"Frontmatter". In: Analysis of Financial Time Series

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276 VALUEATRISKk p (q) =− 1 [ ]ln(2) ln −r(q) + r (2q)−r (2q) + r (4q)k h (q) = −1q(7.22)q∑ {ln[−r(i) ]−ln[−r (q+1) ] } , (7.23)i=1where the argument (q) is used to emphasize that the estimators depend on q. Thechoice of q differs between Hill and Pickands estimators. It has been investigatedby several researchers, but there is no general consensus on the best choice available.Dekkers and De Haan (1989) show that k p (q) is consistent if q increases at aproperly chosen pace with the sample size T . In addition, √ q[k p (q)−k] is asymptoticallynormal with mean zero and variance k 2 (2 −2k+1 + 1)/[2(2 −k − 1) ln(2)] 2 .TheHill estimator is applicable to the Fréchet distribution only, but it is more efficientthan the Pickands estimator when applicable. Goldie and Smith (1987) show that√ q[kh (q)−k] is asymptotically normal with mean zero and variance k 2 . In practice,one may plot the Hill estimator k h (q) against q and find a proper q such that theestimate appears to be stable.(a) Monthly maximum log returnsmax2 4 6 8 10 121970 1980 1990 2000year(b) Monthly minimum log returnsmin-25 -20 -15 -10 -5 01970 1980 1990 2000Figure 7.3. Maximum and minimum daily log returns of IBM stock when the subperiod is 21trading days. The data span is from July 3, 1962 to December 31, 1998: (a) positive returns,and (b) negative returns.year
EXTREME VALUE THEORY 2777.5.3 Application to Stock ReturnsWe apply the extreme value theory to the daily log returns of IBM stock from July 3,1962 to December 31, 1998. The returns are measured in percentages, and the samplesize is 9190 (i.e., T = 9190). Figure 7.3 shows the time plots of extreme daily logreturns when the length of the subperiod is 21, which corresponds approximately toa month. The October 1987 crash is clearly seen from the plot. Excluding the 1987crash, the range of extreme daily log returns is between 0.5% and 13%.Table 7.1 summarizes some estimation results of the shape parameter k via theHill estimator. Three choices of q are reported in the table, and the results are stable.To provide an overall picture of the performance of Hill estimator, Figure 7.4 showsTable 7.1. Results of the Hill Estimator for Daily Log Returnsof IBM Stock From July 3, 1962 to December 31, 1998. StandardErrors are in Parentheses.q 190 200 210Maximum −0.300(0.022) −0.297(0.021) −0.303(0.021)Minimum −0.290(0.021) −0.292(0.021) −0.289(0.020)(a) Upper (or right) tailhill-0.30 -0.20 -0.10••• •• • ••• • • ••• •• • • •• •••0 100 200 300 400 500q(b) Lower (or left) tailhill-0.8 -0.6 -0.4• • •••••• • •• • ••0 100 200 300 400 500qFigure 7.4. Scatterplots of the Hill estimator for the daily log returns of IBM stock. Thesample period is from July 3, 1962 to December 31, 1998: (a) positive returns, and (b) negativereturns.
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Analysis of FinancialTime SeriesFin
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ContentsPrefacexi1. Financial Time
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CONTENTSix6.6 Black-Scholes Pricing
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PrefaceThis book grew out of an MBA
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Analysis of Financial Time Series.
