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Given historic data of stock prices and a state model for volatility, we are applying nonlinear filtering methods to estimate conditional probability density function of stochastic volatility at time given sigma algebra generated by all stock prices up to time . Numerical methods for nonlinear filtering based on Strang splitting scheme are proposed and numerical simulations are presented. Method for evaluation and comparison of different schemes is proposed based on value at risk (VaR) calculations.*| <r<@     `  c $A )??  )`  c $A *??p  *x  <A ,??`~  ,`  c $A -??` 7  -H  0޽h ? ̙33  x p P@ (  @ @ <  Z References 0(   @ <0j $[1] W. F. Ames, Numerical Methods for Partial Differential Equations, Academic Press, London, 1992. [2] R. Frey and W. J. Runggaldier, A nonlinear filtering approach to volatility estimation with a view towards high frequency data, preprint, to appear in International Journal of Theoretical and Applied Finance. [3] A. Kolmogoroff, Zufllige bewegungen, Annals of Mathematics, Volume 35, No. 1, 1934. [4] A. N. Shiryaev, Essentials of Stochastic Finance, World Scientific, New Jersey, 1999. [5] I. Karatzas and S.E. Shreve, Methods of Mathematical Finance, Springer, New York, 1998. [6] J. Ma and J. Yong, Forward-Backward Stochastic Differential Equations and Their Applications, Springer, New York, 1999. [7] J. Cvitani, R. Liptser, and B. Rozovskii, Tracking Volatility, In Proc. 39th IEEE Conf. On Decision and Control, Sydney, 2000. >BhoM!P=RLI   Z           D  Y     #                * H @ 0޽h ? ̙33g    `p ( @  p p <×`` @ dContinuous Model,( p 0Dʗ@` B  Let be probability space with complete filtration , Wiener process with respect to the filtration , and progressively measurable processes. Let price process (stock price) satisfies Black-Scholes type model that allows stochastic volatility i.e. for we have (see [7]) Further we consider stochastic process which represents amount of money investor puts into the stock with price and let represents interest rate of the riskless asset and let wealth process satisfies (see [6])@)     n ` p c $A7 l??@$ l` p c $A m??@Aj m` p c $A" n??0 n`  p c $A ??0^u `  p c $A% ?? g `  p c $A ?? ! ` p c $A$ ?? @ ` p c $A# ??0 ` p c $A& F??6 F` p c $A' G??0   G` p c $A& H?? p,  H` p c $A( I??P ~  I` p c $A, J?? r  J` p c $A* K??@ `  K` p c $A) L??  LH p 0޽h ? ̙339    pty (  t t <ۗcp O Assumptions ,   t <ߗ  XWe assume , and which for gives (using It s formula) (for this model see [4]) where are the so called i.i.d. random variables. We can consider as observed data. Considering discrete observation we have that the change of wealth is also discrete i.e. we have where and&   `  t c $A+ O??  O`  t c $A- P??(Pp 1 P`  t c $A. Q?? V Q`  t c $A/ T??P 0 T` t c $A1 U?? \ U` t c $A0 V?? ) V` t c $A W?? W j W` t c $A2 Y?? F  Y` t c $A Z?? @  Z` t c $A \?? `  \` t c $A ]??` p  ]H t 0޽h ? ̙33  c [  ( o    <P1 J WAssumptions (Cont.)(    <$@PB  bWe assume that we do not borrow the money and that we do not sell shares which we do not own at the moment i.e. we have constraint Generally we assume such that satisfies It s differential equation i.e. we have that is solution of where is Wiener process with respect to filtration and and are suitable functions. We consider special case of the so called constant velocity tracking (in radar tracking) i.e. we have where&  R `  c $A ??  `  c $A5 _?? ' _`  c $A4 `?? . ``   c $A4 b??p b`   c $A6 c??  c`   c $A3 e??   e`   c $A i??`   i`   c $A! j??   j`  c $A8 l?? @  l`  c $A9 m??0 p i  m`  c $A: n??