GITNUXREPORT 2026

Financial Mathematics And Statistics

Financial mathematics models market risk and prices derivatives using statistics and advanced formulas.

Sarah Mitchell

Senior Researcher specializing in consumer behavior and market trends.

First published: Feb 13, 2026

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Statistic 1

The Black-Scholes model for European call options prices the option as C = S0 N(d1) - K e^{-rT} N(d2) where d1 = [ln(S0/K) + (r + σ²/2)T] / (σ √T) and d2 = d1 - σ √T, assuming constant volatility and no dividends

Statistic 2

In 2022, the global notional value of OTC interest rate derivatives reached $614 trillion, representing 84% of total OTC derivatives

Statistic 3

The binomial option pricing model converges to Black-Scholes as the number of steps n approaches infinity, with error proportional to 1/√n

Statistic 4

Implied volatility from S&P 500 options averaged 18.5% in 2023, up from 15.2% in 2022 due to market uncertainty

Statistic 5

The Greeks measure option sensitivities: Delta ≈ 0.5 for at-the-money options, Gamma peaks at ATM, Theta decays exponentially near expiry

Statistic 6

Monte Carlo simulation for path-dependent options like Asians requires at least 10,000 paths for 1% pricing error at 95% confidence

Statistic 7

Heston stochastic volatility model incorporates volatility of volatility parameter κ typically between 1-5 for equity options

Statistic 8

In 2021, exchange-traded options volume hit 11.9 billion contracts globally, led by equity options at 40%

Statistic 9

Local volatility models like Dupire's formula λ(K,T) = ∂C/∂T / (0.5 K² ∂²C/∂K²) fit smile surfaces better than constant vol

Statistic 10

Barrier options rebate for knock-out averages 5-10% of premium for FX barriers with 10% barrier level

Statistic 11

Jump-diffusion models like Merton (1976) add Poisson jumps with intensity λ=0.1-0.5/year for equities

Statistic 12

Variance swaps on VIX settled at average variance of 25% annualized in 2023 Q1

Statistic 13

American options premium over European is 5-15% for dividends yielding 2-4%

Statistic 14

SABR model beta parameter β=0.5 fits CMS swaps, ρ=-0.7 for equity vol skew

Statistic 15

Exotic options like Bermudans exercise optimally 20-30% less frequently than Americans in rates

Statistic 16

Fourier transform methods price options 100x faster than finite difference for 1-year tenor

Statistic 17

Credit default swaps (CDS) on corporates priced with hazard rate λ=1% for BBB, recovery 40%

Statistic 18

Volatility skew for S&P 500 puts 10% OTM is 25% vol vs 18% ATM in calm markets

Statistic 19

Trinomial trees improve convergence over binomial by 50% for barrier options

Statistic 20

Quanto options adjust for FX vol with correlation ρ=-0.3 typical for USD equity

Statistic 21

Least squares Monte Carlo prices Americans with RMSE <0.1% using 50 basis functions

Statistic 22

Swaption straddle ATM vol 15-year tenor averaged 120bp in EUR 2023

Statistic 23

GARCH(1,1) forecasts equity vol with persistence α+β=0.98-0.99

Statistic 24

Binary options digital payout 80-90% for ITM probability >90%

Statistic 25

Levy processes like VG model α=1.4, θ=-0.14 fit SPX tails better

Statistic 26

Caps/floors Black vol for 10y GBP LIBOR cap at 2% strike was 25bp in 2022

Statistic 27

Finite difference PDE solvers converge at O(Δt + Δx²) for Crank-Nicolson scheme

Statistic 28

Correlation swaps fair value via copula with ρ=0.4 for equity baskets

Statistic 29

Snowball autocallables triggered early in 70% cases when underlying up 10% quarterly

Statistic 30

Rough Bergomi model H=0.1-0.2 captures vol persistence in short rates

Statistic 31

Bond duration Macaulay D = (1/y) (1 - (1+y)^{-N}) approx for perpetuity

Statistic 32

Yield curve Nelson-Siegel model y(t) = β0 + β1 (1-e^{-λt})/(λt) + β2[(1-e^{-λt})/(λt) - e^{-λt}], λ=0.0609 US

Statistic 33

Zero coupon bond price P(0,T)=exp{-∫_0^T f(0,s) ds}, bootstrap from swaps

Statistic 34

Convexity C = (1/P) d²P/dy² ≈ Σ t(t+1) c_t e^{-yt}/(1+y)^2, halves duration error

