Nonparametric Risk Management with Generalized Hyperbolic Distributions

Transcription

1 Nonparametric Risk Management with Generalized Hyperbolic Distributions Ying Chen Wolfgang Härdle Center for Applied Statistics and Economics Institut für Statistik and Ökonometrie Humboldt-Universität zu Berlin

2 Motivation 1-1 A Stylized Fact in Financial Markets Estimated density (nonparametric) Estimated log density (nonparametric) Y Y X X Figure 1: Densities (left) and log-densities (right) of the devolatilized return of daily DEM/USD FX rate from 1979/12/01 to 1994/04/01 (3720 observations). The kernel density estimate of the residuals (red) and the normal density (blue) with ĥ = 0.55 (rule of thumb). fx.xpl

3 Motivation 1-2 A Stylized Fact in Financial Markets Estimated density (nonparametric) Estimated log density (nonparametric) Y*E Y X X Figure 2: Densities (left) and log-densities (right) of the devolatilized DEM/USD return on the basis of GARCH(1,1) fit: ˆσ 2 t = 1.65e r 2 t σ2 t 1 + εt. The kernel density estimate of the residuals (red) and the normal density (blue) with ĥ = 2.17 (rule of thumb). garch.xpl

4 Motivation 1-3 Risk Management Models Heteroscedastic model R t = σ t ε t, t = 1, 2, R t (log) return, σ t volatility, ε t i.i.d. stochastic term. Typical assumptions 1 The stochastic term is normally distributed, ε t N(0, 1). 2 A time-homogeneous structure of volatility: ARCH model, Engle (1995) GARCH model, Bollerslev (1995) Stochastic volatility model, Harvey, Ruiz and Shephard (1995)

5 Motivation 1-4 Improvements A The generalized hyperbolic (GH) distribution family fits the empirical distribution observed in financial markets. Hyperbolic (HYP) distribution in finance, Eberlein and Keller (1995), GH distribution + (parametric) stochastic volatility model, Eberlein, Kallsen and Kristen (2003). B A time inhomogeneous model yields precise volatility estimation. Adaptive volatility estimation + normal distribution, Mercurio and Spokoiny (2004). Combine A & B!

6 Motivation 1-5 Motivation GH distribution + adaptive volatility estimation () 1. Adaptive volatility technique to estimate the volatility σ t by ˆσ t 2. Standardize the returns using ε t = R t ˆσ t 3. Maximum likelihood to estimate the parameters (λ, α, β, δ, µ) of GH distribution 4. Apply to risk measurement, e.g. Value at Risk (VaR) and TailVaR. Backtesting: performs better than a model based on normal distribution.

7 Motivation 1-6 Outline 1. Motivation 2. Generalized hyperbolic (GH) distribution and its maximum likelihood (ML) estimation 3. Adaptive volatility estimation 4. Standardized (devolatilized) returns 5. VaR applications 6. Multivariate VaR and independent component analysis (ICA) All calculations may be replicated in XploRe.

8 GH Distribution 2-1 Generalized Hyperbolic (GH) Distribution X GH with density: f GH (x; λ, α, β, δ, µ) = { (ι/δ)λ K λ 1/2 α } δ 2 + (x µ) 2 2πKλ (δι) { δ 2 + (x µ) 2/ } 1/2 λ e β(x µ) α Where ι 2 = α 2 β 2, K λ ( ) is the modified Bessel function of the third kind with index λ: K λ (x) = y λ 1 exp{ x 2 (y + y 1 )} dy Furthermore, the following conditions must be fulfilled: δ 0, β < α if λ > 0 δ > 0, β < α if λ = 0 δ > 0, β α if λ < 0

9 GH Distribution 2-2 GH parameters µ, δ µ and δ control the location and the scale. E[X ] = µ + δ2 β δι K λ+1 (δι) K λ (δι) { Var[X ] = δ 2 Kλ+1 (δι) διk λ (δι) + (β ι )2 [ K λ+2(δι) { K λ+1(δι) K λ (δι) K λ (δι) }2 ] The term in the big brackets is location and scale invariant. }

10 GH Distribution 2-3 GH parameter mu GH parameter delta Y Y X X Figure 3: The GH pdf (black) with λ = 0.5, α = 3, β = 0, δ = 1 and µ = 2. The left red line is obtained for µ = 3 and the right red line is for δ = 2 holding the other parameters constant. ghexample.xpl