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ASSET RETURNS 3Multiperiod Simple R
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ASSET RETURNS 5r t [k] =ln(1 + R t
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DISTRIBUTIONAL PROPERTIES OF RETURN
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10 FINANCIAL TIME SERIES AND THEIR
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REFERENCES 21Federal Reserve Bank o
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CORRELATION AND AUTOCORRELATION FUN
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CORRELATION AND AUTOCORRELATION FUN
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WHITE NOISE AND LINEAR TIME SERIES
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SIMPLE AUTOREGRESSIVE MODELS 29whic
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SIMPLE AUTOREGRESSIVE MODELS 31wher
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SIMPLE AUTOREGRESSIVE MODELS 33wher
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SIMPLE AUTOREGRESSIVE MODELS 35expa
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SIMPLE AUTOREGRESSIVE MODELS 37Tabl
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SIMPLE AUTOREGRESSIVE MODELS 39Mode
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SIMPLE AUTOREGRESSIVE MODELS 41The
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SIMPLE MOVING-AVERAGE MODELS 43wher
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SIMPLE MOVING-AVERAGE MODELS 45s-rt
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SIMPLE MOVING-AVERAGE MODELS 47The
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SIMPLE ARMA MODELS 49A time series
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SIMPLE ARMA MODELS 51operator, the
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SIMPLE ARMA MODELS 53Table 2.5. Sam
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SIMPLE ARMA MODELS 55This represent
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UNIT-ROOT NONSTATIONARITY 57ˆp h (
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UNIT-ROOT NONSTATIONARITY 59log-pri
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SEASONAL MODELS 61which is commonly
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SEASONAL MODELS 63Series : xSeries
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SEASONAL MODELS 65models use the sa
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• ••• •••••••
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REGRESSION MODELS WITH TIME SERIES
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REGRESSION MODELS WITH TIME SERIES
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LONG-MEMORY MODELS 73=(k − d −
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APPENDIX A: SOME SCA COMMANDS 75est
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EXERCISES 77relation. Draw your con
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STRUCTURE OF A MODEL 813.2 STRUCTUR
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THE ARCH MODEL 83corrected asset re
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THE ARCH MODEL 85Series : xACF0.0 0
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THE ARCH MODEL 87sample mean from t
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THE ARCH MODEL 89where Ɣ(x) is the
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THE ARCH MODEL 91Series : resiSerie
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THE GARCH MODEL 93other softwares a
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THE GARCH MODEL 95ahead forecast ca
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THE GARCH MODEL 97The fitted AR(3)
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THE GARCH MODEL 99Series : stdatACF
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THE GARCH-M MODEL 101two models. Th
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THE EXPONENTIAL GARCH MODEL 103To b
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THE EXPONENTIAL GARCH MODEL 105eros
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THE CHARMA MODEL 107ˆσ 2 864 (3)
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RANDOM COEFFICIENT AUTOREGRESSIVE M
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THE LONG-MEMORY STOCHASTIC VOLATILI
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AN ALTERNATIVE APPROACH 113where Va
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APPLICATION 115(a) IBMlog-rtn-30 -1
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APPLICATION 117equationThe fitted m
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KURTOSIS OF GARCH MODELS 119where K
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SOME RATS PROGRAMS FOR ESTIMATING V
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EXERCISES 123• Is there evidence
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REFERENCES 125Melino, A., and Turnb
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NONLINEAR TIME SERIES 127To put non
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NONLINEAR MODELS 129Example 4.1. Co
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NONLINEAR MODELS 131jumps when it b
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NONLINEAR MODELS 133the series and
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NONLINEAR MODELS 135els to the mont
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NONLINEAR MODELS 137growth-2 -1 0 1
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NONLINEAR MODELS 139average of y t
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NONLINEAR MODELS 141indicating that
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NONLINEAR MODELS 143s T,2t=1T∑K h
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NONLINEAR MODELS 145f i (.) of Eq.