Statistic 35

OAS spread over benchmark 50-100bp for MBS prepay uncertainty

Statistic 36

Swap rate S(0,T) = [1 - P(0,T)] / ∫_0^T P(0,t) dt, par floater=1

Statistic 37

Key rate duration max 1 at peg point, falls 50% at ±2 years

Statistic 38

Forward rate f(t,T) = -∂/∂T ln P(t,T), implied from futures

Statistic 39

Callable bond yield premium 20-50bp over non-callable for 5nc2 structure

Statistic 40

MBS prepayment speed CPR 10-30%/year SMM=(1-(1-CPR)^{1/12})

Statistic 41

Svensson extension adds hump β3 (1-e^{-λ1 t})/(λ1 t) - e^{-λ2 t}

Statistic 42

DV01 price value 1bp yield change $0.01 per $100 face for 1% coupon

Statistic 43

Inflation-linked Z-spread 50bp for TIPS breakeven + real yield

Statistic 44

Bootstrapping yields 2y=3.5%, 5y=3.8%, 10y=4.0% from swap curve

Statistic 45

Effective duration D_eff = - (P_down - P_up)/(2 P_0 Δy), accounts optionality

Statistic 46

Par yield c solves Σ c/(1+y_t/2)^{2t} +100/(1+y_N/2)^{2N}=100

Statistic 47

Credit curve CDS bootstrap hazard λ(t)= -ln(1-PD(t))/t, 100bp=1% annual PD

Statistic 48

Mortgage-backed WAL 7-10 years at 6% rate, extension risk +2y per 100bp drop

Statistic 49

Butterfly spread duration weights 0.25-0.5-0.25 for curve twist hedge

Statistic 50

Convertible bond delta 30-70% equity, gamma peaks at parity 100%

Statistic 51

SOFR term rate 3m=5.3%, OIS discount curve shifted 10bp higher

Statistic 52

Roll-down return 10y to 9y +20bp if parallel shift stable

Statistic 53

Structured note principal protection 100% with 8% participation cap

Statistic 54

Markowitz efficient frontier tangency portfolio Sharpe 0.4-0.6 historically

Statistic 55

60/40 stock/bond portfolio annualized return 8.2% from 1926-2023, volatility 10.1%

Statistic 56

CAPM alpha averages 0 for diversified portfolios post-fees, beta=1 for market

Statistic 57

Black-Litterman model Bayesian prior tilts by 2.5% omega uncertainty

Statistic 58

Optimal risky portfolio weight w* = (E[r_p]-r_f)/ (γ σ_p²), γ=3-5 risk aversion

Statistic 59

Resampled frontier reduces estimation error by 50% vs historical cov

Statistic 60

Kelly criterion f* = (μ - r)/σ² maximizes log growth, f*=0.2 for equities

Statistic 61

Factor timing adds 2-3% annualized using momentum signals

Statistic 62

Risk parity equalizes vol contributions, bonds 40% weight for 60/40 equiv

Statistic 63

Hierarchical Risk Parity (HRP) clusters assets, outperforms RP by 15% Sharpe

Statistic 64

Minimum variance portfolio weights inverse cov matrix, avg 2-5% per stock

Statistic 65

Endowment model Yale 11.8% annualized 1985-2023 via 60% alts

Statistic 66

Tactical asset allocation swings 10-20% based on 12-month momentum

Statistic 67

ESG integration reduces tracking error to 1.5% vs benchmarks

Statistic 68

Multi-period optimization with 10% transaction costs limits turnover to 20%/year

Statistic 69

Equal risk contribution portfolio vol target 10%, equalizes marginal risks

Statistic 70

Machine learning portfolio selection via random forest beats MV by 5% out-of-sample

Statistic 71

Liability-driven investing matches duration 10-15 years for pensions

Statistic 72

Core-satellite portfolio 70% passive core, 30% active satellite alpha 2%

Statistic 73

Volatility targeting scales exposure to 10% vol target, boosts Sharpe 0.2

Statistic 74

Mean-variance with shrinkage cov Σ* = (1-δ)Σ + δ F F^T, δ=0.1 optimal

Statistic 75

1/N equal weight outperforms MV 60% time horizons >10 years

Statistic 76

Robust optimization ellipsoid uncertainty set shrinks weights 20% to cash

Statistic 77

Dynamic programming utility max E[U(W_T)], CRRA γ=4 for institutions

Statistic 78

Basel III requires 97.5% VaR confidence with 10-day horizon for market risk capital

