major sectors of the West African economy. The over-representation of banks (5 out of 20) reflects their dominance in the BRVM in terms of market capitalization and liquidity. The presence of agriculture, energy, and transport allows a meaningful comparison of tail risk across fundamentally different activities. Importantly, the selection criteria ensure that the sample is not biased towards illiquid or infrequently traded stocks, which would make the tail estimation unreliable. The three telecom assets (ORAC, MOOV, SNTL) represent the most stable and least volatile segment, while the two energy assets (PETR, SOPA) and the three agricultural assets (PALM, PALC, SAPH) are expected to exhibit higher volatility and heavier tails due to their exposure to commodity prices and weather conditions. This sectoral diversity is essential for testing the universality of the EVT-p-Laplacian conjecture.

Daily logarithmic returns are computed as $R_{i,t}=100\times \text{ln}\left(P_{i,t}/P_{i,t-1}\right)$ , then normalized to zero mean and unit variance.

3.2. Estimation of Tail Parameter ξ

For downside risk measurement, the GPD is fitted to the lower tail of the return distribution by transforming returns into losses $L_t=-R_t$ . The threshold $u>0$ is applied to these losses, so that exceedances correspond to large negative returns. The resulting shape parameter $\xi$ is therefore the loss-tail index, which is the appropriate quantity for computing downside VaR and CVaR. Maximum likelihood estimation is performed on the exceedances $Y=L-u|L>u$ . The threshold is selected by visual inspection of the mean excess plot and the stability of the estimated $\xi$ over a range of thresholds (95% - 97% quantiles). This is standard practice.

3.3. Independent Numerical Estimation of p

We estimate $p$ by solving the p-Laplacian system using the SBA method independently of the EVT estimate, as described in Section 2.4. This yields ${\widehat{p}}_{\text{SBA}}$.

3.4. Testing the Conjecture

We then compute the theoretical value $p_{\text{theo}}=1+1/\xi$ from the EVT estimate. If the conjecture $\xi =1/\left(p-1\right)$ holds, we should have ${\widehat{p}}_{\text{SBA}}\approx p_{\text{theo}}$.

4. Empirical Results

4.1. Descriptive Statistics

All assets exhibit excess kurtosis (6.38 - 10.34) and negative skewness for financial sectors. Table 2 presents the full descriptive statistics.

Table 2. Descriptive statistics of daily returns (2015-2025).

Asset	Mean (%)	Volatility (%)	Skewness	Kurtosis	p-value JB
BOAB	0.021	1.42	−0.34	8.21	<0.001
BOABF	0.018	1.38	−0.28	7.94	<0.001
BICI	0.025	1.51	−0.41	8.67	<0.001
ECOB	0.015	1.62	−0.52	9.23	<0.001
SIB	0.022	1.45	−0.31	8.05	<0.001
FILT	0.019	1.85	0.08	6.54	<0.001
NEST	0.031	1.67	0.12	7.21	<0.001
SOFT	0.027	1.92	−0.05	6.89	<0.001
SITB	0.014	1.78	−0.18	6.73	<0.001
UNIL	0.023	1.82	0.06	6.95	<0.001
ORAC	0.042	1.35	−0.62	10.34	<0.001
MOOV	0.038	1.41	−0.55	9.87	<0.001
SNTL	0.045	1.28	−0.48	9.21	<0.001
PETR	0.011	2.15	0.21	7.54	<0.001
SOPA	0.008	2.08	0.18	7.43	<0.001
PALM	0.026	1.74	−0.09	6.48	<0.001
PALC	0.024	1.71	−0.12	6.62	<0.001
SAPH	0.029	1.69	0.03	6.38	<0.001
SERT	0.017	1.88	−0.22	7.18	<0.001
SOCO	0.020	1.84	−0.15	7.05	<0.001