11 GH Distribution 2-4 GH parameters β β describes the skewness. For a symmetric distribution β = 0 according to the following lemma. Lemma The linear transformation Y = ax + b of X GH is again GH distributed with parameters λ Y = λ, α Y = α/ a, β Y = β/ a, δ Y = δ a and µ Y = aµ + b. f GH (y = x; λ, α, β, δ, µ) β=0 = f GH (x; λ, α, β, δ, µ)

12 GH Distribution 2-5 GH parameter beta (n) GH parameter beta (p) Y Y X X Figure 4: The GH pdf (black) with λ = 0.5, α = 3, β = 0, δ = 2 and µ = 2. The left red line is obtained for µ = 3 and the right red line is for δ = 2 holding the other parameters constant. ghexample.xpl

13 Y GH Distribution 2-6 GH parameters α α has an effect on kurtosis. GH parameter alpha X Figure 5: The GH pdf (black) with λ = 0.5, α = 6, β = 0, δ = 1 and µ = 2. The left red line is obtained for µ = 3 and the right red line is for δ = 2 holding the other parameters constant. ghexample.xpl

14 GH Distribution 2-7 Subclass of GH distribution The parameters (µ, δ, β, α) can be interpreted as trend, riskiness, asymmetry and the likeliness of extreme events. Hyperbolic (HYP) distributions: λ = 1, f HYP (x; α, β, δ, µ) = ι 2αδK 1 (δι) e{ α δ 2 +(x µ) 2 +β(x µ)}, (1) where x, µ IR, 0 δ and β < α. Normal-inverse Gaussian (NIG) distributions: λ = 1/2, f NIG (x; α, β, δ, µ) = αδ π where x, µ IR, 0 < δ and β α. K 1 { α δ 2 + (x µ) 2 } δ 2 + (x µ) 2 e {δι+β(x µ)}. (2)

15 GH Distribution 2-8 Tail behavior of GH distribution f GH (x; λ, α, β, δ, µ = 0) x λ 1 e (α β)x as x, (3) where a(x) b(x) as x means that both a(x)/b(x) and b(x)/a(x) are bounded as x. Comparison with other distributions: Normal distribution: f Normal = 1 (x µ) 2 ς (2π) e 2ς 2 Laplace distribution: f Laplace = 1 2ς e x µ /ς Cauchy distribution: f Cauchy = 1 ςπ[1+(x M) 2 /ς 2 ] where µ is the location parameter, ς is the scale parameter and M is the Median.

16 GH Distribution 2-9 Distribution comparison Tail comparison Y NIG Laplace Normal Cauchy Y*E Cauchy Laplace NIG Normal X X Figure 6: Graphical comparison of the NIG distribution (line), standard normal distribution (dashed), Laplace distribution (dotted) and Cauchy distribution (dots). tail.xpl

17 GH Distribution 2-10 Maximum Likelihood (ML) Estimation L HYP = n log ι n log 2 n log α n log δ n log K 1 (δι) n + { α δ 2 + (x t µ) 2 + β(x t µ)} t=1 L NIG = n log α + n log δ n log π + nδι n { } + [log K 1 α δ 2 + (x t µ) 2 + t=1 n β(x t µ) t=1 1 ] 2 log{δ2 + (x t µ) 2 }

18 GH Distribution 2-11 Example 1: DEM/USD exchange rate VaR timeplot returns*e time*e3 Figure 7: The time plot of VaR forecasts using EMA (green) and RMA (blue) and the associated changes (dots) of the P&L of the DEM/USD rates. Exceptions are marked in red. revartimeplot.xpl SFEVaRbank.xpl

19 Y Y GH Distribution 2-12 ML estimators of HYP distribution: ˆα = 1.744, ˆβ = 0.017, ˆδ = and ˆµ = Estimated fx density (HYP) Estimated fx log density (HYP) X X Figure 8: The estimated density (left) and log density (right) of the standardize returns of FX rates (red) with nonparametric kernel (ĥ = 0.55) and a simulated HYP density (blue) with the maximum likelihood estimators. fx.xpl