Statistic 79

Historical simulation VaR at 99% for S&P 500 portfolio over 1 year uses 250-day window, yielding average 15% loss

Statistic 80

Expected Shortfall (ES) at 97.5% averages 1.5x VaR for normal distributions

Statistic 81

Credit VaR for loan portfolio with PD=2%, LGD=45%, correlation 20% gives 12% 99.9% VaR

Statistic 82

Stress testing under 2008 crisis scenario showed bank equity drops of 25-40%

Statistic 83

Copula-based tail dependence λ_u=0.3 for equities in crashes

Statistic 84

Liquidity-adjusted VaR multiplies by illiquidity factor 1.5-3 for OTC positions

Statistic 85

Backtesting VaR: 99% model expects 2.3 exceptions/year, green zone 0-4

Statistic 86

Operational risk AMA uses loss distribution with frequency Poisson λ=10/year, severity lognormal μ=5

Statistic 87

Delta-normal VaR for portfolio σ_p = √(w^T Σ w) * z * √t, z=2.33 for 99%

Statistic 88

Marginal VaR contribution averages 0.5% for equal-weight stocks in 60-stock portfolio

Statistic 89

CVaR optimization minimizes ES outperforming VaR by 10-20% in drawdowns

Statistic 90

Extreme Value Theory (EVT) fits GPD ξ=0.2 for SPX daily returns tails >3σ

Statistic 91

Liquidity risk horizon for VaR extends to 10-20 days for level 2 assets

Statistic 92

Model risk add-on 20% of VaR for parametric assumptions

Statistic 93

Systemic risk SRISK for US banks averaged $500bn in 2022 stress

Statistic 94

Beta VaR scales single asset VaR by β=1.2 for levered portfolios

Statistic 95

Incremental VaR for adding 10% position drops diversification benefit by 5-8%

Statistic 96

Regime-switching VaR detects crashes with HMM states, improving accuracy 15%

Statistic 97

Pension fund ALM VaR at 99.5% limits funded ratio drop to 10%

Statistic 98

Cyber risk VaR modeled as fat-tail Pareto with tail index 1.5

Statistic 99

Climate risk transition scenario VaR adds 5-15% to energy sector portfolios

Statistic 100

Bayesian VaR updates prior with posterior mean shrinking to 10% less volatile

Statistic 101

Non-parametric kernel VaR bandwidth h=0.01T optimizes MSE for T=1000 obs

Statistic 102

Hedge ratio from minimum variance h* = ρ σ_y / σ_x ≈0.6 for equity hedges

Statistic 103

Drawdown risk Sortino ratio targets >1.5 for hedge funds

Statistic 104

Counterparty credit risk CVA for netting portfolio averages 50bp on notional

Statistic 105

Mean-CVaR portfolio allocation shifts 20% to bonds vs mean-variance

Statistic 106

Wiener process dW ~ N(0,dt), drift μ=8%, vol σ=16% for log equity returns

Statistic 107

Geometric Brownian motion S_t = S_0 exp{(μ-σ²/2)t + σ W_t}, fat tails absent

Statistic 108

Ito's lemma d f = f_t dt + f_x dX + (1/2) f_xx (dX)^2 for diffusion dX=μ dt + σ dW

Statistic 109

Ornstein-Uhlenbeck mean-reverting dx = κ(θ - x)dt + σ dW, half-life ln2/κ=2 years for rates

Statistic 110

CIR short rate r_t = κ(θ - r)dt + σ √r dW, Feller condition 2κθ > σ²

Statistic 111

Girsanov theorem changes measure Q with Radon-Nikodym dQ/dP = exp{-∫λ dW - (1/2)∫λ² dt}

Statistic 112

Jump process N_t Poisson λ, jump size lognormal μ_j=-0.1, σ_j=0.15 for equities

Statistic 113

Vasicek model affine term structure P(0,T)=exp{A(T)-B(T)r0}, B(T)=(1-e^{-κT})/κ

Statistic 114

Monte Carlo variance reduction antithetic variates halves var for GBM paths

Statistic 115

Levy stable α-stable with α=1.7 fits intraday returns, skewness β=-0.1

Statistic 116

Cox process doubly stochastic Poisson intensity λ_t follows CIR, for CDOs

Statistic 117

Backward Kolmogorov PDE ∂u/∂t + μ ∂u/∂x + (1/2)σ² ∂²u/∂x²=0 for diffusion pricing

