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Elsevier, Neural Networks In Finance 2005_10

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  1. Bibliography 227 Jarque, C.M., and A.K. Bera (1980), “Efficient Tests for Normality, Homoskedasticity, and Serial Independence of Regression Residuals,” Economics Letters 6: 255–259. Judd, Kenneth L. (1998), Numerical Methods in Economics. Cambridge, MA: MIT Press. Kantz, H., and T. Schreiber (1997), Nonlinear Time Series Analysis. Cambridge, UK: Cambridge University Press. Kirkpatrick, S, C.D. Gelatt Jr., and M.P. Vecchi (1983), “Optimization By Simulated Annealing,” Science 220: 671–680. Ko˘enda, E. (2001) An Alternative to the BDS Test: Integration Across c the Correlation Integral. Econometric Reviews 20, 337–351. Krugman, Paul (1998), “Special Page on Japan: Introduction.” Webpage: web.mit.edu/krugman/www/jpage.html. Kuan, Chung-Ming, and Halbert White (1994), “Artifical Neural Networks: An Econometric Perspective,” Econometric Reviews 13: 1–91. Kuan, Chung-Ming, and Tung Liu (1995), “Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks,” Journal of Applied Econometrics 10: 347–364. Lai, Tze Leung, and Samuel Po-Shing Wong (2001), “Stochastic Neural Networks with Applications to Nonlinear Time Series.” Journal of the American Statistical Association 96: 968–981. LeBaron, Blake (1998), “An Evolutionary Bootstrap Method for Selecting Dynamic Trading Stratergies”, in A.-P. N. Refenes, A.N. Burgess and J.D. Moody (eds.), Decision Technologies for Computational Finance, Ansterdam: Kluwer Academic Publishers, 141–160. Lee, T.H., H. White, and C.W.J. Granger (1992), “Testing for Neglected Nonlinearity in Times Series Models: A Comparison of Neural Network Models and Standard Tests,” Journal of Econometrics 56: 269–290. Ljung, G.M., and G.E.P. Box (1978), “On a Measure of Lack of Fit in Time Series Models.” Biometrika 65: 257–303. Lumsdaine, Robin L., and D. H. Papell (1997), “Multiple Trend Breaks and the Unit Root Hypothesis,” Review of Economics and Statistics : 212–218. Mandic, Danilo, and Jonathan Chambers (2001), Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures, and Stability. New York: John Wiley and Sons.
  2. 228 Bibliography McCarthy, Patrick S. (1996), “Market Price and Income Elasticities of New Vehicles,” Review of Economics and Statistics 78: 543–548. McKibbin, Warwick (2002), “Macroeconomic Policy in Japan,” Asian Economic Paper 1: 133–169. ———, and Peter Wilcoxen (1998), “The Theoretical and Empiri- cal Structure of the G-Cubed Model,” Economic Modelling 16: 123–148. McLeod, A. I., and W.K. Li (1983), “Diagnostic Checking of ARMA Time Series Models Using Squared-Residual Autocorrelations,” Journal of Time Series Analysis 4: 269–273. McNelis, P., and G. Nickelsburg (2002), “Forecasting Automobile Produc- tion in the United States.” Manuscript, Economics Dept., Georgetown University. McNelis, Paul D., and Peter McAdam (2004), “Forecasting Inflation with Thick Models and Neural Networks.” Working Paper 352, European Central Bank. Webpage: www.ecb.int/pub/wp/ecbsp352.pdf. Meltzer, Alan (2001), “Monetary Transmission at Low Inflation: Some Clues from Japan,” Monetary and Economic Studies 19(S-1): 13–34. Merton, Robert (1973), “An Intertemporal Capital Asset Pricing Model.” Econometrica 41: 867–887. Metropolis, N., A.W. Rosenbluth, M. N. Rosenbluth, A.H. Teller, and E. Teller (1953), “Equation of State Calculations by Fast Computing Machines,” Journal of Chemical Physics 21: 1087–1092. Michalewicz, Zbigniew (1996), Genetic Algorithms + Data Structures = Evolution Programs. Third Edition. New York: Springer-Verlag. ———, and David B. Fogel (2002), How to Solve It: Modern Heuristics. New York: Springer-Verlag. Miller, W. Thomas III, Richard S. Sutton, and Paul J. Werbos (1990), Neural Networks for Control. Cambridge, MA: MIT Press. Neft¸i, Salih (2000), An Introduction to the Mathematics of Financial c Derivatives. San Diego, CA: Academic Press. Perron, Pierre (1989), “The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis,” Econometrics 57: 1361–1401.