20 GH Distribution 2-13 The HYP likelihood surface w.r.t. β and µ on the basis of DEM/USD data. HYP lohlikelihood fct wrt beta and mu Figure 9: The partial HYP likelihood surface of the standardize returns of FX rates, the largest ML is marked in red. liksurf.xpl

21 Y Y GH Distribution 2-14 ML estimators of NIG distribution: ˆα = 1.340, ˆβ =, ˆδ = and ˆµ =. Estimated fx density (NIG) Estimated fx log density (NIG) X X Figure 10: The estimated density (left) and log density (right) of the standardize return of FX rates (red) with nonparametric kernel (ĥ = 0.55) and a simulated NIG density (blue) with the maximum likelihood estimators. fx.xpl

22 GH Distribution 2-15 Example 2: A German bank portfolio (kupfer.dat) VaR timeplot returns*e time*e3 Figure 11: The time plot of VaR forecasts using EMA (green) and RMA (blue) and the associated changes (dots) of the P&L of the German bank portfolio. Exceptions are marked in red. revartimeplot.xpl

23 Y Y GH Distribution 2-16 Estimated density (nonparametric) Estimated log density (nonparametric) X X Figure 12: Graphical comparison of densities (left) and log-densities (right) of a German bank portfolio rate (5603 observations). The kernel density estimate of the standardized residuals (red) and the normal density (blue) with ĥ = 0.61 (rule of thumb). kupfer.xpl

24 Y Y GH Distribution 2-17 ML estimators of HYP distribution: ˆα = 1.819, ˆβ = 0.168, ˆδ = and ˆµ = Estimated bank portfolio density (HYP) Estimated bank portfolio log density (HYP) X -5 0 X Figure 13: The estimated density (left) and log density (right) of the standardize return of bank portfolio rates (red) with nonparametric kernel (ĥ = 0.61) and a simulated HYP density (blue) with the maximum likelihood estimators. kupfer.xpl

25 Y Y GH Distribution 2-18 ML estimators of NIG distribution: ˆα = 1.415, ˆβ = 0.171, ˆδ = and ˆµ = Estimated bank portfolio density (NIG) Estimated bank portfolio log density (NIG) X X Figure 14: The estimated density (left) and log density (right) of the standardize return of bank portfolio rates (red) with nonparametric kernel (ĥ = 0.61) and a simulated NIG density (blue) with the maximum likelihood estimators. kupfer.xpl

26 Adaptive Volatility 3-1 Adaptive Volatility Estimation Adaptive Volatility Estimation Assumption: For a fixed point τ, volatility is locally time-homogeneous in a short time interval [τ m, τ), thus we can estimate ˆσ τ 2 = ˆσ I 2 = 1 I I = [τ m, τ). t I R2 t, where I is the number of observations in τ m τ n

27 Adaptive Volatility 3-2 Adaptive Volatility Estimation Adaptive Volatility Estimation For a fixed point τ, volatility is locally time-homogeneous in a short time interval [τ m, τ), thus we can estimate ˆσ τ 2 = ˆσ I 2 = 1 I I = [τ m, τ). t I R2 t, where I is the number of observations in τ m τ n Questions: How to estimate the volatility? How to specify the time homogeneous interval?

28 Adaptive Volatility 3-3 Volatility estimation Volatility estimation Power transformation yields lighter tails. The random variable R t γ is distributed more evenly. For every γ > 0, we have E( R t γ F t 1 ) = σt γ E( ε t γ F t 1 ) = C γ σt γ E[( R t γ C γ σt γ ) 2 F t 1 ] = σt 2γ E[( ε t γ C γ ) 2 F t 1 ] = σt 2γ Dγ 2 R t γ = C γ σ γ t + D γ σ γ t ζ t, (4) where C γ is the conditional mean and D 2 γ the conditional variance of ε γ and ζ t = ( ε t γ C γ )/D γ is i.i.d. with mean 0.

29 Adaptive Volatility 3-4 Denote by θ t = C γ σt γ the conditional mean of R t γ. In a time-homogeneous interval I, the constant θ I = C γ σ γ I estimated by ˆθ I : ˆθ I = 1 R t γ. I t I can be We employ the nearly constant θ I to determine the length of the interval I.