`  n`  c $AA  ??   H  0޽h ? ̙33& 3&+&DG%(    <` o!Simulation Examples vs. Real Data"!(< " X  0AO?`` 4  # @  C 4B  #   tBCUDE FUU@4B  # 4B  # 4B  # 4B  # 4B  #   6 (  U0$<  :B  3 :B  3   6(  V50$<  :B  3 :B  3   6$(  W100$<  :B  3 :B  3   6(  W150$<  :B  3 :B  3   6 (  W200$<  :B  3 :B  3   6#(  W250$<  :B  3 :B ! 3  " 6X)(  W300$<  :B # 3 :B $@ 3  % 6<-L X0.95$<  :B & 3 :B '@ 3  ( 62. U1$<  :B ) 3 78:B *@ 3 78 + 67L_ X1.05$<  :B , 3 ij:B -@ 3 ij . 6;Le W1.1$<  :B / 3 :B 0@ 3  1 60@L X1.15$<  :B 2 3 :B 3@ 3  4 6De W1.2$<  :B 5 3 :B 6@ 3  7 6IL( X1.25$<  :B 8 3  9 tBCUDE FUU@:B : 3  ; |BLCDEF_c M4 I(F+y/y6Q:7AcEIL7O?WZ0^eipt{%Z "%{-Z0^8e;p?VFAJZQVU{\l_{gVjZnuy 7|y\44nuuB #'*l2L5=@GKRVZadl"oMwzl{se6+!(,37:BvEMPWn[bfrjYqJty|<8 "r1vUr  &"&!-P1T8<mC:GLN[RbUP\/`(gkrv^} N8k}+HD ,Q$(/f2u6c=nA?HfLu  @]R < s *A p` = c $AT ?? 0M R > s * p ? TO ?   O Simulation:    |B @ TD?`  R A s *@ `` B c $AP ??P 7m  C TT ? `R  O Simulation:    |B D TD? @ R E s * F TX ?  m)Daily stock price in 1995 for ATT stock ** * |B G TD? H  0޽h ? ̙33  (      <`b z qValue at Risk (VaR)(&   R  < ??` ]  `   c $A= ??p   `  c $AL ??   H  0޽h ? ̙33  A 9 (    <r F z YValue at Risk (Cont.)(    <w@ &B  Let is conditional probability density of given Therefore we have Since and is strictly decreasing function on such that there exists a unique such that Then the optimal portfolio is  `  c $AB ??@ `  c $AQ ??  `  c $AS ??  r `  c $AR ??@  `  c $AC ?? v  `   c $AI ?? J  `   c $AW z??0L  z`   c $AE {??ppn  {`   c $AY }??@ PN  }`  c $AZ ~??@ x  ~`  c $A\ ??0 Pp@  `  c $A[ ?? ` `  c $AD ?? p   `  c $AM ??   H  0޽h ? ̙33 $(     <HP  YValue at risk (Cont.)(    <6  ;The problem is to approximate i.e. the conditional probability density of given . The approximation of will be done at the grid points i.e. Let where we want to find the maximal such that  `  c $A] ??  `  c $A^ ??   `  c $AJ ??  `  c $A` ??  `  c $Aa ??P  `   c $Ab ??` `   c $Ad ??`P  `   c $Ac ??S  `   c $AK ??    `   c $Ae ?? ,  `  c $AN ??  H  0޽h ? ̙33  P(     <NI  ZPredictor for Tracking(  r  <̛ Transitional probability density function of the process defined by (1) is the fundamental solution of the Fokker-Planck equation Therefore to give an estimate of the conditional probability density defined for any nonnegative or bounded Borel function by the following equality we approximate solution of (5) on interval with initial condition @k     `  c $Af ??@ `  c $A_ ??@ `  c $AV ??A `  c $Ai  ??p   `  c $AU  ??P `,   `   c $Ak  ??    `   c $AX ??   H  0޽h ? ̙33X (   `  c $Aj ?? x   <,  bPredictor for Tracking (Cont.)(  R  <ܮ0 wL  For model we have prediction conditional probability density function given by which is defined by This is the so called prediction step.  `  c $Ah ??   `  c $Al ??0  H  0޽h ? ̙33v &(    <0   [Correction for Tracking(    <L0s ;For correction in tracking Bayes rule can be applied i.e. Since we have In calculations instead of we take Therefore the problem is how to approximate the solution of (5) as simple and fast as possible. &   `  c $Aq ?? `  c $As ??` P  `  c $A ?? `  c $Ao  ??0 `>   `  c $An !??0 K  !H  0޽h ? ̙33 ld( o    <f  UNumerical Schemes(    <.  _Let and For the exact solution at we have where  ` `  c $At ??`Ppa `  c $A ??`P @l `  c $Au ??pP  `  c $Av ??  `  c $Aw ?? m  H  0޽h ? ̙33;  {(    <Ng ]Numerical Schemes (Cont.)(    <t0@  :We have the following two approximations The approximation in (6) corresponds to Strang scheme by separately solving in given order the following PDE s@V  ;   `  c $Ay ??P1 `  c $A| ??` `  c $Am ??3I `  c $Ap ??