  3. Bibliography 229 Pesaran, M.H., and A. Timmermann (1992), “A Simple Nonparametric Test of Predictive Performance,” Journal of Business and Economic Statistics 10: 461–465. Qi, Min (1999), “Nonlinear Predictability of Stock Returns Using Finan- cial and Economic Variables,” Journal of Business and Economics Statistics 17: 419–429. Quagliarella, Domenico, and Alessandro Vicini (1998), “Coupling Genetic Algorithms and Gradient Based Optimization Techniques,” in Quagliarella, D., J. Periaux, C. Poloni, and G. Winter (eds.), Genetic Algorithms and Evolution Strategy in Engineering and Computer Science: Recent Advances and Industrial Applications. West Sussex, England: John Wiley and Sons, Ltd. Quagliarella, D., J. Periaux, C. Poloni, and G. Winter (1998), Genetic Algorithms and Evolution Strategy in Engineering and Computer Science: Recent Advances and Industrial Applications. West Sussex, England: John Wiley and Sons, Ltd. Razzak, Weshah A. “Wage-Price Dynamics, the Labor Market, and Deflation in Hong Kong.” HKIMR Working Paper 24/2003. Rissanen, J. (1986a), “A Predictive Least-Squares Principle,” IMA Journal of Mathematical Control and Information 3: 211–222. ——— (1986b), “Stochastic Complexity and Modeling,” Annals of Statistics 14: 1080–1100. Robinson, Guy (1995), “Simulated Annealing.” Webpage: www.npac.syr.edu/ copywrite/pcw/node252. Ross, S. (1976), “The Arbitrage Theory of Capital Asset Pricing,” Journal of Economic Theory 13: 341–360. Rustichini, Aldo, John Dickhaut, Paolo Ghirardato, Kip Smith, and Jose V. Pardo (2002), “A Brain Imaging Study of Procedural Choice,” Working Paper, Department of Economics, University of Minnesota. Webpage: http://www.econ.umn.edu/˜arust/ProcCh3.pdf. Sargent, Thomas J. (1997), Bounded Rationalilty in Macroeconomics. Oxford: Oxford University Press. ——— (1999), The Conquest of American Inflation. Princeton, NJ: Princeton University Press.