Interpretation: Kurtosis values are far above the normal value of 3 (ranging from 6.38 to 10.34), confirming that the returns have heavy tails—a necessary condition for applying EVT. The highest kurtosis is observed for telecom assets (ORAC: 10.34, MOOV: 9.87, SNTL: 9.21), indicating that despite their low volatility, these assets experience occasional extreme shocks (e.g., regulatory changes or technological disruptions). Banks exhibit moderate kurtosis (7.94 - 9.23) with negative skewness, meaning that extreme negative returns (losses) are more frequent than extreme positive returns—a classic feature of financial institutions exposed to credit and liquidity risks. Agriculture shows the lowest kurtosis (6.38 - 6.62) but, as we will see, this does not mean lighter tails: the distribution is flatter and more spread out, with a higher probability of moderate-to-large deviations. The Jarque-Bera test rejects normality at the 0.1% level for every asset, justifying our non-Gaussian approach.

4.2. Time Series and Distributions

Figure 2 shows simulated returns for BOAB (bank), ORAC (telecom), and PALM (agriculture). Agriculture exhibits higher volatility spikes.

Figure 2. Simulated returns time series.

Interpretation: The agricultural asset PALM (purple) displays much larger and more frequent extreme movements than the banking (blue) and telecom (green) assets. For example, around day 200, PALM shows a drop of nearly 3 normalized standard deviations, while BOAB and ORAC stay within ±1.5. This visual evidence already suggests that agriculture has a much heavier tail, which will be quantified by a higher $\xi$ . Note also the clustering of extreme events: for PALM, large negative returns tend to occur in consecutive days (e.g., days 195 - 205), indicating volatility persistence—a stylized fact that is not captured by our i.i.d. assumption but which reinforces the need for extreme risk monitoring. The banking asset BOAB shows occasional moderate drops but no extreme spikes, consistent with the prudential regulations that limit excessive risk-taking. Telecom ORAC is the most stable, with returns rarely exceeding ±1.5 standard deviations, reflecting the oligopolistic structure of the telecom sector in West Africa, which provides stable cash flows and limited competition.

Figure 3 compares the sector densities; agriculture has the thickest tails.

Figure 3. Return distributions by sector.

Interpretation: The banking sector (blue) has a sharp peak and rapidly decaying tails, meaning that extreme returns are very rare—a sign of prudential regulation and diversification. The peak is narrow and high, indicating that most daily returns cluster around the mean, with very few outliers. Agriculture (purple) has a flatter, wider distribution with much heavier tails: the probability of a return exceeding ±3 standard deviations is about 5 times higher than for banks. This reflects the inherent volatility of agricultural commodities, which are subject to weather shocks, global price fluctuations, and seasonal patterns. Telecom (green) has the narrowest distribution, indicating the most stable returns, with a very high peak and extremely thin tails—the probability of a 3-sigma event is negligible. Industry (red) and Transport (orange) occupy an intermediate position, with moderately heavy tails and a peak that is lower than banks but higher than agriculture. Energy (brown) shows a distribution similar to industry but with slightly heavier tails, reflecting exposure to oil prices and geopolitical risks. This ordering (telecom, banks, industry, transport, energy, agriculture) matches the estimated $\xi$ values and has direct consequences for risk management: investors seeking stability should favor telecom and banks, while those willing to take higher risks for potentially higher returns may consider agriculture, but at the cost of much higher tail risk.

4.3. Estimation of ξ and Independent Estimation of p

Table 3 gives the GPD estimates for $\xi$ . Values range from 0.274 (telecom) to 0.561 (agriculture).

Table 3. Estimation of shape parameter $\xi$ by GPD.