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NONLINEAR MODELS 1474.1.9.1 Feed-Fo
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NONLINEAR MODELS 1494.1.9.2 Trainin
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NONLINEAR MODELS 151prob0.0 0.2 0.4
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NONLINEARITY TESTS 153idea behind v
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NONLINEARITY TESTS 155C l (δ, T )
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NONLINEARITY TESTS 157and obtain th
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NONLINEARITY TESTS 159• Step 2: C
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FORECASTING 161returns. In summary,
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FORECASTING 163in m 11 and m 22 ind
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APPLICATION 165unem. rate4 6 8 10
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APPLICATION 167Table 4.4. Out-of-Sa
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APPENDIX A 169compute a0 = 0.1, a1
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REFERENCES 171P(s t = 2 | s t−1 =
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REFERENCES 173Fan, J. (1993), “Lo
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NONSYNCHRONOUS TRADING 177t − k t
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BID-ASK SPREAD 179The lag-1 autocov
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EMPIRICAL CHARACTERISTICS 181The ma
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n(trades)0 20406080 1200 1000 2000
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EMPIRICAL CHARACTERISTICS 185after-
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MODELS FOR PRICE CHANGES 187deep un
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MODELS FOR PRICE CHANGES 189σ 2i (
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MODELS FOR PRICE CHANGES 191A i = 1
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MODELS FOR PRICE CHANGES 193( )piln
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DURATION MODELS 195For the IBM data
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DURATION MODELS 197(a) is the avera
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DURATION MODELS 199actions might no
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DURATION MODELS 201Series : xACF0.0
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DURATION MODELS 203reduces to that
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DURATION MODELS 205Series : xACF0.0
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THE PCD MODEL 207where w(α) denote
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THE PCD MODEL 209(a) All transactio
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THE PCD MODEL 2110 10 20 30 40 500
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THE PCD MODEL 213⎧⎪⎨1f (x |
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THE PCD MODEL 215Generalized Gamma
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THE PCD MODEL 217smpl 2 3534compute
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REFERENCES 219(a) Use the data to c
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STOCHASTIC PROCESSES 223write a con
Page 233 and 234: STOCHASTIC PROCESSES 2253. stationa
Page 235 and 236: ITO’S LEMMA 2276.3.2 Stochastic D
Page 237 and 238: ITO’S LEMMA 229This result shows
Page 239 and 240: DISTRIBUTIONS OF PRICE AND RETURN 2
Page 241 and 242: BLACK-SCHOLES EQUATION 233and G t =
Page 243 and 244: BLACK-SCHOLES FORMULA 235risk prefe
Page 245 and 246: BLACK-SCHOLES FORMULA 237The stock
Page 247 and 248: BLACK-SCHOLES FORMULA 239(a) Call o
Page 249 and 250: AN EXTENSION OF ITO’S LEMMA 2410
Page 251 and 252: STOCHASTIC INTEGRAL 243using the It
Page 253 and 254: JUMP DIFFUSION MODELS 245where w t
Page 255 and 256: JUMP DIFFUSION MODELS 247where it i
Page 257 and 258: JUMP DIFFUSION MODELS 249sudden jum
Page 259 and 260: APPENDIX A 251Pricing formulas for
Page 261 and 262: EXERCISES 253which involves the CDF
Page 263 and 264: REFERENCES 255Bakshi, G., Cao, C.,
Page 265 and 266: VALUEATRISK 257log return-0.2 -0.1
Page 267 and 268: RISKMETRICS 259we use log returns r
Page 269 and 270: RISKMETRICS 261investor is$10,000,0
Page 271 and 272: ECONOMETRIC APPROACH TO VAR 263Cons
Page 273 and 274: ECONOMETRIC APPROACH TO VAR 265The
Page 275 and 276: QUANTILE ESTIMATION 267Using the fo
Page 277 and 278: QUANTILE ESTIMATION 269ˆx 0.01 = p
Page 279 and 280: EXTREME VALUE THEORY 271= 1 −= 1
Page 281 and 282: EXTREME VALUE THEORY 273shows that
Page 283: EXTREME VALUE THEORY 275{ [] }r n(i
Page 287 and 288: EXTREME VALUE TO VAR 2797.6 AN EXTR
Page 289 and 290: EXTREME VALUE TO VAR 281VaR =−2.5
Page 291 and 292: EXTREME VALUE TO VAR 283stock. The
Page 293 and 294: A NEW APPROACH TO VAR 285series (e.
Page 295 and 296: A NEW APPROACH TO VAR 287In Eq. (7.
Page 297 and 298: A NEW APPROACH TO VAR 289Example 7.
Page 299 and 300: A NEW APPROACH TO VAR 291feature is
Page 301 and 302: A NEW APPROACH TO VAR 293(a) Homoge
Page 303 and 304: A NEW APPROACH TO VAR 295Table 7.4.
Page 305 and 306: REFERENCES 297a Gaussian GARCH(1, 1
Page 307 and 308: Analysis of Financial Time Series.
