Chapter 9 Modelling Volatility and Correlation PDF
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2013
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Chris Brooks
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These are slides from a chapter on modelling volatility and correlation in finance, presented in an introductory econometrics for finance course. The slides cover concepts such as non-linear models, testing for non-linearity, chaos theory and the use of ARCH and GARCH models.
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Chapter 9 Modelling volatility and correlation ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 1 An Excursion into Non-linearity Land Motivation: the linear structural (and time series) models cannot explain a number of important features common to...
Chapter 9 Modelling volatility and correlation ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 1 An Excursion into Non-linearity Land Motivation: the linear structural (and time series) models cannot explain a number of important features common to much financial data - leptokurtosis - volatility clustering or volatility pooling - leverage effects Our “traditional” structural model could be something like: yt = 1 + 2x2t +... + kxkt + ut, or more compactly y = X + u We also assumed that ut N(0,2). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 2 A Sample Financial Asset Returns Time Series Daily S&P 500 Returns for August 2003 – August 2013 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 3 Non-linear Models: A Definition Campbell, Lo and MacKinlay (1997) define a non-linear data generating process as one that can be written yt = f(ut, ut-1, ut-2, …) where ut is an iid error term and f is a non-linear function. They also give a slightly more specific definition as yt = g(ut-1, ut-2, …)+ ut2(ut-1, ut-2, …) where g is a function of past error terms only and 2 is a variance term. Models with nonlinear g( ) are “non-linear in mean”, while those with nonlinear 2( ) are “non-linear in variance”. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 4 Types of non-linear models The linear paradigm is a useful one. Many apparently non-linear relationships can be made linear by a suitable transformation. On the other hand, it is likely that many relationships in finance are intrinsically non-linear. There are many types of non-linear models, e.g. - ARCH / GARCH - switching models - bilinear models ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 5 Testing for Non-linearity – The RESET Test The “traditional” tools of time series analysis (acf’s, spectral analysis) may find no evidence that we could use a linear model, but the data may still not be independent. Portmanteau tests for non-linear dependence have been developed. The simplest is Ramsey’s RESET test, which took the form: ut = 0 + 1 yt2 + 2 yt3 +...+ p −1 ytp + vt Here the dependent variable is the residual series and the independent variables are the squares, cubes, …, of the fitted values. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 6 Testing for Non-linearity – The BDS Test Many other non-linearity tests are available - e.g., the BDS and bispectrum test BDS is a pure hypothesis test. That is, it has as its null hypothesis that the data are pure noise (completely random) It has been argued to have power to detect a variety of departures from randomness – linear or non-linear stochastic processes, deterministic chaos, etc) The BDS test follows a standard normal distribution under the null The test can also be used as a model diagnostic on the residuals to ‘see what is left’ If the proposed model is adequate, the standardised residuals should be white noise. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 7 Chaos Theory Chaos theory is a notion taken from the physical sciences It suggests that there could be a deterministic, non-linear set of equations underlying the behaviour of financial series or markets Such behaviour will appear completely random to the standard statistical tests A positive sighting of chaos implies that while, by definition, long- term forecasting would be futile, short-term forecastability and controllability are possible, at least in theory, since there is some deterministic structure underlying the data Varying definitions of what actually constitutes chaos can be found in the literature. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 8 Detecting Chaos A system is chaotic if it exhibits sensitive dependence on initial conditions (SDIC) So an infinitesimal change is made to the initial conditions (the initial state of the system), then the corresponding change iterated through the system for some arbitrary length of time will grow exponentially The largest Lyapunov exponent is a test for chaos It measures the rate at which information is lost from a system A positive largest Lyapunov exponent implies sensitive dependence, and therefore that evidence of chaos has been obtained Almost without exception, applications of chaos theory to financial markets have been unsuccessful This is probably because financial and economic data are usually far noisier and ‘more random’ than data from other disciplines ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 9 Neural Networks Artificial neural networks (ANNs) are a class of models whose structure is broadly motivated by the way that the brain performs computation ANNs have been widely employed in finance for tackling time series and classification problems Applications have included forecasting financial asset returns, volatility, bankruptcy and takeover prediction Neural networks have virtually no theoretical motivation in finance (they are often termed a ‘black box’) They can fit any functional relationship in the data to an arbitrary degree of accuracy. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 10 Feedforward Neural Networks The most common class of ANN models in finance are known as feedforward network models These have a set of inputs (akin to regressors) linked to one or more outputs (akin to the regressand) via one or more ‘hidden’ or intermediate layers The size and number of hidden layers can be modified to give a closer or less close fit to the data sample A feedforward network with no hidden layers is simply a standard linear regression model Neural network models work best where financial theory has virtually nothing to say about the likely functional form for the relationship between a set of variables. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 11 Neural Networks – Some Disadvantages Neural networks are not very popular in finance and suffer from several problems: – The coefficient estimates from neural networks do not have any real theoretical interpretation – Virtually no diagnostic or specification tests are available for estimated models – They can provide excellent fits in-sample to a given set of ‘training’ data, but typically provide poor out-of-sample forecast accuracy – This usually arises from the tendency of neural networks to fit closely to sample-specific data features and ‘noise’, and so they cannot ‘generalise’ – The non-linear estimation of neural network models can be cumbersome and computationally time-intensive, particularly, for example, if the model must be estimated repeatedly when rolling through a sample. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 12 Models for Volatility Modelling and forecasting stock market volatility has been the subject of vast empirical and theoretical investigation There are a number of motivations for this line of inquiry: – Volatility is one of the most important concepts in finance – Volatility, as measured by the standard deviation or variance of returns, is often used as a crude measure of the total risk of financial assets – Many value-at-risk models for measuring market risk require the estimation or forecast of a volatility parameter – The volatility of stock market prices also enters directly into the Black– Scholes formula for deriving the prices of traded options We will now examine several volatility models. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 13 Historical Volatility The simplest model for volatility is the historical estimate Historical volatility simply involves calculating the variance (or standard deviation) of returns in the usual way over some historical period This then becomes the volatility forecast for all future periods Evidence suggests that the use of volatility predicted from more sophisticated time series models will lead to more accurate forecasts and option valuations Historical volatility is still useful as a benchmark for comparing the forecasting ability of more complex time models ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 14 Heteroscedasticity Revisited An example of a structural model is yt = 1 + 2x2t + 3x3t + 4x4t + u t with ut N(0, u2 ). The assumption that the variance of the errors is constant is known as homoscedasticity, i.e. Var (ut) = u2. What if the variance of the errors is not constant? - heteroscedasticity - would imply that standard error estimates could be wrong. Is the variance of the errors likely to be constant over time? Not for financial data. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 15 Autoregressive Conditionally Heteroscedastic (ARCH) Models So use a model which does not assume that the variance is constant. Recall the definition of the variance of ut: t2= Var(ut ut-1, ut-2,...) = E[(ut-E(ut)) ut-1, ut-2,...] 2 We usually assume that E(ut) = 0 so 2= Var(ut ut-1, ut-2,...) = E[ut2 ut-1, ut-2,...]. t What could the current value of the variance of the errors plausibly depend upon? – Previous squared error terms. This leads to the autoregressive conditionally heteroscedastic model for the variance of the errors: t2= 0 + 1ut2−1 This is known as an ARCH(1) model The ARCH model due to Engle (1982) has proved very useful in finance. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 16 Autoregressive Conditionally Heteroscedastic (ARCH) Models (cont’d) The full model would be yt = 1 + 2x2t +... + kxkt + ut , ut N(0, t2) where t2 = 0 + 1 ut2−1 We can easily extend this to the general case where the error variance depends on q lags of squared errors: t2 = 0 + 1 ut −1 +2 ut −2 +...+qut − q 2 2 2 This is an ARCH(q) model. Instead of calling the variance t2, in the literature it is usually called ht, so the model is yt = 1 + 2x2t +... + kxkt + ut , ut N(0,ht) where ht = 0 + 1 ut2−1 +2 ut2−2 +...+q ut2− q ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 17 Another Way of Writing ARCH Models For illustration, consider an ARCH(1). Instead of the above, we can write yt = 1 + 2x2t +... + kxkt + ut , ut = vtt t = 0 + 1ut2−1 , vt N(0,1) The two are different ways of expressing exactly the same model. The first form is easier to understand while the second form is required for simulating from an ARCH model, for example. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 18 Testing for “ARCH Effects” 1. First, run any postulated linear regression of the form given in the equation above, e.g. yt = 1 + 2x2t +... + kxkt + ut saving the residuals, ût. 2. Then square the residuals, and regress them on q own lags to test for ARCH of order q, i.e. run the regression uˆt2 = 0 + 1uˆt2−1 + 2uˆt2− 2 +... + quˆt2− q + vt where vt is iid. Obtain R2 from this regression 3. The test statistic is defined as TR2 (the number of observations multiplied by the coefficient of multiple correlation) from the last regression, and is distributed as a 2(q). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 19 Testing for “ARCH Effects” (cont’d) 4. The null and alternative hypotheses are H0 : 1 = 0 and 2 = 0 and 3 = 0 and... and q = 0 H1 : 1 0 or 2 0 or 3 0 or... or q 0. If the value of the test statistic is greater than the critical value from the 2 distribution, then reject the null hypothesis. Note that the ARCH test is also sometimes applied directly to returns instead of the residuals from Stage 1 above. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 20 Problems with ARCH(q) Models How do we decide on q? The required value of q might be very large Non-negativity constraints might be violated. – When we estimate an ARCH model, we require i >0 i=1,2,...,q (since variance cannot be negative) A natural extension of an ARCH(q) model which gets around some of these problems is a GARCH model. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 21 Generalised ARCH (GARCH) Models Due to Bollerslev (1986). Allow the conditional variance to be dependent upon previous own lags The variance equation is now t2 = 0 + 1ut2−1 +t-12 (1) This is a GARCH(1,1) model, which is like an ARMA(1,1) model for the variance equation. We could also write t-12 = 0 + 1 ut2−2 +t-22 t-22 = 0 + 1ut2−3 +t-32 Substituting into (1) for t-12 : t2 = 0 + 1ut2−1 +(0 + 1 ut2−2 +t-22) = 0 + 1ut2−1 +0 + 1 ut2−2 +t-22 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 22 Generalised ARCH (GARCH) Models (cont’d) Now substituting into (2) for t-22 t2 =0 + 1ut2−1 +0 + 1 ut2−2 +2(0 + u 1 t −3 +t-3 ) 2 2 t2 = 0 + 1ut2−1 +0 + 1 ut2−2 +02 + 1u2 t2−3 +3t-32 t2 = 0 (1++2) + 1 ut2−1 (1+L+2L2 ) + 3t-32 An infinite number of successive substitutions would yield t2 = 0 (1++2+...) + 1ut2−1 (1+L+2L2+...) + 02 So the GARCH(1,1) model can be written as an infinite order ARCH model. We can again extend the GARCH(1,1) model to a GARCH(p,q): t2 = 0+1ut2−1 +2ut2− 2 +...+qut2−q +1t-12+2t-22+...+pt-p2 q p t2 = 0 + i u + j t − j 2 2 t −i i =1 j =1 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 23 Generalised ARCH (GARCH) Models (cont’d) But in general a GARCH(1,1) model will be sufficient to capture the volatility clustering in the data. Why is GARCH Better than ARCH? - more parsimonious - avoids overfitting - less likely to breech non-negativity constraints ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 24 The Unconditional Variance under the GARCH Specification The unconditional variance of ut is given by 0 Var(ut) = 1 − (1 + ) when 1 + < 1 1 + 1 is termed “non-stationarity” in variance 1 + = 1 is termed intergrated GARCH For non-stationarity in variance, the conditional variance forecasts will not converge on their unconditional value as the horizon increases. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 25 Estimation of ARCH / GARCH Models Since the model is no longer of the usual linear form, we cannot use OLS. We use another technique known as maximum likelihood. The method works by finding the most likely values of the parameters given the actual data. More specifically, we form a log-likelihood function and maximise it. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 26 Estimation of ARCH / GARCH Models (cont’d) The steps involved in actually estimating an ARCH or GARCH model are as follows 1. Specify the appropriate equations for the mean and the variance - e.g. an AR(1)- GARCH(1,1) model: yt = + yt-1 + ut , ut N(0,t2) t2 = 0 + 1ut2−1 +t-12 2. Specify the log-likelihood function to maximise: T 1 T 1 T L = − log( 2 ) − log( t ) − ( yt − − yt −1 ) 2 / t 2 2 2 2 t =1 2 t =1 3. The computer will maximise the function and give parameter values and their standard errors ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 27 Parameter Estimation using Maximum Likelihood Consider the bivariate regression case with homoscedastic errors for simplicity: y t = 1 + 2 xt + u t Assuming that ut N(0,2), then yt N( 1 + 2 xt , 2) so that the probability density function for a normally distributed random variable with this mean and variance is given by 1 1 ( y t − 1 − 2 xt ) 2 (1) f ( y t 1 + 2 xt , ) = 2 exp − 2 2 2 Successive values of yt would trace out the familiar bell-shaped curve. Assuming that ut are iid, then yt will also be iid. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 28 Parameter Estimation using Maximum Likelihood (cont’d) Then the joint pdf for all the y’s can be expressed as a product of the individual density functions f ( y1 , y 2 ,..., yT 1 + 2 X t , 2 ) = f ( y1 1 + 2 X 1 , 2 ) f ( y 2 1 + 2 X 2 , 2 )... f ( yT 1 + 2 X 4 , 2 ) (2) T = f ( yt 1 + 2 X t , 2 ) t =1 Substituting into equation (2) for every yt from equation (1), 1 1 T ( y t − 1 − 2 xt ) 2 f ( y1 , y 2 ,..., yT 1 + 2 xt , ) = T 2 exp − (3) ( 2 ) T 2 t =1 2 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 29 Parameter Estimation using Maximum Likelihood (cont’d) The typical situation we have is that the xt and yt are given and we want to estimate 1, 2, 2. If this is the case, then f( ) is known as the likelihood function, denoted LF(1, 2, 2), so we write 1 1 T ( y t − 1 − 2 xt ) 2 LF ( 1 , 2 , ) = T 2 exp − (4) ( 2 ) T 2 t =1 2 Maximum likelihood estimation involves choosing parameter values (1, 2,2) that maximise this function. We want to differentiate (4) w.r.t. 1, 2,2, but (4) is a product containing T terms. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 30 Parameter Estimation using Maximum Likelihood (cont’d) Since max f ( x) = maxlog( f ( x)) , we can take logs of (4). x x Then, using the various laws for transforming functions containing logarithms, we obtain the log-likelihood function, LLF: T 1 T ( y t − 1 − 2 xt ) 2 LLF = −T log − log( 2 ) − 2 2 t =1 2 which is equivalent to T T 1 T ( y − − x ) 2 LLF = − log 2 − log( 2 ) − t 1 2 t (5) 2 2 2 t =1 2 Differentiating (5) w.r.t. 1, 2,2, we obtain LLF 1 ( y − 1 − 2 xt ).2. − 1 =− t 1 2 2 (6) ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 31 Parameter Estimation using Maximum Likelihood (cont’d) LLF 1 ( yt − 1 − 2 xt ).2. − xt (7) =− 2 2 2 LLF T 1 1 ( y t − 1 − 2 xt ) 2 =− + (8) 2 22 2 4 Setting (6)-(8) to zero to minimise the functions, and putting hats above the parameters to denote the maximum likelihood estimators, From (6), ( y − ˆ − ˆ x ) = 0 t 1 2 t y − ˆ − ˆ x = 0 t 1 2 t y − Tˆ − ˆ x = 0 t 1 2 t 1 ˆ − ˆ 1 T t 1 2 T xt = 0 y − (9) ˆ1 = y − ˆ 2 x ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 32 Parameter Estimation using Maximum Likelihood (cont’d) From (7), ( y − ˆ − ˆ x ) x = 0 t 1 2 t t y x − ˆ x − ˆ x = 0 t t 1 t 2 2 t y x − ˆ x − ˆ x = 0 t t 1 t 2 2 t ˆ x = y x − ( y − ˆ x ) x 2 2 t t t 2 t ˆ x = y x − Txy − ˆ Tx 2 2 t t t 2 2 ˆ 2 ( xt2 −Tx 2 ) = yt xt − Txy ˆ 2 = y x − Txy t t (10) ( x −Tx ) 2 t 2 T 1 From (8), ˆ 2 = ˆ 4 (y t − ˆ1 − ˆ 2 xt ) 2 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 33 Parameter Estimation using Maximum Likelihood (cont’d) Rearranging, ˆ 2 = 1 ( yt − ˆ1 − ˆ 2 xt ) 2 T 1 2 = ut2 (11) T How do these formulae compare with the OLS estimators? (9) & (10) are identical to OLS (11) is different. The OLS estimator was 1 2 = T−k ut2 Therefore the ML estimator of the variance of the disturbances is biased, although it is consistent. But how does this help us in estimating heteroscedastic models? ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 34 Estimation of GARCH Models Using Maximum Likelihood Now we have yt = + yt-1 + ut , ut N(0, t2) t2 = 0 + 1ut2−1 +t-12 T 1 T 1 T L = − log( 2 ) − log( t ) − ( yt − − yt −1 ) 2 / t 2 2 2 2 t =1 2 t =1 Unfortunately, the LLF for a model with time-varying variances cannot be maximised analytically, except in the simplest of cases. So a numerical procedure is used to maximise the log-likelihood function. A potential problem: local optima or multimodalities in the likelihood surface. The way we do the optimisation is: 1. Set up LLF. 2. Use regression to get initial guesses for the mean parameters. 3. Choose some initial guesses for the conditional variance parameters. 4. Specify a convergence criterion - either by criterion or by value. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 35 Non-Normality and Maximum Likelihood Recall that the conditional normality assumption for ut is essential. We can test for normality using the following representation ut = vtt vt N(0,1) ut t = 0 + 1ut −1 + 2 t −1 2 2 v = t t uˆt The sample counterpart is vˆt = ̂ t Are the v̂t normal? Typically v̂t are still leptokurtic, although less so than the ût. Is this a problem? Not really, as we can use the ML with a robust variance/covariance estimator. ML with robust standard errors is called Quasi- Maximum Likelihood or QML. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 36 Extensions to the Basic GARCH Model Since the GARCH model was developed, a huge number of extensions and variants have been proposed. Three of the most important examples are EGARCH, GJR, and GARCH-M models. Problems with GARCH(p,q) Models: - Non-negativity constraints may still be violated - GARCH models cannot account for leverage effects Possible solutions: the exponential GARCH (EGARCH) model or the GJR model, which are asymmetric GARCH models. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 37 The EGARCH Model Suggested by Nelson (1991). The variance equation is given by u t −1 u 2 + t −1 log( t ) = + log( t −1 ) + − 2 2 t −1 2 t −1 2 Advantages of the model - Since we model the log(t2), then even if the parameters are negative, t2 will be positive. - We can account for the leverage effect: if the relationship between volatility and returns is negative, , will be negative. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 38 The GJR Model Due to Glosten, Jaganathan and Runkle t2 = 0 + 1ut2−1 +t-12+ut-12It-1 where It-1 = 1 if ut-1 < 0 = 0 otherwise For a leverage effect, we would see > 0. We require 1 + 0 and 1 0 for non-negativity. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 39 An Example of the use of a GJR Model Using monthly S&P 500 returns, December 1979- June 1998 Estimating a GJR model, we obtain the following results. yt = 0.172 (3.198) t 2 = 1.243 + 0.015 u t2−1 + 0.498 t −1 2 + 0.604 u t2−1 I t −1 (16.372 ) (0.437 ) (14.999 ) (5.772 ) ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 40 News Impact Curves The news impact curve plots the next period volatility (ht) that would arise from various positive and negative values of ut-1, given an estimated model. News Impact Curves for S&P 500 Returns using Coefficients from GARCH and GJR Model Estimates: 0.14 GARCH GJR 0.12 Value of Conditional Variance 0.1 0.08 0.06 0.04 0.02 0 -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Value of Lagged Shock ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 41 GARCH-in Mean We expect a risk to be compensated by a higher return. So why not let the return of a security be partly determined by its risk? Engle, Lilien and Robins (1987) suggested the ARCH-M specification. A GARCH-M model would be yt = + t-1+ ut , ut N(0,t2) t2 = 0 + 1ut2−1 +t-12 can be interpreted as a sort of risk premium. It is possible to combine all or some of these models together to get more complex “hybrid” models - e.g. an ARMA-EGARCH(1,1)-M model. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 42 What Use Are GARCH-type Models? GARCH can model the volatility clustering effect since the conditional variance is autoregressive. Such models can be used to forecast volatility. We could show that Var (yt yt-1, yt-2,...) = Var (ut ut-1, ut-2,...) So modelling t2 will give us models and forecasts for yt as well. Variance forecasts are additive over time. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 43 Forecasting Variances using GARCH Models Producing conditional variance forecasts from GARCH models uses a very similar approach to producing forecasts from ARMA models. It is again an exercise in iterating with the conditional expectations operator. Consider the following GARCH(1,1) model: y t = + u t , ut N(0,t2), t 2 = 0 + 1u t2−1 + t −1 2 What is needed is to generate forecasts of T+12 T, T+22 T,..., T+s2 T where T denotes all information available up to and including observation T. Adding one to each of the time subscripts of the above conditional variance equation, and then two, and then three would yield the following equations T+12 = 0 + 1uT2 +T2 , T+22 = 0 + 1uT+12 +T+12 , T+32 = 0 + 1 uT+2+T+22 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 44 Forecasting Variances using GARCH Models (Cont’d) Let 1f,T be the one step ahead forecast for 2 made at time T. This is 2 easy to calculate since, at time T, the values of all the terms on the RHS are known. 1f,T 2 would be obtained by taking the conditional expectation of the first equation at the bottom of slide 36: 1f,T = 0 + 1uT2 +T2 2 Given, 1f,T how is 2f,T , the 2-step ahead forecast for 2 made at time T, 2 2 calculated? Taking the conditional expectation of the second equation at the bottom of slide 36: 2f,T = 0 + 1E( uT +1 T) + 1f,T 2 2 2 where E( uT +1 T) is the expectation, made at time T, of uT +1, which is 2 2 the squared disturbance term. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 45 Forecasting Variances using GARCH Models (Cont’d) We can write E(uT+12 t) = T+12 But T+12 is not known at time T, so it is replaced with the forecast for it, f 2 , so that the 2-step ahead forecast is given by 1,T 2f,T = 0 + 1 1f,T + 1f,T 2 2 2 f 2 = + ( +) f 2 2,T 0 1 1,T By similar arguments, the 3-step ahead forecast will be given by 3f,T = ET(0 + 1uT+22 + T+22) 2 = 0 + (1+) 2f,T 2 = 0 + (1+)[ 0 + (1+) 1f,T ] 2 = 0 + 0(1+) + (1+)2 1f,T 2 Any s-step ahead forecast (s 2) would be produced by s −1 f 2 = 0 ( 1 + ) i −1 + ( 1 + ) s −1 1f,T 2 s ,T i =1 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 46 What Use Are Volatility Forecasts? 1. Option pricing C = f(S, X, 2, T, rf) 2. Conditional betas im,t i ,t = m2 ,t 3. Dynamic hedge ratios The Hedge Ratio - the size of the futures position to the size of the underlying exposure, i.e. the number of futures contracts to buy or sell per unit of the spot good. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 47 What Use Are Volatility Forecasts? (Cont’d) What is the optimal value of the hedge ratio? Assuming that the objective of hedging is to minimise the variance of the hedged portfolio, the optimal hedge ratio will be given by s h= p F where h = hedge ratio p = correlation coefficient between change in spot price (S) and change in futures price (F) S = standard deviation of S F = standard deviation of F What if the standard deviations and correlation are changing over time? s ,t Use ht = p t F ,t ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 48 Testing Non-linear Restrictions or Testing Hypotheses about Non-linear Models Usual t- and F-tests are still valid in non-linear models, but they are not flexible enough. There are three hypothesis testing procedures based on maximum likelihood principles: Wald, Likelihood Ratio, Lagrange Multiplier. Consider a single parameter, to be estimated, Denote the MLE as ˆ and a restricted estimate as ~. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 49 Likelihood Ratio Tests Estimate under the null hypothesis and under the alternative. Then compare the maximised values of the LLF. So we estimate the unconstrained model and achieve a given maximised value of the LLF, denoted Lu Then estimate the model imposing the constraint(s) and get a new value of the LLF denoted Lr. Which will be bigger? Lr Lu comparable to RRSS URSS The LR test statistic is given by LR = -2(Lr - Lu) 2(m) where m = number of restrictions ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 50 Likelihood Ratio Tests (cont’d) Example: We estimate a GARCH model and obtain a maximised LLF of 66.85. We are interested in testing whether = 0 in the following equation. yt = + yt-1 + ut , ut N(0, t ) 2 t2 = 0 + 1 ut2−1 + t −1 2 We estimate the model imposing the restriction and observe the maximised LLF falls to 64.54. Can we accept the restriction? LR = -2(64.54-66.85) = 4.62. The test follows a 2(1) = 3.84 at 5%, so reject the null. Denoting the maximised value of the LLF by unconstrained ML as L(ˆ) ~ and the constrained optimum as L( ). Then we can illustrate the 3 testing procedures in the following diagram: ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 51 Comparison of Testing Procedures under Maximum Likelihood: Diagramatic Representation L( ) () A L ˆ () ~ B L ~ ˆ ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 52 Hypothesis Testing under Maximum Likelihood The vertical distance forms the basis of the LR test. The Wald test is based on a comparison of the horizontal distance. The LM test compares the slopes of the curve at A and B. We know at the unrestricted MLE, L(ˆ), the slope of the curve is zero. ~ But is it “significantly steep” at L( ) ? This formulation of the test is usually easiest to estimate. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 53 An Example of the Application of GARCH Models - Day & Lewis (1992) Purpose To consider the out of sample forecasting performance of GARCH and EGARCH Models for predicting stock index volatility. Implied volatility is the markets expectation of the “average” level of volatility of an option: Which is better, GARCH or implied volatility? Data Weekly closing prices (Wednesday to Wednesday, and Friday to Friday) for the S&P100 Index option and the underlying 11 March 83 - 31 Dec. 89 Implied volatility is calculated using a non-linear iterative procedure. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 54 The Models The “Base” Models For the conditional mean RMt − RFt = 0 + 1 ht + ut (1) And for the variance ht = 0 + 1ut2−1 + 1ht −1 (2) u t −1 u 2 1/ 2 or ln( ht ) = 0 + 1 ln( ht −1 ) + 1 ( + t −1 − ) (3) ht −1 ht −1 where RMt denotes the return on the market portfolio RFt denotes the risk-free rate ht denotes the conditional variance from the GARCH-type models while t2 denotes the implied variance from option prices. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 55 The Models (cont’d) Add in a lagged value of the implied volatility parameter to equations (2) and (3). (2) becomes ht = 0 + 1ut2−1 + 1ht −1 + t2−1 (4) and (3) becomes u t −1 u 2 1/ 2 ln( ht ) = 0 + 1 ln( ht −1 ) + 1 ( + t −1 − ) + ln( t2−1 ) (5) ht −1 ht −1 We are interested in testing H0 : = 0 in (4) or (5). Also, we want to test H0 : 1 = 0 and 1 = 0 in (4), and H0 : 1 = 0 and 1 = 0 and = 0 and = 0 in (5). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 56 The Models (cont’d) If this second set of restrictions holds, then (4) & (5) collapse to ht2 = 0 + t2−1 (4’) and (3) becomes ln( ht2 ) = 0 + ln( t2−1 ) (5’) We can test all of these restrictions using a likelihood ratio test. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 57 In-sample Likelihood Ratio Test Results: GARCH Versus Implied Volatility R Mt − R Ft = 0 + 1 ht + u t (8.78) ht = 0 + 1u t2−1 + 1 ht −1 (8.79) ht = 0 + 1u t2−1 + 1 ht −1 + t2−1 (8.81) ht2 = 0 + t2−1 (8.81) Equation for 0 1 010-4 1 1 Log-L 2 Variance specification (8.79) 0.0072 0.071 5.428 0.093 0.854 - 767.321 17.77 (0.005) (0.01) (1.65) (0.84) (8.17) (8.81) 0.0015 0.043 2.065 0.266 -0.068 0.318 776.204 - (0.028) (0.02) (2.98) (1.17) (-0.59) (3.00) (8.81) 0.0056 -0.184 0.993 - - 0.581 764.394 23.62 (0.001) (-0.001) (1.50) (2.94) Notes: t-ratios in parentheses, Log-L denotes the maximised value of the log-likelihood function in each case. 2 denotes the value of the test statistic, which follows a 2(1) in the case of (8.81) restricted to (8.79), and a 2 (2) in the case of (8.81) restricted to (8.81). Source: Day and Lewis (1992). Reprinted with the permission of Elsevier Science. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 58 In-sample Likelihood Ratio Test Results: EGARCH Versus Implied Volatility R Mt − R Ft = 0 + 1 ht + u t (8.78) u t −1 u t −1 2 1/ 2 ln( ht ) = 0 + 1 ln( ht −1 ) + 1 ( + − ) (8.80) ht −1 ht −1 u t −1 u t −1 2 1/ 2 ln( ht ) = 0 + 1 ln( ht −1 ) + 1 ( + − ) + ln( t2−1 ) (8.82) ht −1 ht −1 ln( ht2 ) = 0 + ln( t2−1 ) (8.82) Equation for 0 1 010 -4 1 Log-L 2 Variance specification (c) -0.0026 0.094 -3.62 0.529 -0.273 0.357 - 776.436 8.09 (-0.03) (0.25) (-2.90) (3.26) (-4.13) (3.17) (e) 0.0035 -0.076 -2.28 0.373 -0.282 0.210 0.351 780.480 - (0.56) (-0.24) (-1.82) (1.48) (-4.34) (1.89) (1.82) (e) 0.0047 -0.139 -2.76 - - - 0.667 765.034 30.89 (0.71) (-0.43) (-2.30) (4.01) Notes: t-ratios in parentheses, Log-L denotes the maximised value of the log-likelihood function in each case. 2 denotes the value of the test statistic, which follows a 2(1) in the case of (8.82) restricted to (8.80), and a 2 (2) in the case of (8.82) restricted to (8.82). Source: Day and Lewis (1992). Reprinted with the permission of Elsevier Science. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 59 Conclusions for In-sample Model Comparisons & Out-of-Sample Procedure IV has extra incremental power for modelling stock volatility beyond GARCH. But the models do not represent a true test of the predictive ability of IV. So the authors conduct an out of sample forecasting test. There are 729 data points. They use the first 410 to estimate the models, and then make a 1-step ahead forecast of the following week’s volatility. Then they roll the sample forward one observation at a time, constructing a new one step ahead forecast at each step. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 60 Out-of-Sample Forecast Evaluation They evaluate the forecasts in two ways: The first is by regressing the realised volatility series on the forecasts plus a constant: t2+1 = b0 + b1 2ft + t +1 (7) where t +1 is the “actual” value of volatility, and 2ft is the value forecasted 2 for it during period t. Perfectly accurate forecasts imply b0 = 0 and b1 = 1. But what is the “true” value of volatility at time t ? Day & Lewis use 2 measures 1. The square of the weekly return on the index, which they call SR. 2. The variance of the week’s daily returns multiplied by the number of trading days in that week. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 61 Out-of Sample Model Comparisons t2+1 = b0 + b1 2ft + t +1 (8.83) Forecasting Model Proxy for ex b0 b1 R2 post volatility Historic SR 0.0004 0.129 0.094 (5.60) (21.18) Historic WV 0.0005 0.154 0.024 (2.90) (7.58) GARCH SR 0.0002 0.671 0.039 (1.02) (2.10) GARCH WV 0.0002 1.074 0.018 (1.07) (3.34) EGARCH SR 0.0000 1.075 0.022 (0.05) (2.06) EGARCH WV -0.0001 1.529 0.008 (-0.48) (2.58) Implied Volatility SR 0.0022 0.357 0.037 (2.22) (1.82) Implied Volatility WV 0.0005 0.718 0.026 (0.389) (1.95) Notes: Historic refers to the use of a simple historical average of the squared returns to forecast volatility; t-ratios in parentheses; SR and WV refer to the square of the weekly return on the S&P 100, and the variance of the week’s daily returns multiplied by the number of trading days in that week, respectively. Source: Day and Lewis (1992). Reprinted with the permission of Elsevier Science. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 62 Encompassing Test Results: Do the IV Forecasts Encompass those of the GARCH Models? t2+1 = b0 + b1 It2 + b2 Gt 2 + b3 Et 2 + b4 Ht 2 + t +1 (8.86) Forecast comparison b0 b1 b2 b3 b4 R2 -0.00010 0.601 0.298 - - 0.027 Implied vs. GARCH (-0.09) (1.03) (0.42) Implied vs. GARCH 0.00018 0.632 -0.243 - 0.123 0.038 vs. Historical (1.15) (1.02) (-0.28) (7.01) Implied vs. EGARCH -0.00001 0.695 - 0.176 - 0.026 (-0.07) (1.62) (0.27) Implied vs. EGARCH 0.00026 0.590 -0.374 - 0.118 0.038 vs. Historical (1.37) (1.45) (-0.57) (7.74) GARCH vs. EGARCH 0.00005 - 1.070 -0.001 - 0.018 (0.37) (2.78) (-0.00) Notes: t-ratios in parentheses; the ex post measure used in this table is the variance of the week’s daily returns multiplied by the number of trading days in that week. Source: Day and Lewis (1992). Reprinted with the permission of Elsevier Science. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 63 Conclusions of Paper Within sample results suggest that IV contains extra information not contained in the GARCH / EGARCH specifications. Out of sample results suggest that nothing can accurately predict volatility! ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 64 Stochastic Volatility Models It is a common misconception that GARCH-type specifications are stochastic volatility models However, as the name suggests, stochastic volatility models differ from GARCH principally in that the conditional variance equation of a GARCH specification is completely deterministic given all information available up to that of the previous period There is no error term in the variance equation of a GARCH model, only in the mean equation Stochastic volatility models contain a second error term, which enters into the conditional variance equation. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 65 Autoregressive Volatility Models A simple example of a stochastic volatility model is the autoregressive volatility specification This model is simple to understand and simple to estimate, because it requires that we have an observable measure of volatility which is then simply used as any other variable in an autoregressive model The standard Box-Jenkins-type procedures for estimating autoregressive (or ARMA) models can then be applied to this series For example, if the quantity of interest is a daily volatility estimate, we could use squared daily returns, which trivially involves taking a column of observed returns and squaring each observation The model estimated for volatility, t2, is then ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 66 A Stochastic Volatility Model Specification The term ‘stochastic volatility’ is usually associated with a different formulation to the autoregressive volatility model, a possible example of which would be where ηt is another N(0,1) random variable that is independent of ut The volatility is latent rather than observed, and so is modelled indirectly Stochastic volatility models are superior in theory compared with GARCH-type models, but the former are much more complex to estimate. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 67 Covariance Modelling: Motivation A limitation of univariate volatility models is that the fitted conditional variance of each series is entirely independent of all others This is potentially an important limitation for two reasons: – If there are ‘volatility spillovers’ between markets or assets, the univariate model will be mis-specified – It is often the case that the covariances between series are of interest too – The calculation of hedge ratios, portfolio value at risk estimates, CAPM betas, and so on, all require covariances as inputs Multivariate GARCH models can be used for estimation of: – Conditional CAPM betas – Dynamic hedge ratios – Portfolio variances ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 68 Simple Covariance Models: Historical and Implied In exactly the same fashion as for volatility, the historical covariance or correlation between two series can be calculated from a set of historical data Implied covariances can be calculated using options whose payoffs are dependent on more than one underlying asset The relatively small number of such options that exist limits the circumstances in which implied covariances can be calculated Examples include rainbow options, ‘crack spread’ options for different grades of oil, and currency options. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 69 Implied Covariance Models To give an illustration for currency options, the implied variance of the cross-currency returns is given by where and are the implied variances of the x and y returns respectively, and is the implied covariance between x and y So if the implied covariance between USD/DEM and USD/JPY is of interest, then the implied variances of the returns of USD/DEM and USD/JPY and the returns of the cross-currency DEM/JPY are required. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 70 EWMA Covariance Models A EWMA specification gives more weight in the covariance to recent observations than an estimate based on the simple average The EWMA model estimates for variances and covariances at time t in the bivariate setup with two returns series x and y may be written as hij,t = λhij,t−1 + (1 − λ)xt−1yt−1 where i j for the covariances and i = j; x = y for the variances The fitted values for h also become the forecasts for subsequent periods λ (0 < λ < 1) denotes the decay factor determining the relative weights attached to recent versus less recent observations This parameter could be estimated but is often set arbitrarily (e.g., Riskmetrics use a decay factor of 0.97 for monthly data but 0.94 for daily). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 71 EWMA Covariance Models - Limitations This equation can be rewritten as an infinite order function of only the returns by successively substituting out the covariances: The EWMA model is a restricted version of an integrated GARCH (IGARCH) specification, and it does not guarantee the fitted variance- covariance matrix to be positive definite EWMA models also cannot allow for the observed mean reversion in the volatilities or covariances of asset returns that is particularly prevalent at lower frequencies. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 72 Multivariate GARCH Models Multivariate GARCH models are used to estimate and to forecast covariances and correlations. The basic formulation is similar to that of the GARCH model, but where the covariances as well as the variances are permitted to be time-varying. There are 3 main classes of multivariate GARCH formulation that are widely used: VECH, diagonal VECH and BEKK. VECH and Diagonal VECH e.g. suppose that there are two variables used in the model. The conditional covariance matrix is denoted Ht, and would be 2 2. Ht and VECH(Ht) are h11t h11t h12t Ht = VECH ( H t ) = h22t h21t h22t h12t ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 73 VECH and Diagonal VECH In the case of the VECH, the conditional variances and covariances would each depend upon lagged values of all of the variances and covariances and on lags of the squares of both error terms and their cross products. In matrix form, it would be written VECH (H t ) = C + A VECH ( t −1t −1 ) + B VECH (H t −1 ) t t −1 ~ N (0, H t ) Writing out all of the elements gives the 3 equations as h11t = c11 + a11u12t + a12 u 22t + a13u1t u 2 t + b11h11t −1 + b12 h22 t −1 + b13 h12 t −1 h22 t = c21 + a 21u12t + a 22 u 22t + a 23u1t u 2 t + b21h11t −1 + b22 h22 t −1 + b23 h12 t −1 h12 t = c31 + a 31u12t + a 32 u 22t + a 33u1t u 2 t + b31h11t −1 + b32 h22 t −1 + b33 h12 t −1 Such a model would be hard to estimate. The diagonal VECH is much simpler and is specified, in the 2 variable case, as follows: h11t = 0 + 1u12t −1 + 2 h11t −1 h22t = 0 + 1u 22t −1 + 2 h22t −1 h12t = 0 + 1u1t −1u 2t −1 + 2 h12t −1 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 74 BEKK and Model Estimation for M-GARCH Neither the VECH nor the diagonal VECH ensure a positive definite variance-covariance matrix. An alternative approach is the BEKK model (Engle & Kroner, 1995). The BEKK Model uses a Quadratic form for the parameter matrices to ensure a positive definite variance / covariance matrix Ht. In matrix form, the BEKK model is H t = W W + AH t −1 A + Bt −1t −1B Model estimation for all classes of multivariate GARCH model is again performed using maximum likelihood with the following LLF: ( ) = − TN 2 1 T ( log 2 − log H t + t' H t−1 t 2 t =1 ) where N is the number of variables in the system (assumed 2 above), is a vector containing all of the parameters, and T is the number of obs. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 75 Correlation Models and the CCC The correlations between a pair of series at each point in time can be constructed by dividing the conditional covariances by the product of the conditional standard deviations from a VECH or BEKK model A subtly different approach would be to model the dynamics for the correlations directly In the constant conditional correlation (CCC) model, the correlations between the disturbances to be fixed through time Thus, although the conditional covariances are not fixed, they are tied to the variances The conditional variances in the fixed correlation model are identical to those of a set of univariate GARCH specifications (although they are estimated jointly): ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 76 More on the CCC The off-diagonal elements of Ht, hij,t (i j), are defined indirectly via the correlations, denoted ρij: Is it empirically plausible to assume that the correlations are constant through time? Several tests of this assumption have been developed, including a test based on the information matrix due and a Lagrange Multiplier test There is evidence against constant correlations, particularly in the context of stock returns. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 77 The Dynamic Conditional Correlation Model Several different formulations of the dynamic conditional correlation (DCC) model are available, but a popular specification is due to Engle (2002) The model is related to the CCC formulation but where the correlations are allowed to vary over time. Define the variance-covariance matrix, Ht, as Ht = DtRtDt Dt is a diagonal matrix containing the conditional standard deviations (i.e. the square roots of the conditional variances from univariate GARCH model estimations on each of the N individual series) on the leading diagonal Rt is the conditional correlation matrix Numerous parameterisations of Rt are possible, including an exponential smoothing approach ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 78 The DCC Model – A Possible Specification A possible specification is of the MGARCH form: where: S is the unconditional correlation matrix of the vector of standardised residuals (from the first stage estimation), ut = Dt−1ϵt ι is a vector of ones Qt is an N × N symmetric positive definite variance-covariance matrix ◦ denotes the Hadamard or element-by-element matrix multiplication procedure This specification for the intercept term simplifies estimation and reduces the number of parameters. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 79 The DCC Model – A Possible Specification Engle (2002) proposes a GARCH-esque formulation for dynamically modelling Qt with the conditional correlation matrix, Rt then constructed as where diag(·) denotes a matrix comprising the main diagonal elements of (·) and Q∗ is a matrix that takes the square roots of each element in Q This operation is effectively taking the covariances in Qt and dividing them by the product of the appropriate standard deviations in Qt∗ to create a matrix of correlations. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 80 DCC Model Estimation The model may be estimated in a single stage using ML although this will be difficult. So Engle advocates a two-stage procedure where each variable in the system is first modelled separately as a univariate GARCH A joint log-likelihood function for this stage could be constructed, which would simply be the sum (over N) of all of the log-likelihoods for the individual GARCH models In the second stage, the conditional likelihood is maximised with respect to any unknown parameters in the correlation matrix The log-likelihood function for the second stage estimation will be of the form where θ1 and θ2 denote the parameters to be estimated in the 1st and 2nd stages respectively. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 81 Asymmetric Multivariate GARCH Asymmetric models have become very popular in empirical applications, where the conditional variances and / or covariances are permitted to react differently to positive and negative innovations of the same magnitude In the multivariate context, this is usually achieved in the Glosten et al. (1993) framework Kroner and Ng (1998), for example, suggest the following extension to the BEKK formulation (with obvious related modifications for the VECH or diagonal VECH models) where zt−1 is an N-dimensional column vector with elements taking the value −ϵt−1 if the corresponding element of ϵt−1 is negative and zero otherwise. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 82 An Example: Estimating a Time-Varying Hedge Ratio for FTSE Stock Index Returns (Brooks, Henry and Persand, 2002). Data comprises 3580 daily observations on the FTSE 100 stock index and stock index futures contract spanning the period 1 January 1985 - 9 April 1999. Several competing models for determining the optimal hedge ratio are constructed. Define the hedge ratio as . – No hedge (=0) – Naïve hedge (=1) – Multivariate GARCH hedges: Symmetric BEKK Asymmetric BEKK In both cases, estimating the OHR involves forming a 1-step ahead forecast and computing hCF ,t +1 OHRt +1 = − t hF ,t +1 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 83 OHR Results In Sample Unhedged Naïve Hedge Symmetric Time Asymmetric =0 =1 Varying Time Varying Hedge Hedge hFC ,t hFC ,t t = t = h F ,t h F ,t Return 0.0389 -0.0003 0.0061 0.0060 {2.3713} {-0.0351} {0.9562} {0.9580} Variance 0.8286 0.1718 0.1240 0.1211 Out of Sample Unhedged Naïve Hedge Symmetric Time Asymmetric =0 =1 Varying Time Varying Hedge Hedge hFC ,t hFC ,t t = t = h F ,t h F ,t Return 0.0819 -0.0004 0.0120 0.0140 {1.4958} {0.0216} {0.7761} {0.9083} Variance 1.4972 0.1696 0.1186 0.1188 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 84 Plot of the OHR from Multivariate GARCH Time Varying Hedge Ratios 1.00 0.95 Conclusions 0.90 - OHR is time-varying and less than 1 0.85 - M-GARCH OHR provides a better hedge, both in-sample 0.80 and out-of-sample. - No role in calculating OHR for 0.75 asymmetries 0.70 0.65 500 1000 1500 2000 2500 3000 Symmetric BEKK Asymmetric BEKK ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 85