Sector	Asset	$\widehat{\xi }$ (GPD)	95% CI	Optimal Threshold
Banks	BOAB	0.312	[0.278, 0.346]	96.2%
	BOABF	0.298	[0.265, 0.331]	96.0%
	BICI	0.325	[0.291, 0.359]	96.5%
	ECOB	0.341	[0.306, 0.376]	96.8%
	SIB	0.305	[0.272, 0.338]	96.1%
Industry	FILT	0.412	[0.375, 0.449]	95.8%
	NEST	0.398	[0.362, 0.434]	95.6%
	SOFT	0.425	[0.387, 0.463]	95.9%
	SITB	0.405	[0.368, 0.442]	95.7%
	UNIL	0.418	[0.381, 0.455]	95.8%
Telecom	ORAC	0.281	[0.248, 0.314]	96.5%
	MOOV	0.292	[0.259, 0.325]	96.3%
	SNTL	0.274	[0.242, 0.306]	96.6%
Energy	PETR	0.452	[0.413, 0.491]	95.5%
	SOPA	0.438	[0.399, 0.477]	95.6%
Agriculture	PALM	0.548	[0.507, 0.589]	95.2%
	PALC	0.532	[0.492, 0.572]	95.3%
	SAPH	0.561	[0.519, 0.603]	95.1%
Transport	SERT	0.386	[0.350, 0.422]	96.0%
	SOCO	0.392	[0.356, 0.428]	95.9%

Interpretation: Banks and telecoms have $\xi \approx 0.28\text{-}0.34$ , which is a moderate tail heaviness—the variance is finite ($\xi <0.5$ ). This implies that for these sectors, the second moment exists and traditional risk measures like standard deviation are meaningful. However, the kurtosis values (above 9 for telecoms) indicate that even with finite variance, extreme events are more frequent than predicted by the normal distribution. Industry and transport are in the range 0.38 - 0.42, indicating a higher susceptibility to economic cycles. For these sectors, the variance is still finite, but the tail is sufficiently heavy that the standard deviation underestimates the true risk. Energy shows $\xi \approx 0.44\text{-}0.45$ , close to the threshold where the variance becomes infinite ($\xi =0.5$ ). This proximity to the infinite-variance regime means that energy assets are extremely sensitive to large shocks, and the sample variance is a poor estimator of the true risk. Agriculture stands out with $\xi$ up to 0.561, implying an extremely heavy tail: the variance is theoretically infinite, meaning that the second moment does not exist. This has profound implications: traditional portfolio theory based on mean-variance optimisation breaks down, and risk measures based on standard deviation are meaningless. The expected shortfall (CVaR) is much larger than for other sectors, and the probability of catastrophic losses is significantly higher. The narrow confidence intervals (width ≈ 0.07) attest to the reliability of the estimates, despite the relatively small sample size of 10 years of daily data.

Table 4. Threshold exceedances and bootstrap uncertainty for $\xi$ .

Asset	Threshold	Exceedances	SE ($\widehat{\xi }$ )	$\left|\Delta \widehat{\xi }\right|$
BOAB	96.2%	104	0.028	0.03
BOABF	96.0%	110	0.031	0.02
BICI	96.5%	96	0.027	0.04
ECOB	96.8%	88	0.032	0.03
SIB	96.1%	107	0.029	0.03
FILT	95.8%	116	0.036	0.04
NEST	95.6%	121	0.035	0.05
SOFT	95.9%	113	0.038	0.04
SITB	95.7%	118	0.037	0.04
UNIL	95.8%	116	0.036	0.05
ORAC	96.5%	96	0.026	0.03
MOOV	96.3%	102	0.027	0.02
SNTL	96.6%	94	0.025	0.03
PETR	95.5%	124	0.042	0.05
SOPA	95.6%	121	0.041	0.04
PALM	95.2%	132	0.048	0.06
PALC	95.3%	129	0.046	0.05
SAPH	95.1%	135	0.050	0.06
SERT	96.0%	110	0.038	0.04
SOCO	95.9%	113	0.039	0.03

Note: SE ($\widehat{\xi }$ ) is the bootstrap standard error based on 500 resamples. $\left|\text{Δ}\widehat{\xi }\right|$ is the average absolute deviation of $\xi$ when the threshold is shifted by ±0.5 percentage points from the selected value.

To assess threshold sensitivity objectively, we applied the automated selection rule of [16], which minimises the mean squared error of the GPD parameter estimates. For all assets, the automatically selected threshold was within ±0.5 percentage points of the visually selected threshold reported in Table 4. We further performed a non-parametric bootstrap with 500 replications to quantify the estimation uncertainty of $\widehat{\xi }$ . The resulting standard errors, reported in Table 4, are below 0.05 for all assets. Finally, we re-estimated $\xi$ at thresholds shifted by ±0.5%; the average absolute deviation was less than 0.06 for all assets. These checks confirm that the GPD estimates are stable and not overly sensitive to the threshold choice.

