<<

. 19
( 19)



maximum likelihood estimation and 260“1
Multivariate Switching Regime Beta Model
Markovian switching models 193, 194“5,
282
246
application to foreign exchange rates
197“205 na¨ve approach
±
descriptive statistics 199 in EUR/USD forecasting and trading models
10
empirical results 200
evaluation strategy 200 imputation accuracy 394
evaluation with a simple hedging strategy to missing data 385
nearest neighbours 4
202“5
moving average de¬nitions 203“4 neural network models
trading results 204“5 activation function 24
advantages/disadvantages 21
trading rules of EMA trading models with
volatility ¬lters 204 in forecasting FX time series 2“3
trading rules with EMA trading models fully connected 24
in FX forecasting and trading currency
204
trend-following moving average (MA) volatility 129, 130“2
models 202 imputation accuracy 394
features of foreign exchange interbank issue affecting performance 25“6
market 197“9 literature evaluation 3“4
residuals analysis 200“2 logistic function 24, 26
Markovitz model of portfolio risk in Excel missing data 381, 382, 390“1, 394
293“311 post-training weights analysis 28
404 Index
pairs trading 42, 49
neural network models (continued)
parameter-driven models 244“5
procedure 26“31
technical implementation, for classi¬cation Pentanomial model 284“6
Pentanomial tree 279, 280
in Excel 167“72, 191
theory and methodology 20“31 Phillips“Perron (PP) test statistic 6, 7, 385
principal components analysis (PCA) 67, 73,
transaction costs 36
Neural Network Regression (NNR) models 1, 74“5
2“3, 4, 129 algorithm 396“8
forecasting accuracy results 32, 35, 36, 37 computation 391“2
transfer function and 138 drawback of 395
volatility forecasting and 137“9 imputation accuracy 394
Neural Network Regression/Recurrent Neural missing data and 381, 382, 391“3
Network (NNR/RNN) pure risk premium 81
cf GARCH (1,1) in trading simulation
quadrinomial tree 278, 279
147“9
quasi-maximum likelihood (QML) estimator
out-of-sample forecasting accuracy 143“5
253, 256, 262“3
volatility forecasts 140“2
Newey“West standard errors 108
Ramsey™s RESET test 14
Newey“West standard deviations 106
random coef¬cient model 226
no-arbitrage approach 47
random trend model 226
nonlinear error-correction modelling 131
random walk forecasting accuracy 3
nonlinear nonparametric models 139
random walk model 3, 4, 225“6
nonstationarity 42“3
Recurrent Neural Network (RNN) 3, 29, 38
in forecasting and trading currency volatility
oblique decision trees 172
129, 131
observation-driven models 244“5
volatility forecasting and 139“40
Occam™s Razor 25
one-factor homoskedastic model 74 relative value analysis 67
Ricatti equation 92
optimal decay model 316“18, 324“6
optimisation error 24 risk-adjusted measures of return 2
risk neutrality (risk-neutral density; RND)
option pricing, stochastic volatility pricing
applied to 275“80 function estimation 349, 350, 351
ordinary least squares (OLS) estimation 46 using option spreads 365“70
in factor sensitivities estimation 216“22 riskless arbitrage 47, 48, 49
RiskMetricsTM model 315“16, 324, 350
Ornstein“Uhlenbeck process 245, 276
orthogonal GARCH 313 rnn1 29“31
over¬tting of models 25 robust regression analysis 391
over-the-counter currency options rolling regression 218“22
advantages of 349“50 root-mean-squared error (RMSE) 1, 25, 31,
at-the-money forward volatility 355, 356 32, 130, 138, 143, 144, 393
empirical applications 365“77
scalar GARCH model with variance targeting
estimation of RND functions using option
328“9
spreads 365“70
SEE5 classi¬er 178“91
implied correlations/credibility tests
advanced options 189
371“7
boosting 184“6
indicators of credibility of an exchange rate
categorical value subsets 186“8
band 361“5
construct classier 189“91
measures of correlation and prices 359“61
costs ¬le 189
risk neutrality function estimation 349,
350, 351 cross-validation trials 188“9
data inputs 178“80
risk-reversals 353“4, 356
RND estimation using option spreads implementing for windows 180“1
rulesets 182“3
355“9
strangles 353, 354, 356 softening thresholds 188
winnowing attributes 186
valuation of currency options spreads
353“5 sell signal, EUR/USD 31
Index 405
semi parametric log-periodogram estimator time series modelling, cointegration and 42“5
262, 263 time-varying factor sensitivities estimation
serial correlation LM test 385 213“36
shadow effect in rolling estimation 221 ordinary least squares (OLS) 216“22
Sharpe ratio 19, 31“2, 34, 37, 130 stochastic parameter regression model
sigmoid function 138 223“36
Simulated Expectation Maximisation (SEM) weighted least squares (WLS) 222“3
260 trading rules
Simulated Likelihood Ratio (SLR) 260“1 applied example 344“6
simulated method of moments 256 correlation of linear forecasters 340“1
˜smile effect™ 134, 276 data 333“5
˜sneer effect™ 276 equivalence 336“8
SRSV model 267 expected return of linear forecasters 342“4
“statarb” strategy 50 expected transactions cost under assumption
statistical arbitrage 47“50 of random walk 338“40
statistical modelling 67 expected volatility of moving averages
stochastic parameter regression model 341“2
223“36 moving averages 335“6
stochastic variance 130 trading simulation performance measures 33,
stochastic volatility (SV) models 240, 34
246“50 transaction costs 36
with continuous volatility 247“8 trend-stationary time series 43
extensions 261“3 Twoing Rule 174
Kalman ¬lter for quasi-maximum
likelihood (QML) estimation Value at Risk (VaR) 72, 243“4
252“3 stochastic volatility pricing applied to
Kalman smoother for 254“5 280“3, 286
with discrete volatility 248“50 variance ratio analysis 50
extensions 263 variance ratio function (VRF) 45
Hamilton ¬lter for maximum likelihood variance ratio tests 44“5
estimation of 253 volatility and correlation modelling using Excel
Kim smoother for 255 313“32
estimation 250“61, 255“61 basics 314“15
extensions of 261“3 model comparison 322“3, 331
¬lter for 250“3 multivariate models 324“31
simulation-based methods 256“61 univariate models 315“23
smoother for 254“5 volatility and forecasting accuracy model
Student-t-based 261 combinations 142“3
stock beta estimation 219 volatility forecast classi¬cation 146
stopping rule 174 Volatility program 265“75
Student-t-based stochastic volatility model estimation 267“73
261 forecasting 275
Student-t-distribution 136 simulation 273“5
Sum Minority 174 volatility smile 356
supervised learning 164
Wald statistic 136
tau-period variance ratio 44 weather data and weather derivatives 383“5
Theil inequality coef¬cient (Theil-U) 31, 32, weather databank 384“5
130, 143, 144, 393 weighted least squares (WLS) 222“3
three-layer feedforward neural network
167 yield curve, evolution of 140


Index compiled by Annette Musker

<<

. 19
( 19)