30 Adaptive Volatility 3-5 Use R t γ = θ t + D γ σ γ t ζ t : ˆθ I = 1 I t I Var[ˆθ I F t ] = s2 γ I 2 E θt 2 t I R t γ = 1 I t I θ t + s γ I θ t ζ t. t I where s γ = D γ /C γ. We denote vi 2 variance of ˆθ I, whose estimator is ˆv I = = s2 γ I 2 E t I θ2 t, the conditional s γ I 1/2 ˆθ I.

31 Adaptive Volatility 3-6 Time homogeneous interval Split interval I into J I and I \J I. τ m I \J J τ 1 Denote ˆθ I \J and ˆθ J as the estimators of the subintervals in the time-homogeneous interval I. Then the deviation = ˆθ I \J ˆθ J must be small. ˆθ I \J ˆθ J T I,τ. (5) where T I,τ is an unknown critical value in the homogeneity test. I = max {I : I fulfills (7)}.

32 Adaptive Volatility 3-7 Time homogeneous interval τ m I \J J τ 1 ˆθ I \J ˆθ J T I,τ. Questions: How to estimate the volatility? How to specify the time homogeneous interval? How to choose γ? How to specify T I,τ?

33 Adaptive Volatility 3-8 Homogeneity test Lemma For every 0 γ 1 there exists a constant a γ > 0 such that log E e uζγ a γu 2 2, where ζ γ = ( ε t γ C γ )/D γ and ε is a GH distributed stochastic term. If Lemma 2 holds, then Υ t = exp ( t s=1 p sζ s (a γ /2) t ) s=1 p2 s is a supermartingale, where p s is a predictable process w.r.t. the information set F t 1. Since:

34 Adaptive Volatility 3-9 E(Υ t F t 1 ) Υ t 1 = E(Υ t F t 1 ) E(Υ t 1 F t 1 ) ( t ) ( t t 1 t 1 = E[exp p s ζ s (a γ /2) exp p s ζ s (a γ /2) = E[exp = 0 s=1 ( t 1 s=1 exp(p 1 ζ 1 ) exp(a γ /2p 1 ) } {{ } 1,Lemma2 i.e. E(Υ t F t 1 ) Υ t 1. s=1 t 1 p s ζ s (a γ /2) s=1 p 2 s p 2 s exp(p t 1ζ t 1 ) exp(a γ /2p t 1 ) } {{ } 1 ) s=1 s=1 p 2 s (exp(p t ζ t a γ /2p 2 t ) 1) F t 1 ] exp(p t ζ t ) E[ 1 F t 1 ] exp(a γ /2p t ) }{{} 1 )

35 Adaptive Volatility 3-10 Theorem If R 1,..., R τ obey the heteroscedastic model and the volatility coefficient σ t satisfies the condition b σ 2 t bb with some positive constant b and B, then given a large value η it holds for the estimate ˆθ I of θ τ : P( ˆθ I θ τ > I (1 + ηs γ I 1/2 ) + ηˆv I ) 4 eη(1 + logb) exp( ). 2a γ(1+ηs γ I 1/2 ) 2 η 2 where I is the bias defined as 2 I = I 1 t I (θ t θ τ ) 2. Theorem 1 indicates that the estimation error ˆθ I θ τ is small relative to ηˆv I ( T I,τ ) for τ I with a high probability, since in a time homogeneous interval the squared bias I is negligible.

36 Adaptive Volatility 3-11 Test homogeneity: Under homogeneity ˆθ I θ τ is bounded by ηˆv I provided that η is sufficiently large. ˆθ I θ τ ηˆv I Based on the triangle inequality, we get: ˆθ I \J ˆθ J is bounded by η(ˆv I \J + ˆv J ) for J I, i.e. ˆθ I \J ˆθ J T I,τ = η(ˆv I \J + ˆv J ) = η ( ˆθ J 2 J 1 + ˆθ I 2 \J I \J 1 ), where η = ηs γ.

37 Adaptive Volatility 3-12 Test homogeneity: Under homogeneity ˆθ I θ τ is bounded by ηˆv I provided that η is sufficiently large. ˆθ I θ τ < ηˆv I ˆθ I \J ˆθ J T I,τ = η(ˆv I \J + ˆv J ) = η ( ˆθ J 2 J 1 + ˆθ I 2 \J I \J 1 ), Questions: How to estimate the volatility? How to specify the time homogeneous interval? How to specify T I,τ? How to choose γ? How to choose η?