`@ `  c $Az ?? y  `   c $Ax ?? p9  H  0޽h ? ̙33    K(     <`Nf ]Numerical Schemes (Cont.)(    <4P  f$Generalization of Strang s scheme is given by for prescribed we have The finite difference corresponding to (8) is given by where&   x `  c $A~ ??P `  c $A ??:  `  c $A ??  `  c $A ??@  `  c $A ?? @@  `   c $A ??` @i H  0޽h ? ̙33 910 (     <P@  ]Numerical Schemes (Cont.)(  P  <tJf  Let for From the expression for the fundamental solution of (5) (see [3]) we have that the exact solution of (5), with initial condition expressed by and is given by   `  c $A ??0@ `  c $A ??  `  c $A ??, c  `  c $A ??   `  c $A ??R X}  `   c $A ?? ` H  0޽h ? ̙33  e ]  (   '   <<  ]Numerical Schemes (Cont.)(    <dA  FChecking Strang s splitting vs. simple operator splitting. Both schemes converge with increasing m and refinement of space grid. vs. Strang s scheme shows higher accuracy in time variable t.@      2 X  0A ?`, X  0A ? `0 X  0A? 0X  0A?pp\ v  N ?  `   TI ?@ o :  rStrang s scheme   v  N ?``  TM ?@0: f simple operator splitting scheme!! ! |B  TD? |B @ TD? 0 v  N ?P   TTQ ?N  Winitial condition  |B @ TD?`Pv  N ?p   T\U ?+  S true solution  |B  TD? p H  0޽h ? ̙33 d\` (      T(w ?``! @ hSimulation Example 1,   Hu ?`z HHere we consider stochastic volatility model given with Stochastic Differential Equation (1) and Initial conditional probability density function is given by We consider two types of estimators: maximum probability estimator and standard conditional probability estimator (or only prob. estim.) We assume that 10 percent of wealth can be lost with probability at most 0.1 and we invest according to portfolio calculated by (3). l',L# " / `   c $A !?? 7* !`   c $A ,??p ,`   c $A '??` '`   c $A *??p@  *`   c $A -??p   -H   0޽h ? ̙33 p&(    T\ ?`@ Simulation Example 1 (Cont.),4p  HA ?P ` p  HA ? T p  HA ?0 P p  HA ?$ H  0޽h ? ̙33 D<P (    <)   Simulation Example 2(>     <)  FFor simulated observations we used where and we used the tracker from Example 1with     `  c $A ??`  `  c $A /??P /`  c $A 3?? 3R  s *A0A 5R  s *A0 XH  0޽h ? ̙33 }(  p  HA ?`  W  T̐ ?!p 9Real Data (from slide Simulation Examples vs. Real Data) (: ,/,N ! R  s *A R  s *A@ pH R  s *A l @ tH  0޽h ? ̙33 91( C+   <<)N M Real data (    <A)@  d<Here we considered ATT daily stocks prices i.e. average of daily high and low, for the year 1995, collected by Reuters and then we applied our nonlinear filtering method to estimate and track the volatility. Some previous work with simulated data showed that for bigger uncertainty parameter in the description of the model the max-probability estimator is shown to be more close to the true value of the volatility. This is generally the case when max-probability estimator and conditional expectation estimators differ significantly which is the case in what follows. = H  0޽h ? ̙33 /'(     <Xe), N Conclusion (    <tj)zf  YWe have the new method of estimating volatility of prices. The method is robust with respect to the model which governs the volatility. The method does not require huge historical data to estimate the volatility. The method can estimate time-varying volatility and model varying volatility. We assume that method in conjunction with other models can be very powerful tool for estimating volatility and therefore enable us to better estimate VaR, CaR, and other financial instruments used in modern finance. @    ; H  0޽h ? ̙33 ?7(     <u)P~  YFurther possibilities(    <z)`uH ^Applying our method to continuous SDE s which are coming from already known discrete models (GARCH). Developing suitable correlation techniques for portfolio optimization i.e. possible application of our method to correlation prediction for better accessing the investment. Passively track a stock index (DAX). Optimal control of exchange rates and/or price of a stock. Optimal control for portfolio with transaction costs. 