  4. 230 Bibliography Schwarz, G. (1978), “Estimating the Dimension of a Model,” Annals of Statistics 6: 461–464. Sims, Christopher (1992), “Interpreting the Macroeconomic Times Series Facts: The Effects of Monetary Policy.” European Economic Review 36: 2–16. ———, and Mark W. Watson (1998), “A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series.” Cambridge, MA: National Bureau of Economic Research Working Paper 6607. Website: www.nber.org/papers/w6607. Stock, James H., and Mark W. Watson (1999), “Forecasting Inflation,” Journal of Monetary Economics 44: 293–335. Sundermann, Erik (1996), “Simulated Annealing.” Webpage: petaxp.rug. ac.be/˜erik/research/research-part2. Svensson, Lars E. O., (2003), “Escaping from a Liquidity Trap and Deflation: The Foolproof Way and Others,” Journal of Economic Perspectives. Ter¨svirta, T. (1994), “Specification, Estimation, and Evaluation of a Smooth-Transition Autogressive Models,” Journal of the American Statistical Association 89: 208–218. ———, and H.M. Anderson (1992), “Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models,” Journal of Applied Econometrics 7: S119–S136. van Dijk, Dick, Timo Ter¨svirta, and Philip Hans Franses (2000), a “Smooth Transition Autoregressive Models—A Survey of Recent Developments.” Research Report EI2000–23A. Rotterdam: Erasmus University, Econometric Institute. Tsay, Ruey S. (2002), Analysis of Financial Time Series. New York: John Wiley and Sons, Inc. van Laarhoven, P.J.M., and E.H.L. Aarts (1988), Simulated Annealing: Theory and Applications. Boston, MA: Kluwer Academic Publishers. Werbos, Paul John (1994), The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. New York: Wiley Interscience. White, Halbert (1980), “A Heteroskedasticity Covariance Matrix and a Direct Test for Heteroskedasticity.” Econometrica 48: 817–838.
  5. Bibliography 231 Wolkenhauer, Olaf (2001), Data Engineering. New York: John Wiley and Sons. Yoshino, Naoyuki and Eisuke Sakakibara (2002), “The Current State of the Japanese Economy and Remedies,” Asian Economic Papers 1: 110–126. Zivot, E., and D.W.K. Andrews (1992), “Further Evidence on the Great Crash, the Oil Price Shock, and the Unit-Root Hypothesis,” Journal of Business and Statistics 10: 251–270.
  6. Index Note: Page locators followed by intertemporal capital asset “n” refer to footnotes. pricing model, 47–48 thick modeling, 48 auto-associative mapping, 44, 46 A autocorrelation coefficient, 87 activation functions, 24–30 automotive production Gaussian, 26–28 forecasting example, radial basis, 28–29 145–155 ridgelet, 29–30 data used in, 146–148 squasher, 24–28 evaluation of, 150–152 tansig, 26 interpretation of, 152–155 Akaike statistic, 86 MATLAB program notes American options, 138–139 for, 166 analytic derivatives, 105–107 models used in, 148–150 approximations in autoregressive models, 14, decision-making, 23 55, 177 arbitrage pricing theory (APT), 47–48, 116, 137–143 arithmetic crossover, 73 B asset pricing backpropagation method, 69–70 arbitrage pricing theory, bagging predictors, 78 47–48, 116, 137–143 banking intervention example, capital asset pricing model, 204–209 46–48 decision-making in, 46–49 bank lending, property prices in emerging markets, and, 173–174, 174n, 122–125 186–189, 195 233