38 Adaptive Volatility 3-13 Cross-validation (CV) method: τ 1 ( 2}, η = argmin{ R t γ ˆθ (t,η )) t=t 0 where t 0 is the starting point. Choice of transformation parameter γ: we choose γ = 0.5 to compare with the normal distribution based model.

39 Adaptive Volatility 3-14 Cross-validation (CV) method: τ 1 ( 2}, η = argmin{ R t γ ˆθ (t,η )) t=t 0 Choice of transformation parameter γ: we choose γ = 0.5 to compare with the normal distribution based model. Questions: How to estimate the volatility? How to specify the time homogeneous interval? How to choose γ? How to choose η?

40 Adaptive Volatility 3-15 Iteration Start from a short homogeneous interval [τ m 0, τ 1], the algorithm consists of 4 steps. Step 1: At τ 1, enlarge the interval I from [τ m 0, τ) to [τ k m 0, τ), i.e. m = k m 0. The parameters m 0 and k are integers specified according to data. Values of m 0 = 5 and k = 2 are recommended.

41 Adaptive Volatility 3-16 Step 2: Inside interval I, do multiple homogeneity tests based on subintervals J = [τ 1 3 m, τ) until J = [τ 2 3m, τ). J τ m τ 2/3m τ 1/3m τ 1 Step 3: If homogeneity hypothesis is rejected at point s, the loop stops. Otherwise go back to Step 1. Step 4: Do Step 1 to Step 3 for t [t 0, τ 1] with different η s. Choose η that gives the minimal global forecast error. τ 1 t=t 0 ( R t γ ˆθ (t,η )) 2. J

42 Simulation 4-1 Simulation Goal: Estimate the local volatility using. Simulation: 200 processes with HYP and NIG distribution with (α, β, δ, µ) = (2, 0, 1, 0) respectively. Each process has T = 1000 observations. Parameters: starting point t 0 = 201, power transformation parameter γ = 0.5, m 0 = 5 and k = 2.

43 Simulation 4-2 Case 1: 200 simulated HYP random variables and 0.01 : 1 t 400 σ 1,t = 0.05 : 400 < t : 750 < t 1000 Case 2: 200 simulated NIG random variables 0.02t 5 : 1 t 300 σ 2,t = 0.02t 10 : 300 < t t 100 : 600 < t 1000

44 Simulation 4-3 sim 115 Y*E X*E2 Figure 15: The estimated local volatilities for simulation 115 (HYP). sim1.xpl

45 Simulation 4-4 sim 44 Y X*E2 Figure 16: The estimated local volatilities for simulation 44 (NIG). sim2.xpl

46 Simulation 4-5 Sensitivity Analysis: Define a percentage rule that tells us after how many steps a sudden jump is detected at 40%, 50% or 60% of the jump level. mean std max min σ 1 : Detection delay to the first jump at t = % rule % rule % rule σ 1 : Detection delay to the second jump at t = % rule % rule % rule Table 1: Descriptive statistics for the detection delay of the sudden vola jumps.

47 Simulation 4-6 mean std max min σ 2 : Detection delay to the first jump at t = % rule % rule % rule σ 2 : Detection delay to the first jump at t = % rule % rule % rule Table 2: (Continued) Descriptive statistics for the detection delay of the sudden vola jumps.

48 Empirical Study 5-1 Data set and devolatilized return DEM/USD bank portfolio period to observations mean std skewness kurtosis Table 3: Descriptive statistics for the standardized residuals of DEM/USD data and bank portfolio data. The data sets are available in

49 Empirical Study 5-2 Daily DEM/USD returns from 1979/12/01 to 1994/04/01 Y*E X*E3 GARCH(1,1) vola estimators (exchange rate) Y*E X*E3 Figure 17: The return process of DEM/USD exchange rates (top), the GARCH(1,1) (ˆσ t 2 = 1.65e r t σ2 t 1 + εt) volatility estimates (bottom). garch.xpl

50 Empirical Study 5-3 Adaptive local constant volatility estimators (exchange rate) Y Y*E Figure 18: X*E2 Length of homogeneous intervals X*E2 The adaptive volatility estimates (top) and the lengths of the homogeneous intervals for t 0 = 501, η = 1.06 and m 0 = 5. The average length of time homogeneous interval is 51. fx.xpl