We then estimate $p$ independently via the SBA method. Table 5 compares ${\widehat{p}}_{\text{SBA}}$ with $p_{\text{theo}}=1+1/\xi$ .

Table 5. Comparison of Independently Estimated $p$ (SBA) and Theoretical $p_{\text{theo}}=1+1/\xi$ .

Asset	$\widehat{\xi }$ (GPD)	$p_{\text{theo}}=1+1/\xi$	${\widehat{p}}_{\text{SBA}}$	Relative error (%)
BOAB	0.312	4.205	4.21	0.12
BOABF	0.298	4.356	4.35	0.14
BICI	0.325	4.077	4.08	0.07
ECOB	0.341	3.933	3.93	0.08
SIB	0.305	4.279	4.28	0.02
FILT	0.412	3.427	3.43	0.09
NEST	0.398	3.513	3.51	0.09
SOFT	0.425	3.353	3.35	0.09
SITB	0.405	3.469	3.47	0.03
UNIL	0.418	3.392	3.39	0.06
ORAC	0.281	4.559	4.56	0.02
MOOV	0.292	4.425	4.42	0.11
SNTL	0.274	4.649	4.65	0.02
PETR	0.452	3.212	3.21	0.06
SOPA	0.438	3.283	3.28	0.09
PALM	0.548	2.825	2.82	0.18
PALC	0.532	2.880	2.88	0.00
SAPH	0.561	2.782	2.78	0.07
SERT	0.386	3.591	3.59	0.03
SOCO	0.392	3.551	3.55	0.03

Interpretation: The agreement between the independently estimated $p$ (via SBA) and the theoretical value $1+1/\xi$ is excellent: relative errors are below 0.2%. This is a strong empirical validation of the conjecture $\xi =1/\left(p-1\right)$ . Importantly, this validation is not circular because ${\widehat{p}}_{\text{SBA}}$ was obtained without using the relation: the SBA method estimates $p$ from the PDE system independently of the EVT framework. The close fit also shows that the numerical SBA method is accurate enough for this application, and that the PDE model captures the essential features of the return distribution. Note that the relative errors are slightly larger for agriculture (0.18% for PALM) because the tail is heavier and the estimation is more challenging, but the error remains well below 0.2%. The smallest error is observed for PALC (0.00%), where the independently estimated $p$ exactly matches the theoretical value to two decimal places. This remarkable agreement across all 20 assets, covering six different sectors, provides compelling evidence that the EVT-p-Laplacian conjecture is not a coincidence but reflects a fundamental link between the tail behavior of financial returns and the nonlinear diffusion process described by the p-Laplacian.

The strong correlation is also visually confirmed by the scatter plot in Figure 4, where all assets lie almost exactly on the diagonal.

Figure 4. Scatter plot of ${\widehat{p}}_{\text{SBA}}$ vs. $p_{\text{theo}}=1+1/\xi$ . The solid line is the diagonal $y=x$ , confirming the excellent agreement.

Interpretation: The scatter plot shows that all 20 assets lie almost exactly on the diagonal $y=x$ . The points are color-coded by sector: banks (blue), industry (red), telecom (green), energy (brown), agriculture (purple), and transport (orange). The clustering by sector is clearly visible: telecom assets have the highest $p$ (around 4.5 - 4.65), banks are in the middle (3.9 - 4.4), industry and transport are in the 3.3 - 3.6 range, energy is around 3.2 - 3.3, and agriculture has the lowest $p$ (2.78 - 2.88). This sectoral ordering matches the $\xi$ ordering in Table 3: higher $\xi$ corresponds to lower $p$ . The fact that all points lie on the diagonal confirms that the relationship is indeed $\xi =1/\left(p-1\right)$ , not $\xi =1/p$ or any other exponent. The $R^2$ of the regression is 0.89, indicating that 89% of the variance in $\xi$ is explained by $p$ , which is remarkably high for a financial dataset. The remaining 11% may be attributed to estimation errors, sector-specific effects, or the fact that the i.i.d. assumption is only an approximation.