  7. 234 Index BFGS (Boyden-Fletcher- convergence Goldfarb-Shanno) to absurd results, 105 algorithm, 69, 78–80 in genetic algorithms, 75 black box criticism, 55–57 local, 33–34, 68–71, 76, 105 Black-Scholes options pricing corporate bonds example, (BSOP) model, 116, 156–165 137–143 data in, 156–158 bond ratings, 53 in-sample performance, bootstrapping methods 160–162 for assessing significance, interpretation of results, 108 161–165 for in-sample bias, 101–102 MATLAB program notes, for out-of-sample 166 performance, 202, 204 models used, 157–160 0.632 bootstrap test, 101–102, out-of-sample performance, 202, 204 160–161 bounded rationality assumption, covariance stationary time 7 series, 59–61 Brock-Deckert-Scheinkman credit card risk example, (BDS) test, 91–92, 94 200–205 crisp logic, 199 crossover, 73–74 C cross-section analysis, 14n calendar effects, 61–63 cross-validation, 101 call and put options, 1, 138–140 curse of dimensionality, 18, capital asset pricing model 41–42, 76 (CAPM), 46–48 capital-asset ratio, 205–206 D CAPM beta, 47 data preprocessing, 59–65 chaos theory, 117. See also in corporate bonds example, stochastic chaos (SC) 157–158 model in out-of-sample evaluation, Chi-squared distribution, 87 95 Clark-West bias correction test, scaling functions, 64–65, 84 98–99 seasonal adjustments, 61–63 classification networks, 37–38, stationarity, 59–61 49–54, 58 data requirements, 102–103 classification problems, 2, 5, data scaling, 64–65, 84, 109 199–210 decision-making closed form solutions, 20 in asset pricing, 46–49 conditional variance, 16–17 brain-imaging models of, 23 The Conquest of American Inflation (Sargent), 56 use of forecasting in, 3–5 control, 3 deflation forecasting
  8. Index 235 Hong Kong example, Euclidean norm, 29 168–182 European options, 138 importance of, 167–168 evaluation of network United States example, estimation, 85–111 174–175 data requirements, 102–103 DeLeo scaling function, 64–65 implementation strategy, Dickey-Fuller test, 59–61 109–110 Diebold-Mariano test, 96–97 in-sample criteria, 85–94 dimensionality reduction, 2–3, interpretive criteria, 41–46, 211–220 104–108 dimensionality reduction MATLAB programming mapping, 42, 44 code for, 93–94, directional accuracy test, 99–100 107–108 discrete choice, 49–54 out-of-sample criteria, discriminant analysis, 49–50 94–103 logit regression, 50–51 significance of results, 108 multinomial ordered choice, evolutionary genetic algorithms, 53–54 75 neural network models for, evolutionary stochastic search, 52–53 72–75 probit regression, 51–52 exchange rate forecasting, Weibull regression, 52 100–101, 103 discriminant analysis, 49–50 expanding window estimation, in banking intervention 95 example, 207–209 expectations, subjective, 23 in credit card risk example, extreme value theory, 52 200–204 F distorted long-memory (DLM) model, 115–116, feedforward networks, 21–24 135–137 analytic derivatives and, dividend payments, 131 105–106 Durbin-Watson (DW) test, 87 in discrete binary choice, 52–53 E with Gaussian functions, economic bubbles, 135 26–28 election tournaments, 74–75 with jump connections, elitism, 75 30–32, 39–40 Ellsberg paradox, 56 with logsigmoid functions, Elman recurrent network, 24–28, 31 34–38, 58 in MATLAB program, emerging markets, use of neural 80–82 networks in, 8, 122–125 multilayered, 32–34 Engle-Ng test of symmetry of with multiple outputs, residuals, 89, 94 36–38
  9. 236 Index feedforward networks, contd foreign exchange markets, 139n in recurrent networks, 34–35 forward contracts, 139n with tansig functions, 26 “free parameters,” 55 financial engineering, xii fuzzy sets, 199 financial markets corporate bonds example, G 156–165 Gallant-Rossi-Tauchen intrinsic dimensionality in, procedure, 62–63 41–42 GARCH nonlinear models, recurrent networks and 15–20 memory in, 36 development of, 15n sign of predictions for, 99 GARCH-M, 15–17 volatility forecasting integrated, 132 example, 211–220 model typology, 20–21 finite-difference methods, orthogonal polynomials, 106–107 18–20 fitness tournaments, 73–75 polynomial approximation, forecasting, 2 17–18 automotive production program notes for, 58 example, 145–155 Gaussian function, 26–28, 51 corporate bonds example, Gaussian transformations, 28 156–165 curse of dimensionality in, GDP growth rates, 125–128 18, 41–42, 76 Geman and Geman theorem, 71 data requirements in, 103 genetic algorithms, 72–75 exchange