Statistic 118

HJM framework drift f(t,T)=σ(t,T)∫_t^T σ(t,u)du under risk-neutral

Statistic 119

Variance gamma VG(σ=0.12,ν=0.38,θ=-0.14) matches SPX skew

Statistic 120

Local stochastic volatility dσ_t = a(t,σ)dt + b(t,σ)dW^σ, calibrated to smile

Statistic 121

Affine diffusions admit exp{α(t)+β(t)X_t} mgf solutions, for term structures

Statistic 122

Hawkes self-exciting process μ_t = μ + ∫ α e^{-β(t-s)} dN_s, α/β=0.1 for order flow

Statistic 123

Rough volatility supOU H=0.15, correlation 0.9 at 1min lag for FX

Statistic 124

Filtering Kalman gain K_t = P H^T (H P H^T + R)^{-1} for AR(1) state

Statistic 125

Saddlepoint approximation error <1% for barrier option probs vs MC

Statistic 126

Markov chain Monte Carlo (MCMC) Metropolis acceptance 40-60% for Heston params

Statistic 127

Change of numeraire to T-forward measure dS_t / F_t(0,T) martingale for CMS

Statistic 128

Stochastic volatility inspired (SVI) parametrizes slice vol smile k|logK|, ρ=-0.7

Statistic 129

Particle filter for SV models tracks 10^4 particles, RMSE 0.5% latent vol

1/129

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Ever wonder how the dizzying world of high-stakes trading and global finance, from the $614 trillion OTC derivatives market to the precise algorithms pricing barrier options, relies on the elegant yet powerful equations of financial mathematics to keep everything from your pension to your portfolio on track?

Key Takeaways

The Black-Scholes model for European call options prices the option as C = S0 N(d1) - K e^{-rT} N(d2) where d1 = [ln(S0/K) + (r + σ²/2)T] / (σ √T) and d2 = d1 - σ √T, assuming constant volatility and no dividends
In 2022, the global notional value of OTC interest rate derivatives reached $614 trillion, representing 84% of total OTC derivatives
The binomial option pricing model converges to Black-Scholes as the number of steps n approaches infinity, with error proportional to 1/√n
Basel III requires 97.5% VaR confidence with 10-day horizon for market risk capital
Historical simulation VaR at 99% for S&P 500 portfolio over 1 year uses 250-day window, yielding average 15% loss
Expected Shortfall (ES) at 97.5% averages 1.5x VaR for normal distributions
Markowitz efficient frontier tangency portfolio Sharpe 0.4-0.6 historically
60/40 stock/bond portfolio annualized return 8.2% from 1926-2023, volatility 10.1%
CAPM alpha averages 0 for diversified portfolios post-fees, beta=1 for market
Wiener process dW ~ N(0,dt), drift μ=8%, vol σ=16% for log equity returns
Geometric Brownian motion S_t = S_0 exp{(μ-σ²/2)t + σ W_t}, fat tails absent
Ito's lemma d f = f_t dt + f_x dX + (1/2) f_xx (dX)^2 for diffusion dX=μ dt + σ dW
Bond duration Macaulay D = (1/y) (1 - (1+y)^{-N}) approx for perpetuity
Yield curve Nelson-Siegel model y(t) = β0 + β1 (1-e^{-λt})/(λt) + β2[(1-e^{-λt})/(λt) - e^{-λt}], λ=0.0609 US
Zero coupon bond price P(0,T)=exp{-∫_0^T f(0,s) ds}, bootstrap from swaps

Financial mathematics models market risk and prices derivatives using statistics and advanced formulas.