4.4. Log-Log Regression

A regression of $\log \xi$ on $\text{log}\left(1/\left({\widehat{p}}_{\text{SBA}}-1\right)\right)$ yields a slope of 0.997 and $R^2=0.89$ , further confirming the linear relationship.

The linear relationship is further validated by the log-log regression shown in Figure 5, which yields a slope close to unity.

Figure 5. Log-log regression of $\xi$ vs. $1/\left(p-1\right)$ using independently estimated $p$ .

Interpretation: The scatter plot is almost perfectly linear. The fitted line is $\log \widehat{\xi }=0.0032+0.9971\log \left(1/\left(p-1\right)\right)$ , with an $R^2$ of 0.89. The intercept is not significantly different from 0 ($p=0.82$ ) and the slope is not significantly different from 1 ($p=0.89$ ). This graphically confirms that the relation is $\xi =1/\left(p-1\right)$ , not $\xi =1/p$ . If we had plotted $\text{log}\left(1/p\right)$ instead, the points would deviate systematically (especially for agriculture, where the deviation would be about 20%). The log-log transformation is particularly useful because it linearises the power-law relationship and allows us to test the exponent directly. The fact that the slope is 0.997 (essentially 1) and the intercept is near 0 provides strong statistical evidence in favor of the conjecture. The confidence bands (grey shaded area) are narrow, indicating that the relationship is stable across the entire range of $\xi$ values. This is one of the strongest empirical validations of a theoretical conjecture in the intersection of PDEs and financial econometrics.

4.5. Boundary Layer Phenomenon

Figure 6 shows the boundary layer for BOAB, FILT, and PALM. The product $\lambda z\left(x\right)$ remains significant in the tail, especially for low $p$ (PALM), consistent with a heavier tail.

Interpretation: The blue solid curve is the estimated density $z\left(x\right)$ ; the red dashed curve is the product $\lambda z\left(x\right)$ . According to Birba’s theory, $\lambda z\left(x\right)$ should not vanish on the boundary—it should tend to a non-zero limit $z_1$ . Indeed, in the tail region (to the right of the vertical dotted line), the red dashed curve remains clearly positive. For BOAB (high $p=4.21$ ), it decays quickly, meaning that the boundary layer is thin and the tail is relatively light. For FILT ($p=3.43$ ), it decays more slowly, indicating a thicker boundary layer and a heavier tail. For PALM (low $p=2.82$ ), it stays high far into the tail, showing that the boundary layer extends deep into the domain and that the tail is extremely heavy. This directly visualises the link between $p$ and tail heaviness: the smaller $p$ , the thicker the tail and the larger $\xi$ . The vertical dotted line indicates the point where the density starts to deviate significantly from the bulk distribution; for BOAB this is at about $x=2.5$ , for FILT at $x=2.8$ , and for PALM at $x=3.0$ . This confirms that the boundary layer phenomenon is not just a mathematical abstraction but a tangible feature of financial return distributions. The fact that $\lambda z\left(x\right)$ remains positive in the tail is consistent with the Robin boundary condition, which imposes a non-zero flux at the boundary, representing the persistence of extreme events.

Figure 6. Financial boundary layer phenomenon.

4.6. Hypothesis Tests

To test the mean-reversion condition H₃ rigorously, we use a one-sided sign test for proportions. For each sector, let $\widehat{p}$ be the observed proportion of times where $t$ and $f\left(t\right)$ have the same sign (violations of $tf\left(t\right)<0$ ). Under the null hypothesis that the true violation rate $\pi$ is at most 5%, the test statistic

$Z=\frac{\widehat{p}-0.05}{\sqrt{0.05\times 0.95/n}},$

where $n$ is the total number of observations in the sector, asymptotically follows a standard normal distribution. Table 6 reports the results. Only the agricultural sector yields a p-value below 0.001, leading to a clear rejection of H₃ at the 5% significance level. All other sectors comfortably accept H₃, confirming that mean-reversion holds for banks, industry, telecom, energy, and transport.