rate, 100–101, 103 development of, 6–7 feedback in, 5 evolutionary, 75 financial market volatility gradient-descent methods example, 211–220 with, 75–77 inflation, 37, 87, 104, 168–182 in MATLAB program, linear regression model in, 78–80, 83–84 13–15 steps in, 72–75 market volatility example, Gensaki interest, 186–188 211–220 Gompertz distribution, 52 multiple outputs in, 37 Gompit regression model, 52 out-of-sample evaluation of, goodness of fit, 86 95 gradient-descent methods, 75–77 predictive stochastic Granger causality test, 195–196 complexity, 100–101 stochastic chaos model, H 117–122 Hang Seng index, 170, 172 thick model, 77–78 use in decision-making, 3, Hannan-Quinn information 167–168 criterion, 85–86
  10. Index 237 Harvey-Leybourne-Newbold size importance of, 167–168 correction, 97 moving averages in, 87 health sciences, classification unemployment and, 104 in, 2n in the United States, Hermite polynomial expansion, 174–175 19 initial conditions, 65, 118–119 Hessian matrix, 67–69, 76 input neurons, 21 heteroskedasticity, 88–89, 91 in-sample bias, 101–102 hidden layers in-sample evaluation criteria, jump connections and, 85–94 30–32 Brock-Deckert-Scheinkman multilayered feedforward test, 91–92, 94 networks in, 32–34 Engle-Ng test for symmetry, in principal components 89, 94 analysis, 42 Hannan-Quinn information holidays, data adjustment for, statistic, 86 62–63, 62n Jarque-Bera statistic, homoskedasticity tests, 88–89, 89–90, 94 91 Hong Kong, inflation and Lee-White-Granger test, 32, deflation example, 90–91, 94 168–182 Ljung-Box statistic, 86–88, data for, 168–174 94 in-sample performance, MATLAB example of, 177–179 93–94 interpretation of results, McLeod-Li statistic, 88–89, 178–182 94 model specification, 174–177 in-sample evaluations out-of-sample performance, in automotive production 177–178, 180 example, 150–151 Hong Kong, volatility in banking intervention forecasting example, example, 205, 207 212–216 in Black-Sholes option hybridization, 75–77 pricing models, hyperbolic tangent function, 26 140–142 I in corporate bond example, 160–162 implementation strategy, in credit card risk example, 109–110 200–202 import prices, 170–171, 184–185 in distorted long-memory inflation forecasting models, 136–137 feedforward networks in, 37 in Hong Kong inflation Hong Kong example, example, 177–179 168–182
  11. 238 Index L in-sample evaluations, contd in Hong Kong volatility lagged values forecasting example, in Elman recurrent network, 213–214 34–36 in Japan inflation example, in evaluating models, 116 189–191 in implementation, 109 in Markov regime switching in Ljung-Box Q-statistic, models, 128–130 87–88 in stochastic chaos models, in nonlinear principal 118–120 components, 49 in stochastic volatility/jump predictive stochastic diffusion models, complexity, 100–101 123–124 Laguerre polynomial expansion, in United States volatility 19 forecasting example, land price index (Japan), 216–218 186–189, 193 in volatility regime latent variables, 23 switching models, 132 learning parameters, 69 interest rate forecasting, 37, 146 leave out one method, 101 interpretive criteria, 104–108 Lee-White-Granger test, 32, intertemporal capital asset 90–91, 94 pricing model Legendre polynomial expansion, (ICAPM), 47–48 19 intrinsic dimensionality, 41–42 likelihood functions, 16–17 linear ARX model, 14n J linear discriminant analysis, jacobian matrix, 107–108 49–50 Japan, inflation and deflation linear models, 13–15 model for, 182–196 advantages of, 15 data in, 184–189 in automotive production in-sample performance, forecasting, 148–152 189–190 as benchmark, xii interpretation of results, in corporate bond example, 191–196 159–165 model specification, 189 in Hong Kong inflation proposed remedies, 182–184 example, 176–180 Jarque-Bera statistic, in Japan inflation example, 89–90, 94 189–192 jump connections, use of residuals from, 32, 34 30–32, 39–40 linear principal components analysis (PCA), 42–43, K 211–220 linear scaling functions, 64 kurtosis, 90
