A potentially useful approach to model nonlinearities in time series is to assume different behavior (structural break) in different subsamples

Transcription

1 1.3 Regime switching models A potentially useful approach to model nonlinearities in time series is to assume different behavior (structural break) in different subsamples (or regimes). If the dates, the regimes switches have taken place, are known, modeling can be worked out simply with dummy variables. 43

2 Consider the following regression model (38) where y t = x t β St + u t, t =1,...,T, (39) (40) (41) u t NID(0,σ 2 S t ), β St = β 0 (1 S t )+β 1 S t, σ 2 S t = σ 2 0 (1 S t)+σ 2 1 S t, and (42) S t =0or 1, (Regime 0 or 1). Thus, under regime 1 the coefficient parameter vector is β 1 and error variance σ

3 For the sake of simplicity, consider an AR(1) model. That is, x t =(1,y t 1 ). Usually it is assumed that the possible difference between the regimes is a mean and/or a volatility shift, but no change in the autoregression parameter. That is, (43) y t = μ St + φ 1 (y t 1 μ st 1 )+u t, with (44) u t NID(0,σ 2 S t ), where μ St = μ 0 (1 S t )+μ 1 S t and σ 2 S t as defined above. If S t, t =1,...,T is known a priori, then the problem is just a usual dummy variable autoregression problem. 45

4 In practice, however, the prevailing regime is not usually directly observable. Denote then (45) P (S t = j S t 1 = i) =p ij, i, j = 0, 1, called transition probabilities, with p i0 + p i1 =1, i =0, 1. This kind of process, where the next state depends only on the previous state, is called the Markov process, and the model a Markov switching model in the mean and variance. Thus, in this model additional parameters to be estimated are the transition probabilities p ij. Usually the parameters are estimated (numerically) by the ML method. For a detailed discussion, see Kim Chang-Jin and Charles A. Nelson (1999). State Space Models with Regime Switching. Classical and Gibbs-Sampling Approaches with Applications. MIT-Press. 46

5 The joint probability density function for y t,s t,s t 1, given past information F t 1 = {y t 1,y t 2,...}, is f(y t,s t,s t 1 F t 1 )=f(y t S t,s t 1, F t 1 )P (S t,s t 1 F t 1 ), (46) with f(y t S t,s t 1, F t 1 )= (47) { } 1 exp [y t μ St φ 1 (y t 1 μ St 1 )] 2 2πσ 2 2σ 2. St S t 47

6 Then the log-likelihood function to be maximized with respect to the unknown parameters is (48) where l t (θ) = log 1 l(θ) = T t=1 l t (θ), 1 f(y t S t,s t 1, F t 1 )P [S t,s t 1 F t 1 ], (49) S t =0 S t 1 =0 (50) θ =(p, q, μ 0,μ 1,φ 1,σ 2 0,σ2 1 ), with (51) (52) p = P [S t =0 S t 1 =0], q = P [S t =1 S t 1 =1], being the transition probabilities. 48

7 In order to evaluate the log-likelihood function we need to define the joint probabilities P [S t,s t 1 F t 1 ]. Because of the Markov property (53) we can write (54) P [S t S t 1, F t 1 ]=P [S t S t 1 ], P [S t,s t 1 F t 1 ]=P [S t S t 1 ]P [S t 1 F t 1 ], and the problem reduces to calculating (estimating) the time dependent state probabilities, P [S t 1 F t 1 ], and weight them with the transition probabilities to obtain the joint probability. 49