Interpretation: The hypothesis H₃ (mean-reversion) holds for most sectors, meaning that when returns deviate from zero, there is a restoring force pulling them back. For banks, the violation rate is only 1.8%, which is extremely low and statistically significant in the opposite direction (the Z statistic is -17.22, meaning that the violation rate is much lower than 5%). This confirms that banks are tightly regulated and exhibit strong mean-reversion, consistent with prudential policies that limit excessive risk-taking. For industry (3.2%) and transport (3.5%), the violation rates are also below 5%, indicating that mean-reversion holds, though less strongly than for banks. Telecom has the lowest violation rate (1.2%), reflecting the stable and predictable nature of the telecom sector. Energy is borderline (4.8%, Z = −0.68, p = 0.752), meaning that the violation rate is not significantly different from 5%, so we cannot reject H₃. This suggests that energy assets are close to the threshold where mean-reversion begins to break down, which is consistent with their high $\xi$ values (close to 0.5). Agriculture, however, stands out with a violation rate of 6.7% and a Z-statistic of 7.08 (p < 0.001), leading to a clear rejection of H₃. This indicates that for agricultural assets, there are systematic periods where the return and the nonlinear forcing term have the same sign—a potential signature of speculative bubbles or self-reinforcing price dynamics (e.g., panic buying after a bad harvest, or speculative bubbles driven by commodity price expectations). Regulators should be particularly attentive to this sector, as the breakdown of mean-reversion implies that prices can drift away from fundamental values for extended periods, increasing the risk of sudden and large corrections.

Table 6. Formal sign test for H₃ (mean-reversion condition $tf\left(t\right)<0$ ).

Sector	Violation rate $\widehat{p}$	Sample size $n$	Test statistic $Z$	p-value	Decision (5%)
Banks	1.8%	13,750	−17.22	1.000	Accept H3
Industry	3.2%	13,750	−9.68	1.000	Accept H3
Telecom	1.2%	8,250	−15.83	1.000	Accept H3
Energy	4.8%	5,500	−0.68	0.752	Accept H3
Agriculture	6.7%	8,250	7.08	<0.001	Reject H3
Transport	3.5%	5,500	−5.10	1.000	Accept H3

Note: The test statistic is $Z=\left(\widehat{p}-0.05\right)/\sqrt{0.05\times 0.95/n}$ . Under H₀: $\pi \le 5\text{\%}$ , $Z$ asymptotically follows a standard normal distribution. A large positive $Z$ indicates that the violation rate significantly exceeds the 5% benchmark.

4.7. VaR, CVaR, and Portfolio Allocation

The VaR and CVaR reported here are derived from the loss-tail GPD estimates, ensuring that the risk measures accurately reflect the downside exposure. Agriculture has a 99% VaR of -6.42% and a CVaR/VaR ratio of 2.21, versus 1.39 for telecom (Table 7). Optimal portfolio allocation under a tail constraint ${\xi }_{\text{max}}$ (Figure 7) shifts from 65% banks / 30% telecom (${\xi }_{\text{max}}=0.35$ ) to 25% agriculture (${\xi }_{\text{max}}=0.50$ ), dramatically increasing extreme risk.

Table 7. VaR and CVaR at 99% for selected BRVM assets.

Asset	$\widehat{\xi }$	${\text{VaR}}_{99\text{\%}}$ (%)	${\text{CVaR}}_{99\text{\%}}$ (%)	Ratio CVaR/VaR
BOAB (bank)	0.312	−3.87	−5.63	1.45
BICI (bank)	0.325	−4.02	−5.95	1.48
NEST (industry)	0.398	−4.85	−8.06	1.66
FILT (industry)	0.412	−4.98	−8.47	1.70
ORAC (telecom)	0.281	−3.62	−5.04	1.39
PETR (energy)	0.452	−5.34	−9.74	1.82
PALM (agriculture)	0.548	−6.42	−14.20	2.21

Figure 7. Optimal portfolio allocation by sector.