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The method is robust with respect to the model which governs the volatility i.e. it can perform well even though volatility fits better to different model. The method does not require huge historical data to estimate the volatility. It gives conditional probability distribution function of volatility at any time in future for given sigma algebra of observation. This gives us possibility to calculate value at risk (VaR) for any future investment with better precision and accuracy depending on how well model coresponds to reality. The method is satisfactory with respect to VaR calculated portfolio on given simulation and real data examples. It is derived and proposed method for evaluation and comparisons of different models and methods for estimaton of volatility. 3 T  M E    (' | H  0޽h ? ̙33q  !  (     <u)  J YFurther possibilities(    <z)pP$ @JApplication of the method to continuous SDE s which are coming from already known discrete models (e.g. GARCH). Developing suitable correlation tracking techniques for accurate portfolio optimization i.e. possible application of our method to prediction of time-nonhomogeneous correlation coefficients for better assesing investments (diversification). Passively track a stock indexes (DAX). Optimal control of exchange rates and/or price of a stock (modeling diffusion of news into market). Optimal control for portfolio with transaction costs. 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We assume that 10 percent of wealth can be lost with probability at most 0.1 and we invest according to portfolio calculated by (3). @)     `   c $A !?? 7* !`   c $A ,??p ,`   c $A '??` '`   c $A *??p@  *`   c $A -??p   -H   0޽h ? ̙33 p(    TM ?`@ Simulation Example 1 (Cont.),*   p  HA ?P ` p  HA ? T p  HA ?0 P p  HA ?$ H  0޽h ? ̙33 tl (    <   VSimulation Example 2(    <  For simulated observations we used where and we used the tracker from Example 1with   `  c $A ??`  `  c $A /??P /`  c $A 3?? 3R  s *A0A 5R  s *A0 XH  0޽h ? ̙33 aY(  p  HA ?`  3  T ?!p 9Real Data (from slide Simulation Examples vs. Real Data) (: ,/,*)    R  s *A R  s *A@ pH R  s *A l @ tH  0޽h ? ̙33 (     <J L Conclusion (    <@ MWe have the new method of estimating volatility of stock prices. The method is robust with respect to the model which governs the volatility i.e. it can perform well even though volatility fits better to different model. The method does not require huge historical data to estimate the volatility. It gives conditional probability distribution function of volatility at any time in future for given sigma algebra of observation. This gives us possibility to calculate value at risk (VaR) for any future investment with better precision and accuracy depending on how well model corresponds to reality. The method is satisfactory with respect to VaR calculated portfolio on given simulation and real data examples. It is derived and proposed method for evaluation and comparisons of different models and methods for estimation of volatility. J      H  0޽h ? ̙33 }u (     <  J WFurther possibilities(     <pP$ LApplication of the method to continuous SDE s which are coming from already known discrete models (e.g. GARCH). 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