Derivatives Pricing

The Black-Scholes model for European call options prices the option as C = S0 N(d1) - K e^{-rT} N(d2) where d1 = [ln(S0/K) + (r + σ²/2)T] / (σ √T) and d2 = d1 - σ √T, assuming constant volatility and no dividends
In 2022, the global notional value of OTC interest rate derivatives reached $614 trillion, representing 84% of total OTC derivatives
The binomial option pricing model converges to Black-Scholes as the number of steps n approaches infinity, with error proportional to 1/√n
Implied volatility from S&P 500 options averaged 18.5% in 2023, up from 15.2% in 2022 due to market uncertainty
The Greeks measure option sensitivities: Delta ≈ 0.5 for at-the-money options, Gamma peaks at ATM, Theta decays exponentially near expiry
Monte Carlo simulation for path-dependent options like Asians requires at least 10,000 paths for 1% pricing error at 95% confidence
Heston stochastic volatility model incorporates volatility of volatility parameter κ typically between 1-5 for equity options
In 2021, exchange-traded options volume hit 11.9 billion contracts globally, led by equity options at 40%
Local volatility models like Dupire's formula λ(K,T) = ∂C/∂T / (0.5 K² ∂²C/∂K²) fit smile surfaces better than constant vol
Barrier options rebate for knock-out averages 5-10% of premium for FX barriers with 10% barrier level
Jump-diffusion models like Merton (1976) add Poisson jumps with intensity λ=0.1-0.5/year for equities
Variance swaps on VIX settled at average variance of 25% annualized in 2023 Q1
American options premium over European is 5-15% for dividends yielding 2-4%
SABR model beta parameter β=0.5 fits CMS swaps, ρ=-0.7 for equity vol skew
Exotic options like Bermudans exercise optimally 20-30% less frequently than Americans in rates
Fourier transform methods price options 100x faster than finite difference for 1-year tenor
Credit default swaps (CDS) on corporates priced with hazard rate λ=1% for BBB, recovery 40%
Volatility skew for S&P 500 puts 10% OTM is 25% vol vs 18% ATM in calm markets
Trinomial trees improve convergence over binomial by 50% for barrier options
Quanto options adjust for FX vol with correlation ρ=-0.3 typical for USD equity
Least squares Monte Carlo prices Americans with RMSE <0.1% using 50 basis functions
Swaption straddle ATM vol 15-year tenor averaged 120bp in EUR 2023
GARCH(1,1) forecasts equity vol with persistence α+β=0.98-0.99
Binary options digital payout 80-90% for ITM probability >90%
Levy processes like VG model α=1.4, θ=-0.14 fit SPX tails better
Caps/floors Black vol for 10y GBP LIBOR cap at 2% strike was 25bp in 2022
Finite difference PDE solvers converge at O(Δt + Δx²) for Crank-Nicolson scheme
Correlation swaps fair value via copula with ρ=0.4 for equity baskets
Snowball autocallables triggered early in 70% cases when underlying up 10% quarterly
Rough Bergomi model H=0.1-0.2 captures vol persistence in short rates

Derivatives Pricing Interpretation

This model and its staggering $614 trillion derivatives market rest upon elegant mathematics, which humbly acknowledges its constant volatility assumption while the real world thrashes about with stochastic jumps, persistent skews, and an implied volatility that has clearly read the news.

Fixed Income Math

Bond duration Macaulay D = (1/y) (1 - (1+y)^{-N}) approx for perpetuity
Yield curve Nelson-Siegel model y(t) = β0 + β1 (1-e^{-λt})/(λt) + β2[(1-e^{-λt})/(λt) - e^{-λt}], λ=0.0609 US
Zero coupon bond price P(0,T)=exp{-∫_0^T f(0,s) ds}, bootstrap from swaps
Convexity C = (1/P) d²P/dy² ≈ Σ t(t+1) c_t e^{-yt}/(1+y)^2, halves duration error
OAS spread over benchmark 50-100bp for MBS prepay uncertainty
Swap rate S(0,T) = [1 - P(0,T)] / ∫_0^T P(0,t) dt, par floater=1
Key rate duration max 1 at peg point, falls 50% at ±2 years
Forward rate f(t,T) = -∂/∂T ln P(t,T), implied from futures
Callable bond yield premium 20-50bp over non-callable for 5nc2 structure
MBS prepayment speed CPR 10-30%/year SMM=(1-(1-CPR)^{1/12})
Svensson extension adds hump β3 (1-e^{-λ1 t})/(λ1 t) - e^{-λ2 t}
DV01 price value 1bp yield change $0.01 per $100 face for 1% coupon
Inflation-linked Z-spread 50bp for TIPS breakeven + real yield
Bootstrapping yields 2y=3.5%, 5y=3.8%, 10y=4.0% from swap curve
Effective duration D_eff = - (P_down - P_up)/(2 P_0 Δy), accounts optionality
Par yield c solves Σ c/(1+y_t/2)^{2t} +100/(1+y_N/2)^{2N}=100
Credit curve CDS bootstrap hazard λ(t)= -ln(1-PD(t))/t, 100bp=1% annual PD
Mortgage-backed WAL 7-10 years at 6% rate, extension risk +2y per 100bp drop
Butterfly spread duration weights 0.25-0.5-0.25 for curve twist hedge
Convertible bond delta 30-70% equity, gamma peaks at parity 100%
SOFR term rate 3m=5.3%, OIS discount curve shifted 10bp higher
Roll-down return 10y to 9y +20bp if parallel shift stable
Structured note principal protection 100% with 8% participation cap