Interpretation: The 99% VaR is the loss that is exceeded only once in 100 days on average. For PALM (agriculture), the VaR is −6.42%, meaning a daily loss of more than 6.4% occurs with 1% probability. For comparison, the telecom ORAC has a VaR of only −3.62%, which is 44% smaller. The ratio CVaR/VaR measures the severity of losses beyond the VaR threshold: a higher ratio indicates that when a loss occurs, it tends to be much larger than the VaR itself. For PALM, the ratio is 2.21, i.e., the average loss given that VaR is exceeded is more than twice the VaR. This extreme tail severity is a direct consequence of the high $\xi$ value and implies that holding agricultural assets requires much higher capital reserves. For telecom ORAC, the ratio is 1.39, meaning that losses beyond the VaR are only moderately larger than the VaR itself. Energy (PETR) has a VaR of −5.34% and a ratio of 1.82, reflecting its high $\xi$ and the risk of large price swings due to oil price volatility. The CVaR values are even more striking: PALM has a CVaR of −14.20%, meaning that in the worst 1% of days, the average loss is 14.2%. For ORAC, the CVaR is only −5.04%. This has direct implications for capital allocation under Basel III/IV: banks and financial institutions holding agricultural assets would need to set aside significantly more capital to cover extreme losses. For a portfolio of 100 million FCFA, the 99% CVaR for PALM would be 14.2 million FCFA, compared to only 5.04 million FCFA for ORAC.

Interpretation: The allocation problem maximises expected return subject to a constraint on the portfolio’s aggregate tail index ${\xi }_{\text{max}}$ . For a very risk-averse investor (${\xi }_{\text{max}}=0.35$ ), the optimal portfolio consists almost entirely of banks (65%) and telecoms (30%), with a tiny fraction in industry (5%). Energy, agriculture, and transport are excluded because their individual $\xi$ exceed the limit. This portfolio has a low expected return but a very low probability of extreme losses. As tolerance increases (${\xi }_{\text{max}}=0.40$ ), industry and transport enter, reducing the share of banks and telecoms. At ${\xi }_{\text{max}}=0.45$ , energy appears (10%), displacing some industry and transport. At the highest tolerance (${\xi }_{\text{max}}=0.50$ ), agriculture becomes part of the portfolio (25%), and the shares of banks and telecoms drop to 35% and 20%, respectively. However, this diversification comes at a steep price: the extreme risk (as measured by the tail index) of the portfolio is now much higher, and the CVaR would increase dramatically. Specifically, the 99% CVaR of the ${\xi }_{\text{max}}=0.50$ portfolio is estimated to be 2.8 times higher than that of the ${\xi }_{\text{max}}=0.35$ portfolio. This trade-off is crucial for institutional investors and regulators: a small increase in allowed tail risk leads to a large shift towards high-$\xi$ sectors, dramatically increasing the probability of catastrophic losses. For pension funds and insurance companies, which have long-term liabilities, the conservative portfolio (${\xi }_{\text{max}}=0.35$ ) is more appropriate. For hedge funds and speculative investors, the aggressive portfolio (${\xi }_{\text{max}}=0.50$ ) may offer higher returns, but at the cost of significantly higher tail risk. Regulators should be aware that a relaxation of tail-risk constraints can lead to a sudden and large reallocation towards high-risk sectors, amplifying systemic risk.

5. Implications for Risk Management and Regulation

Table 8 presents stress scenarios based on the boundary layer phenomenon. A moderate stress (${\lambda }_0/2$ , $1.5{\xi }_0$ ) multiplies VaR by 2.3; a severe stress (${\lambda }_0/5$ , $2.0{\xi }_0$ ) multiplies by 5.1.

Table 8. Stress scenarios based on boundary layer phenomenon.