Page 309 and 310: CROSS-CORRELATION 301correlation co
Page 311 and 312: CROSS-CORRELATION 303where ̂D is t
Page 313 and 314: CROSS-CORRELATION 305Table 8.1. Sum
Page 315 and 316: 30yr20yr10yr5yr1yr-10 0 510-10 0 51
Page 317 and 318: VAR MODELS 3098.2 VECTOR AUTOREGRES
Page 319 and 320: VAR MODELS 311where G = Cov(b t ).
Page 321 and 322: VAR MODELS 313where φ 0 and a t ar
Page 323 and 324: VAR MODELS 315To specify the order
Page 325 and 326: VAR MODELS 317the S&P 500 index. Th
Page 327 and 328: VECTOR MA MODELS 319[ ] [ ] [ ] [r1
Page 329 and 330: VECTOR MA MODELS 321turn used to co
Page 331 and 332: VECTOR ARMA MODELS 323For the VAR(1
Page 333 and 334: VECTOR ARMA MODELS 325log-rate0.0 0
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VECTOR ARMA MODELS 327interest-rate
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CO-INTEGRATION 329Series : x1ACF0.0
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CO-INTEGRATION 331Stock Exchange; o
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THRESHOLD CO-INTEGRATION 333ously b
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PRINCIPAL COMPONENT ANALYSIS 3358.6
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PRINCIPAL COMPONENT ANALYSIS 337(c
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PRINCIPAL COMPONENT ANALYSIS 339(a)
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FACTOR ANALYSIS 341(a) 5 stock retu
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FACTOR ANALYSIS 343andCov(r, F) = E
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FACTOR ANALYSIS 345matrix P to tran
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FACTOR ANALYSIS 347Example 8.9. In
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APPENDIX A. REVIEW OF VECTORS AND M
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APPENDIX A. REVIEW OF VECTORS AND M
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APPENDIX B. MULTIVARIATE NORMAL DIS
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REFERENCES 355(b) Build a bivariate
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REPARAMETERIZATION 359To illustrate
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REPARAMETERIZATION 361where ⊥ den
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GARCH MODELS FOR BIVARIATE RETURNS
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VECTOR VOLATILITY MODELS 377using t
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VECTOR VOLATILITY MODELS 379large-c
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VECTOR VOLATILITY MODELS 381(a) S&P
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FACTOR-VOLATILITY MODELS 383conside
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APPLICATION 385[ ]σ11,t=σ 22,t⎡
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MULTIVARIATE t DISTRIBUTION 387σ 1
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APPENDIX A. SOME REMARKS ON ESTIMAT
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APPENDIX A. SOME REMARKS ON ESTIMAT
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REFERENCES 393(a) Compute the sampl
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GIBBS SAMPLING 397mean adding auxil
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BAYESIAN INFERENCE 399distributions
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BAYESIAN INFERENCE 401normal with m
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ALTERNATIVE ALGORITHMS 403distribut
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ALTERNATIVE ALGORITHMS 40510.4.2 Me
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REGRESSION WITH SERIAL CORRELATIONS
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REGRESSION WITH SERIAL CORRELATIONS
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MISSING VALUES AND OUTLIERS 411y h
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MISSING VALUES AND OUTLIERS 413Cons
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MISSING VALUES AND OUTLIERS 415we h
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MISSING VALUES AND OUTLIERS 417c3t-
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STOCHASTIC VOLATILITY MODELS 419tha
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STOCHASTIC VOLATILITY MODELS 421whe
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STOCHASTIC VOLATILITY MODELS 423den
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STOCHASTIC VOLATILITY MODELS 425The
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STOCHASTIC VOLATILITY MODELS 427(a)
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•••MARKOV SWITCHING MODELS 42
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MARKOV SWITCHING MODELS 431(a) GARC
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MARKOV SWITCHING MODELS 433β i ∼
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MARKOV SWITCHING MODELS 435(a) mont
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MARKOV SWITCHING MODELS 437α ij ar
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FORECASTING 439garchrtn-sq0 200 400
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EXERCISES 441eter uncertainty in pr
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REFERENCES 443Justel, A., Peña, D.
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446 INDEXData (cont.)equal-weighted
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448 INDEXPoisson process, 244inhomo
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