51 Empirical Study 5-4 Daily returns of a German bank Y*E X*E3 Adaptive local constant volatility estimators (bank portfolio) Y*E X*E3 Length of homogeneous intervals Y*E Figure 19: X*E3 The return process of a German bank s portfolio (upper), its adaptive volatility estimates (middle) for t 0 = 501, η = 1.23 and m 0 = 5, the lengths of the homogeneous intervals (bottom). The average length of time homogeneous interval is 72. kupfer.xpl

52 Empirical Study Exchange rate 2. Bank portfolio Figure 20: Boxplots of the DEM/USD exchange rates (left) and the German bank portfolio data (right). The mean values of the homogeneous interval length are 51 for DEM/USD and 72 for bank portfolio data. boxplot.xpl

53 Empirical Study 5-6 Value at Risk (VaR) q p is the p-th quantile of the distribution of ε t, i.e. P(ε t < q p ) = p. P(R t < σ t q p F t 1 ) = p VaR p,t = σ t q p ˆσ t are estimated by the described adaptive procedure. q p is given by the quantile of the HYP or NIG distribution

54 Empirical Study 5-7 VARs Parameters estimation is based on the previous 500 observations (standardized returns), which varies little Figure 21: Quantiles based on DEM/USD data vary over time. From the top the evolving HYP quantiles for p = 0.995, p = 0.99, p = 0.975, p = 0.95, p = 0.90, p = 0.10, p = 0.05, p = 0.025, p = 0.01, p =.

55 Empirical Study 5-8 model vs. normal model (a) p = Y*E X*E2 Figure 22: Value at Risk forecast plots for DEM/USD data. (a) p =. Dots denote the exchange rate returns. Exceptions relative to the HYP quantile are displayed as +. The blue line is the VaR forecasts based on while those based on the normality is colored in yellow. fxvar.xpl

56 Empirical Study 5-9 (b) p = 0.01 Y*E X*E2 Figure 23: Value at Risk forecast plots for DEM/USD data. (b) p = 0.01.Dots denote the exchange rate returns. Exceptions relative to the HYP quantile are displayed as +. The blue line is the VaR forecasts based on while those based on the normality is colored in yellow. fxvar.xpl

57 Empirical Study 5-10 (c) p = Y*E X*E2 Figure 24: Value at Risk forecast plots for DEM/USD data. (c) p = Dots denote the exchange rate returns. Exceptions relative to the HYP quantile are displayed as +. The blue line is the VaR forecasts based on while those based on the normality is colored in yellow. fxvar.xpl

58 Empirical Study 5-11 (d) p = 0.05 Y*E X*E2 Figure 25: Value at Risk forecast plots for DEM/USD data. (d)p = 0.05.Dots denote the exchange rate returns. Exceptions relative to the HYP quantile are displayed as +. The blue line is the VaR forecasts based on while those based on the normality is colored in yellow. fxvar.xpl

59 Empirical Study 5-12 Backtesting VaR Testing VaR levels: H 0 : E N = pt vs. H 1 : not H 0 (6) where N is the number of the exceptions on the basis of T observations. Likelihood ratio statistic: LR1 = 2 log { (1 p) T N p N} + 2 log { (1 N/T ) T N (N/T ) N}, where LR1 is asymptotically χ 2 (1) distributed

60 Empirical Study 5-13 Let I t denote the indicator of exceptions at time point t, t = 1,..., T, π ij = P(I t = j I t 1 = i) be the transition probability with i, j = 0 or 1, and n ij = T t=1 I (I t = j I t 1 = i), i, j = 0, 1. Testing Independence: H 0 : π 00 = π 10 = π, π 01 = π 11 = 1 π vs. H 1 : not H 0 Likelihood ratio statistic: LR2 = 2 log {ˆπ n 0 (1 ˆπ) n } log {ˆπ n } ˆπn ˆπn ˆπn 11 11, where ˆπ ij = n ij /(n ij + n i,1 j ), n j = n 0j + n 1j, and ˆπ = n 0 /(n 0 + n 1 ). Under H 0, LR2 is asymptotically χ 2 (1) distributed as well.

61 Empirical Study 5-14 Model p N/T LR1 p-value LR2 p-value Normal * HYP NIG Table 4: Backtesting results for DEM/USD example. * indicates the rejection.

