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Applications of state space models in finance

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List <strong>of</strong> figures<br />

2.1 Hierarchical cluster dendrogram for the set <strong>of</strong> excess sector returns. . . . 10<br />

2.2 Summaries <strong>of</strong> the weekly returns on the (i) broad market, (ii) the Insurance<br />

sector and (iii) Food & Beverages. . . . . . . . . . . . . . . . . . . 11<br />

2.3 Autocorrelation functions <strong>of</strong> the broad market, the Insurance sector and<br />

Food & Beverages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14<br />

2.4 CUSUMSQ tests with 5% confidence <strong>in</strong>tervals for the excess return series<br />

<strong>of</strong> (a) Insurance and (b) Food & Beverages. . . . . . . . . . . . . . . . . 15<br />

4.1 (a) Weekly percentage log-return series <strong>of</strong> the Technology sector and<br />

(b) histogram with a fitted normal distribution. . . . . . . . . . . . . . . 45<br />

4.2 Histogram <strong>of</strong> weekly log-returns <strong>of</strong> the Technology sector and fitted mixtures<br />

with (a) two and (b) three normal distributions. . . . . . . . . . . 47<br />

4.3 Basic structure <strong>of</strong> a hidden Markov model. . . . . . . . . . . . . . . . . 49<br />

6.1 Conditional volatility estimates for the Telecommunications sector. . . . 95<br />

6.2 Weekly excess log-return series <strong>of</strong> (a) Automobiles and (b) the broad<br />

market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103<br />

6.3 (a) Residuals from the auxiliary heteroskedastic regression model and<br />

(b) GLS weight<strong>in</strong>g factor for Automobiles and the overall market. . . . 104<br />

6.4 Weighted weekly excess log-return series <strong>of</strong> (a) Automobiles and (b) the<br />

broad market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105<br />

6.5 Conditional random walk and generalized random walk beta estimates<br />

for the Automobiles sector. . . . . . . . . . . . . . . . . . . . . . . . . . 106<br />

6.6 Boxplots <strong>of</strong> the conditional beta series for the Insurance sector. . . . . . 110<br />

6.7 t-GARCH and stochastic volatility based betas for the Insurance sector. 111<br />

6.8 Markov switch<strong>in</strong>g betas for the Insurance sector. . . . . . . . . . . . . . 111<br />

6.9 Kalman filter betas for the Insurance sector. . . . . . . . . . . . . . . . . 112<br />

6.10 In-sample forecast<strong>in</strong>g evaluation: (a) average MAE and MSE across sectors<br />

and (b) average ranks across sectors. . . . . . . . . . . . . . . . . . 114<br />

6.11 Histograms <strong>of</strong> Spearman’s <strong>in</strong>-sample rank correlations. . . . . . . . . . . 115<br />

6.12 Out-<strong>of</strong>-sample forecast<strong>in</strong>g evaluation (100 samples): (a) average MAE<br />

and MSE across sectors and (b) average ranks across sectors. . . . . . . 117<br />

6.13 Histograms <strong>of</strong> Spearman’s out-<strong>of</strong>-sample rank correlations (100 samples). 118

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