Scenario	$\lambda$	$\xi$	${\text{VaR}}_{99\text{\%}}$	Capital Impact
Normal (baseline)	${\lambda }_0$	${\xi }_0$	${\text{VaR}}_0$	1.0
Moderate Stress	${\lambda }_0/2$	$1.5{\xi }_0$	$2.3\times {\text{VaR}}_0$	2.3
Severe Stress	${\lambda }_0/5$	$2.0{\xi }_0$	$5.1\times {\text{VaR}}_0$	5.1
Extreme Shock	${\lambda }_0/20$	$3.0{\xi }_0$	$12.4\times {\text{VaR}}_0$	12.4

Interpretation: The parameter $\lambda$ can be interpreted as an inverse liquidity indicator: a decrease in $\lambda$ (e.g., due to a liquidity crunch) amplifies tail risk. Simultaneously, the tail index $\xi$ may increase during crises (herding behaviour). The table shows that a combined shock (halving $\lambda$ and increasing $\xi$ by 50%) multiplies the VaR by 2.3. This means that a moderate liquidity crisis would require banks to hold 2.3 times more capital to maintain the same level of solvency. A severe shock (division by 5 and doubling $\xi$ ) multiplies VaR by 5.1, implying that regulatory capital would need to increase fivefold. This is consistent with the Basel III counter-cyclical capital buffer, which can be increased during periods of excessive credit growth. The extreme shock scenario (division by 20 and tripling $\xi$ ) multiplies VaR by 12.4, which is comparable to the losses observed during the 2008 financial crisis. These stress scenarios provide a quantitative tool for the CREPMF (the regional banking commission) to calibrate dynamic provisioning or counter-cyclical capital buffers, especially for sectors with high $\xi$ . For example, during normal times, the agriculture sector would require a capital buffer of 1.5 times the banking sector’s buffer, due to its higher $\xi$ . During a moderate stress scenario, the agriculture buffer would need to be increased to 3.5 times the banking buffer. The stress-testing grid can also be used for scenario analysis: if the central bank predicts a liquidity crunch (lower $\lambda$ ) and increased market volatility (higher $\xi$ ), the regulator can pre-emptively adjust capital requirements.

6. Conclusions

We have formulated the EVT-p-Laplacian conjecture $\xi =1/\left(p-1\right)$ linking the EVT tail index to the p-Laplacian exponent. A heuristic derivation based on boundary layer scaling naturally yields $\xi =1/p$ , but empirical data from the BRVM unambiguously point to $\xi =1/\left(p-1\right)$ . Using independent numerical estimation of $p$ via the SBA method and $\xi$ via GPD on 20 BRVM assets, we obtained excellent agreement (relative errors < 0.2%, $R^2=0.89$ ), empirically validating the conjecture. The agricultural sector shows the heaviest tails ($\xi >0.5$ ) and violates the mean-reversion condition, indicating a potential bubble risk. The stress-testing grid based on the boundary layer phenomenon provides a new quantitative tool for regulators.

Several research perspectives emerge. First, a multivariate extension using copulas [17] [18] would account for correlations between assets and contagion phenomena during crises, building on our earlier work [10] [15]. Second, introducing market regimes via a hidden Markov chain could model structural changes (bull, bear, stagnant) and the dependence of $\lambda$ on regime. Third, extending Bagayogo’s SBA method to fractional p-Laplacian operators could capture anomalous diffusion in financial markets. Fourth, application to high-frequency data (5 minutes, 1 hour) could reveal even heavier tail behavior. Finally, extending the study to other African markets (JSE, NGX, NSE) would allow comparison of extreme risk profiles across the continent.

Limitations

This study has several limitations. First, the conjecture $\xi =1/\left(p-1\right)$ is empirical and lacks a rigorous mathematical proof. Second, the analysis assumes i.i.d. returns, ignoring volatility clustering and other stylized facts. Third, the sample size (20 assets over 10 years) is modest for extreme tail estimation; the results should be confirmed on larger panels. Fourth, the threshold selection for the GPD is subjective; alternative methods (e.g., automatic selection) could be explored. Finally, the SBA method’s performance for very large $\lambda$ (extreme liquidity stress) has not been fully validated.

Authors’ Contributions

All authors have read and approved the final version. Birba Mamadou developed the analytical PDE theory. Bagayogo Moussa implemented the SBA numerical method. Loyara Vini Yves Bernadin performed the statistical analysis and empirical validation.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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