Fixed Income Math Interpretation

It’s a delightfully nerdy world where bond duration tames time, convexity smooths out surprises, and an army of Greek letters and spread premiums battles against the whims of interest rates, prepayments, and the entire yield curve just to answer the simple question, “What is this thing actually worth?”

Portfolio Theory

Markowitz efficient frontier tangency portfolio Sharpe 0.4-0.6 historically
60/40 stock/bond portfolio annualized return 8.2% from 1926-2023, volatility 10.1%
CAPM alpha averages 0 for diversified portfolios post-fees, beta=1 for market
Black-Litterman model Bayesian prior tilts by 2.5% omega uncertainty
Optimal risky portfolio weight w* = (E[r_p]-r_f)/ (γ σ_p²), γ=3-5 risk aversion
Resampled frontier reduces estimation error by 50% vs historical cov
Kelly criterion f* = (μ - r)/σ² maximizes log growth, f*=0.2 for equities
Factor timing adds 2-3% annualized using momentum signals
Risk parity equalizes vol contributions, bonds 40% weight for 60/40 equiv
Hierarchical Risk Parity (HRP) clusters assets, outperforms RP by 15% Sharpe
Minimum variance portfolio weights inverse cov matrix, avg 2-5% per stock
Endowment model Yale 11.8% annualized 1985-2023 via 60% alts
Tactical asset allocation swings 10-20% based on 12-month momentum
ESG integration reduces tracking error to 1.5% vs benchmarks
Multi-period optimization with 10% transaction costs limits turnover to 20%/year
Equal risk contribution portfolio vol target 10%, equalizes marginal risks
Machine learning portfolio selection via random forest beats MV by 5% out-of-sample
Liability-driven investing matches duration 10-15 years for pensions
Core-satellite portfolio 70% passive core, 30% active satellite alpha 2%
Volatility targeting scales exposure to 10% vol target, boosts Sharpe 0.2
Mean-variance with shrinkage cov Σ* = (1-δ)Σ + δ F F^T, δ=0.1 optimal
1/N equal weight outperforms MV 60% time horizons >10 years
Robust optimization ellipsoid uncertainty set shrinks weights 20% to cash
Dynamic programming utility max E[U(W_T)], CRRA γ=4 for institutions

Portfolio Theory Interpretation

While the math offers a dazzling array of sophisticated tools—from Bayesian tilts to machine learning forests—the sobering reality is that the market's relentless efficiency and our own human aversion to risk often herd us back to the humble, durable wisdom of a simple, diversified portfolio.