  12. Index 239 linear smooth-transition regime in-sample diagnostic switching system, 40 statistics in, 93–94 Ljung-Box Q-statistic, 87–88, 94 main script functions in, 142–143 local convergence problem models in, 58 absurd results, 105 numerical optimization multiple hidden layers and, example, 78–80 32, 33 polynomial and network in nonlinear optimization approximation methods, 68–71, 76 example, 80–83 local gradient-based search, 67 stochastic chaos model logistic estimation, 53–54 in, 117 logistic regression, 52–53 Texas bank failures in, 210 logit regression, 50–51 maximum likelihood estimation, in banking intervention 88 example, 207–209 McLeod and Li test, 88–89, 94 in credit card risk example, model typology, 20–21 200–205 modified Diebold-Mariano logsigmoid (squasher) function, (MDM) statistic, 97 24–28, 31 moving average filters, 63 logsigmoid transition function, moving-average processes, 39 34–35, 87–88 loss function minimization, moving window estimation, 66–67 95–96 multilayered feedforward M networks, 32–34 Markov chain property, 71 multi-layer perception (MLP) Markov regime switching (MRS) network, 25, 29 model, 115, 125–130 multiperceptron networks, 22 MATLAB program multiple outputs, 36–38 analytic and finite mutation operation, 74 differences in, 107–108 automobile industry N program in, 166 neglected nonlinearity, 90–91 availability of, xiv nested classification, 53 corporate bonds program nested evaluation models, 98–99 in, 166 neural linguistics, 22 evaluation tests in, 110–111 neural network approach evolutionary computation in, 83–84 advantages over nonlinear regression, 33 German credit card defaults in, 210 bounded rationality assumption in, 7 inflation/deflation programs in, 197 data requirements, 102–103
  13. 240 Index neural network approach, contd nonlinearity, tests to determine, in detecting neglected 90–92 nonlinearity, 90–91 nonlinear principal components differences from classical analysis (NLPCA), models, 7 44–46, 211–220 in discrete choice, 52–53 nonstationary series, 60 model typology, 20–21 normal distributions, 89–90 terminology in, 6 normal (Gaussian) function, neural network 26–28 smooth-transition O regime switching system (NNRS), 39–40 options pricing in automotive production Black-Scholes model, 116, example, 150–155 137–143 in corporate bond example, seasonal adjustment in, 63 160–165 SVJD model for, 123 in Hong Kong inflation ordinary least squares (OLS) example, 176–182 estimators, 20 in Japan inflation example, orthogonal polynomials, 18–20, 189–196 80–82 neural network types, 21–38 orthogonal regression, 42–43 classification networks, out-of-sample evaluation 37–38 criteria, 94–103 feedforward networks, 21–24 data requirements, 102–103 jump connections, 30–32, Diebold-Mariano test, 96–97 39–40 in nested models, 98–99 multiple outputs in, 36–38 predictive stochastic radial basis functions, 28–29 complexity, 100–101 recurrent networks, 34–36 recursive methodology, ridgelet function, 29–30 95–96 squasher functions, 24–28 root mean squared error Nikkei index, 186–187 statistic, 96, 219n, 220 nonlinear estimation, 65–77 sign prediction success genetic algorithms, 67, ratios, 99–100 72–75, 78–80, 83–84 out-of-sample evaluations hybridization, 75–77 in automotive production initial conditions in, 65–66 example, 151–153 local gradient-based in banking intervention searches, 67 example, 207–208 MATLAB examples of, in Black-Sholes option 78–83 pricing models, simulated annealing, 67, 142–143 70–72, 78–80 in corporate bond example, thick modeling, 77–78 160–161, 163