62 Empirical Study 5-15 Model p N/T LR1 p-value LR2 p-value Normal * * HYP NIG Table 5: Backtesting results for bank portfolio example.

63 Outlook 6-1 Multivariate VaR Background: Multivariate GH distribution, Prause(1999), Schmidt, Hrycej and Stützle(2003). First two principal components and multinormal distribution, Härdle, Herwatz and Spokoiny(2003). New idea: Independent Component Analysis (ICA) + univariate

64 Outlook 6-2 ICA Definition of ICA: Observation vector x t = (x 1,t,, x d,t ) Independent vector s t = (s 1,t,, s d,t ) : assumption in ICA - the components s i are statistically independent, i = 1,, d. Unknown mixing matrix A d d dimensions: x = As

65 Y Y Outlook 6-3 Example: Two independent components ( s 1 and ) s 2 are uniform 2 3 distributed. The mixing matrix A = X X Figure 26: The joint distribution of the independent components s 1 and s 2. Below is the joint distribution of the observed mixtures x 1 and x 2. icaexample.xpl

66 Outlook 6-4 BASF-Bayer returns ICA: BASF-Bayer Figure 27: Independent component analysis on the basis of Allianz and Bayer returns from to ica.xpl

67 Outlook 6-5 Conclusion The adaptive volatility estimation method by Mercurio and Spokoiny (2004) is applicable to a general model with generalized hyperbolic innovations. The critical value can be chosen by cross-validation method. The distribution of the devolatilized returns from the adaptive volatility estimation is found to be leptokurtic and, sometimes, asymmetric. We found that the distribution of innovations can be perfectly modelled by the class of generalized hyperbolic distributions.

68 Outlook 6-6 The proposed approach can be applied easily to risk measures such as value at risk, expected shortfall, and so on. We have got a justification of the proposed appoach for use in risk management by backtestings of value at risk model applied to real data.

69 References 7-1 References Barndorff-Nielsen, O. (1977). Exponentially Decreasing Distributions for the Logarithm of Particle Size, Proceedings of the Royal Society of London A 353, Barndorff-Nielsen, O. (1997). Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling, Scandinavian Journal of Statistics 24, Bibby, B. M. and Sørensen, M. (2001). Hyperbolic Processes in Finance, Technical Report 88, University of Aarhus, Aarhus School of Business. Bollerslev, T. (1995). Generalized autoregressive conditional heteroskedasticity, ARCH, selected readings, Oxford University Press.

70 References 7-2 Christoffersen, P. F. (1998). Evaluating Interval Forecast, International Economic Review 39, Eberlein, E. and Keller, U. (1995). Hyperbolic Distributions in Finance, Bernoulli 1, Eberlein, E., Kallsen, J. and Kristen, J. (2003). Risk Management Based on Stochastic Volatility, Journal of Risk 5, Engle, R. F. (1995). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation, ARCH, selected readings, Oxford University Press. Härdle, W., Herwartz, H. and Spokoiny, V. (2003). Time Inhomogeneous Multiple Volatility Modelling, Journal of Financial Econometrics 1, Härdle, W., Kleinow, T. and Stahl, G. (2002). Applied Quantitative Finance, Springer-Verlag Berlin Heidelberg New York.

71 References 7-3 Harvey, A., Ruiz, E. and Shephard N. (1995). Multivariate stochastic variance models, ARCH, selected readings, Oxford University Press. Hyvärinen, A., Karhunen, J. and Oja, E. (2001). Independet Component Analysis, John Willey & Sons, INC. Jorion, P. (2001). Value at Risk, McGraw-Hill. Liptser, R. and Spokoiny, V. (2000). Deviation Probability Bound for Martingales with Applications to Statistical Estimation, Statistics and Probability Letters 46, Mercurio, D. and Spokoiny, V. (2004). Statistical Inference for Time Inhomogeneous Volatility Models, Annals of Statistics 32,

72 References 7-4 Mercurio, D. and Spokoiny, V. (2004). Volatility Estimation via Local Change Point Analysis with Applications to Value-at-Risk, working paper. Prause, K. (1999). The Generalized Hyperbolic Model: Estimation, Financial Derivatives and Risk Measures, dissertation. Schmidt, R., Hrycej, T. and Stützle, E. (2003). Multivariate Distribution Models with Generalized Hyperbolic Margins, working paper.