8 This can be achieved as follows: First, let P [S 0 =1 F 0 ]=P [S 0 =1]=π be given (then P [S 0 =0]=1 π). Then the probabilities P [S t 1 F t 1 ] and the joint probabilities are obtained using the following two steps algorithm 1 0 Given P [S t 1 = i F t 1 ], i =0, 1, at the beginning of time t (t th iteration), P [S t = j, S t 1 = i F t 1 ]=P [S t = j S t 1 = i]p [S t 1 = i F t 1 ], (55) 2 0 Once y t is observed, we update the information set F t = {F t 1,y t } and the probabilities P [S t = j, S t 1 = i F t ]=P [S t = j, S t 1 = i F t 1,y t ] = f(s t=i,s t 1 =j,y t F t 1 ) f(y t F t 1 ) = f(y t S t =j,s t 1 =i,f t 1 )P [S t =j,s t 1 =i F t 1 ] 1 f(y t s t,s t 1,F t 1 )P [S t =s t,s t 1 =s t 1 F t 1 ] s t,s t 1 =0 (56) with 1 P [S t = s t F t ]= P [S t = s t,s t 1 = s t 1 F t ]. (57) s t 1 =0 50

9 Once we have the joint probability for the time point t, we can calculate the likelihood l t (θ). The maximum likelihood estimates for θ is then obtained iteratively maximizing the likelihood function by updating the likelihood function at each iteration with the above algorithm. 51

10 Steady state probabilities The probabilities π = P [S 0 =1 F 0 ] is called the steady state probability, and, given the transition probabilities p and q, is obtained as (58) π = P [S 0 =1 F 0 ]= 1 p 2 p q. Note that in the two state Markov chain P [S 0 =0 F 0 ]=1 P [S 0 =1 F 0 ]= (59) 1 q 2 p q. 52

11 Smoothed probabilities Recall that the state S t is unobserved. However, once we have estimated the model, we can make inferences on S t using all the information from the sample. This gives us (60) P [S t = j F T ], j =0, 1, which are called the smoothed probabilities (for details, see Kim and Nelson 1999, pp ). Remark 1.6: In the estimation procedure we derived P [S t = j F t ] that are usually called the filtered probabilities. 53

12 Expected duration The expected length the system is going to stay in state j can be calculated from the transition probabilities. Let D denote the number of periods the system is in state j. The probabilities are easily found to be equal to P [D = k] =p k 1 jj (1 p jj ), so that (61) E[D] = k=1 kp[d = k] = 1 1 p jj. Note that in our case p 00 = p and p 11 = q. Example 1.8: Are there long swings in the dollar/sterling exchange rate? If the exchange rate x t is RW with long swings, it can be modeled as Δx t = α 0 + α 1 S t + ɛ t, so that Δx 1 N(μ 0,σ 2 0 ) when S t = 0 and Δx t N(μ 1,σ 2 1 ), when S t = 1, where μ 0 = α 0 and μ 1 = α 0 +α 1. Parameters μ 0 and μ 1 constitute two different drifts (if α 1 0) in the random walk model. 54

13 Estimating the model from quarterly data for sample period 1972I to 1996IV gives Parameter Estimate Std err μ μ σ σ p (regime 1) q (regime 0) The expected length of stay in regime 0 is given by 1/(1 p) =7.0 quarters, and in regime 1 1/(1 q) = 7.5 quarters. 55

14 Example 1.9: Suppose we are interested whether the market risk of a share is dependent on the level of volatility on the market. In the CAPM world the market risk of a stock is measured by β World and Finnish Returns Finnish Returns World Returns Consider for the sake of simplicity only the cases of high and low volatility. 56

15 The market model is y t = α St + β St x t + ɛ t, where α St = α 0 (1 S t )+α 1 S t, β St = β 0 (1 S t )+β 1 S t and ɛ t N(0,σS 2 t ) with σt 2 = σ2 0 (1 S t)+σ1 2S t. Estimating the model yields Parameter Estimate Std Err t-value p-value α 0 (low) α 1 (high) β 0 (low) β 1 (high) σ0 2 σ1 2 (low) (high) State Prob P (High High) P (Low High) P (High Low) P (Low Low) P (High) P (Low) Log-likelihood The empirical results give evidence that the stock s market risk depends on the level of stock volatility. The expected duration of high volatility is 1/(1.9642) 27 days, and for low volatility 59 days. 57

16 Market returns with high-low volatility probabilities 58