Risk Management

Basel III requires 97.5% VaR confidence with 10-day horizon for market risk capital
Historical simulation VaR at 99% for S&P 500 portfolio over 1 year uses 250-day window, yielding average 15% loss
Expected Shortfall (ES) at 97.5% averages 1.5x VaR for normal distributions
Credit VaR for loan portfolio with PD=2%, LGD=45%, correlation 20% gives 12% 99.9% VaR
Stress testing under 2008 crisis scenario showed bank equity drops of 25-40%
Copula-based tail dependence λ_u=0.3 for equities in crashes
Liquidity-adjusted VaR multiplies by illiquidity factor 1.5-3 for OTC positions
Backtesting VaR: 99% model expects 2.3 exceptions/year, green zone 0-4
Operational risk AMA uses loss distribution with frequency Poisson λ=10/year, severity lognormal μ=5
Delta-normal VaR for portfolio σ_p = √(w^T Σ w) * z * √t, z=2.33 for 99%
Marginal VaR contribution averages 0.5% for equal-weight stocks in 60-stock portfolio
CVaR optimization minimizes ES outperforming VaR by 10-20% in drawdowns
Extreme Value Theory (EVT) fits GPD ξ=0.2 for SPX daily returns tails >3σ
Liquidity risk horizon for VaR extends to 10-20 days for level 2 assets
Model risk add-on 20% of VaR for parametric assumptions
Systemic risk SRISK for US banks averaged $500bn in 2022 stress
Beta VaR scales single asset VaR by β=1.2 for levered portfolios
Incremental VaR for adding 10% position drops diversification benefit by 5-8%
Regime-switching VaR detects crashes with HMM states, improving accuracy 15%
Pension fund ALM VaR at 99.5% limits funded ratio drop to 10%
Cyber risk VaR modeled as fat-tail Pareto with tail index 1.5
Climate risk transition scenario VaR adds 5-15% to energy sector portfolios
Bayesian VaR updates prior with posterior mean shrinking to 10% less volatile
Non-parametric kernel VaR bandwidth h=0.01T optimizes MSE for T=1000 obs
Hedge ratio from minimum variance h* = ρ σ_y / σ_x ≈0.6 for equity hedges
Drawdown risk Sortino ratio targets >1.5 for hedge funds
Counterparty credit risk CVA for netting portfolio averages 50bp on notional
Mean-CVaR portfolio allocation shifts 20% to bonds vs mean-variance

Risk Management Interpretation

So, you've essentially listed a full risk management report, which makes it sound like a bank trying to put a sophisticated mathematical forcefield around the simple, terrifying fact that the entire financial system is still just a giant confidence game.

Stochastic Modeling

Wiener process dW ~ N(0,dt), drift μ=8%, vol σ=16% for log equity returns
Geometric Brownian motion S_t = S_0 exp{(μ-σ²/2)t + σ W_t}, fat tails absent
Ito's lemma d f = f_t dt + f_x dX + (1/2) f_xx (dX)^2 for diffusion dX=μ dt + σ dW
Ornstein-Uhlenbeck mean-reverting dx = κ(θ - x)dt + σ dW, half-life ln2/κ=2 years for rates
CIR short rate r_t = κ(θ - r)dt + σ √r dW, Feller condition 2κθ > σ²
Girsanov theorem changes measure Q with Radon-Nikodym dQ/dP = exp{-∫λ dW - (1/2)∫λ² dt}
Jump process N_t Poisson λ, jump size lognormal μ_j=-0.1, σ_j=0.15 for equities
Vasicek model affine term structure P(0,T)=exp{A(T)-B(T)r0}, B(T)=(1-e^{-κT})/κ
Monte Carlo variance reduction antithetic variates halves var for GBM paths
Levy stable α-stable with α=1.7 fits intraday returns, skewness β=-0.1
Cox process doubly stochastic Poisson intensity λ_t follows CIR, for CDOs
Backward Kolmogorov PDE ∂u/∂t + μ ∂u/∂x + (1/2)σ² ∂²u/∂x²=0 for diffusion pricing
HJM framework drift f(t,T)=σ(t,T)∫_t^T σ(t,u)du under risk-neutral
Variance gamma VG(σ=0.12,ν=0.38,θ=-0.14) matches SPX skew
Local stochastic volatility dσ_t = a(t,σ)dt + b(t,σ)dW^σ, calibrated to smile
Affine diffusions admit exp{α(t)+β(t)X_t} mgf solutions, for term structures
Hawkes self-exciting process μ_t = μ + ∫ α e^{-β(t-s)} dN_s, α/β=0.1 for order flow
Rough volatility supOU H=0.15, correlation 0.9 at 1min lag for FX
Filtering Kalman gain K_t = P H^T (H P H^T + R)^{-1} for AR(1) state
Saddlepoint approximation error <1% for barrier option probs vs MC
Markov chain Monte Carlo (MCMC) Metropolis acceptance 40-60% for Heston params
Change of numeraire to T-forward measure dS_t / F_t(0,T) martingale for CMS
Stochastic volatility inspired (SVI) parametrizes slice vol smile k|logK|, ρ=-0.7
Particle filter for SV models tracks 10^4 particles, RMSE 0.5% latent vol

Stochastic Modeling Interpretation

While each model dances elegantly around the chaos of markets—from the simple grace of a lognormal walk to the jagged reality of stable Lévy flights and the self-exciting drama of order flow—the practitioner's true art lies in remembering that every equation, however clever, is still just a guest at reality’s very messy party.

Sources & References

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