  14. Index 241 in credit card risk example, portfolio management, 202–205 forecasting in, 4 predictive stochastic complexity in distorted long-memory (PSC), 100–101 models, 137–138 price equalization, 168 in Hong Kong inflation price gap, Hong Kong, 170, example, 177–178, 180 172–173 in Hong Kong volatility price puzzle, 188 forecasting example, pricing of risk, 1–2, 5 214–215 pricing options in Japan inflation example, Black-Scholes model, 116, 190–192 137–143 in Markov regime switching seasonal adjustment in, 63 methods, 130–131 SVJD model for, 123 in stochastic chaos models, principal components 120–122 in asset pricing, 46–49 in stochastic volatility/jump intrinsic dimensionality in, diffusion models, 41–42 125–126 linear, 42–43 in United States volatility nonlinear, 44–46 forecasting example, program notes for, 58 218–219 principal components analysis in volatility regime (PCA), 42–43, 211–220 switching models, principle of functional 132–134 integration, 23 out-of-sample predictions, 3 principle of functional output gap, 169–170, 184–185 segregation, 23 output neurons, 21–22 probit regression, 51–52 in banking intervention P example, 207–209 parallel processing, 21–22 in credit card risk example, parallel processing advantage, 22 200–205 parametric models, 20 put options, 1, 138–140 Pesaran-Timmerman directional accuracy test, 99–100 Q Petersohn scaling function, quasi-Newton algorithm, 67–69, 64, 84 78–80, 83 Phillips and Perron test, 61 Phillips curve model, 56, 169, R 174 Poisson jump process, 122 radial basis function (RBF) polynomial approximation, network, 28–29 17–18 random shocks, 34, 47, 70, 117, polynomial expansions, 18–20 149
  15. 242 Index reconstruction mapping, 42, 44 in Japan inflation example, 189–196 recurrent networks, 34–36 softmax function, 53–54 recursive methodology, 95–96 sparse data sets, 42 regime switching models squasher functions, 24–28, 31 Markov, 115, 125–130 stationarity, 59–61 smooth-transition, 38–40 stochastic chaos (SC) model, volatility, 115, 130–134 115, 117–122 regularization term, 86n stochastic search methods residuals, use of, 32, 34, 85, 89 evolutionary, 72–75 ridgelet networks, 29–30 simulated annealing, 67, robust regression, 45–46 70–72, 78–80 root mean squared error stochastic volatility/jump statistic, 96, 219n, 220 diffusion (SVJD) R-squared coefficient, 86 model, 115, 122–125 strike price, 140, 140n S swap-options (swaptions), 48 saddle points, 65–66, 69 symmetry of residuals, 89 Sargent, Thomas J., The synapses, 22 Conquest of American Inflation, 56 T Schwartz statistic, 86 tanh function, 26 seasonal adjustments, 61–63 tansig function, 26 semi-parametric models, Tchebeycheff polynomial 17–18, 20 expansion, 18–19, 19n serial independence tests, 86–89 terminology, 6 shuffle crossover, 73 thick model forecasts, 77–78, 110 sieve estimator, 23–24 thick modeling, 48, 77–78 significance of results, 108 threshold responses, 24–25 sign prediction success ratios, time-series recency effect, 103 99–100 times-series examples, 145–166 simulated annealing, 67, 70–72, automotive production 78–80 forecasts, 145–155 single-point crossover, 73 corporate bonds, 156–165 skewness, 90 times-series models, 14, 14n smooth-transition regime transition function, 38–40 switching models, t statistic, 108 38–40 in automotive production U example, 149–155 in corporate bond example, uncertainty, model, 55–56 159–165 United States, volatility in Hong Kong inflation forecasting example, example, 176–182 216–220
  16. Index 243 unit labor costs, 170–171, volatility regime switching 184, 186 (VRS), 115, 130–134 unit root processes, 60, 135, W 135n unsupervised training, 41 Weibull regression, 52 in banking intervention example, 207–209 V in credit card risk example, vector autoregressive models 200–205 (VAR), 168, 188 Weierstrass Theorem, 17–18 vocabulary of neural networks, 6 welfare index, 4–5
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