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(92) 瀞 * ** VLCCSuezmaxAframax ARMR- EGARCH Aframax γ Aframax VLCCSuezmax γ 0 0 Clarkson Research Studies 2007 0 2007 0 37.8% 36.3%3.2% * **

(93) 2005 Leverage Effect VLCCSuezmaxAframax Oil TankerProduct Carrier International Maritime Organization,IMO 99 middeck construction 2003 2 50 Marine Environment Protection Committee, MEPC973 978 MARPOL 73/78 I 3G 200 205 25 3H Deadweight Tonnage,DWT Ultra Large Crude Carrier,ULCCVery Large Crude Carrier,VLCC AframaxPanamaxHandymax Handysize VLCC Aframax

(94) Black976 leverage effect Fornari and Mele997 Sign- and Volatility-Switching ARCH GJR- GARCH S&P 500 Index Topix Index CAC40 Index FT-00 Index FAZ Index MIB Index 990 995 0 6 494 Sign- and Volatility-Switching ARCH GJR-GARCH Koutmos998 MLE TGARCH Threshold GARCH Model All Ordinary Index General Index Toronto S.E. 300 Index CAC General Index Commerzbank Index BCI General Index Tokyo S.E. All Stock Index Financial Times 500 Share Index S&P 500 Index 986 4 99 2 3 562 Kassimatis2002 Argentina Datastream Total Stock Market Index 88//5 98/2/27989 2 Bombay S.E. National Index89/7/3 98/2/27992 Korea S. E. Composite Price Index88//5 97/8/3 992 Karachi S. E. 00 Price Index89/7/3 97/8/399 2 Philippines S. E. Composite Price Index88//5 98/2/2799 Taiwan S. E. Weighted Price Index88//5 98/2/2799 EGARCH

(95) Wu and Xiao2002 EARCH return shock conditional volatility Volatility Index,VIX00 Standard and Poor s 00 index, S&P 00 Index 986 999 2 EARCH EGARCH Chen, et al.2003 CAC 40 Index Dax 30 Index FTSE 00 Index Nikkei 225 IndexSwiss Market Price IndexToronto SE 300 Index S&P 500 Index 985 200 4 GARCH Double- Threshold Autoregressive GARCH Model, DTAR-GARCH Model leverage effect Chen, and Yu2005long memory Hang Seng Index, HSIStraits Times Industrial Index, STIITaiwan S.E. Weighted Stock Price Index, TAIEX Korea S.E. Composite Price IndexKCPINikkei 225 IndexS&P 500 Index 985 2002 2 ARFIMA-TGARCH Autoregressive Fractionally Integrated Moving Average With Threshold GARCH Model Markov chain Monte Carlo Method, MCMC Method ARFIMA TGARCH Blenman, et al.2005 Merval Index988/7 997/2, 222 Bovespa Stock Index993/ 997/2, 03

(96) IGPA Index988/7 997/2, 222 IGBVLIndex993/ 997/2, 040 Caracas IBC Index989/ 997/2, 2082 EGARCH-M EGARCH In Mean Model volatility persistence Pati2006 Nifty Index Futures 2002 2005 2 29 009 ARMA-GARCHAutoregressive Moving Averege- GARCH ARMA-EGARCHAutoregressive Moving Averege-EGARCH Maturity Effect Mohanty2006 GARCH EGARCHGQARCHGeneral Quadratic ARCHGJR GARCH GARCH Sensitive indexbse 00from Bombay Stock ExchangeS&P CNX 500S&P CNX Niftyfrom National Stock Exchange 995 4 2005 3 3 Chong Hendry 986 Forecasting Encompassing Test EGARCH EGARCH EGARCH GARCH EGARCH EGARCH GARCH Leeves2007 Jakarta S.E. Index DataStream 990 4 2 999 2 3 GlostenJagannathan Runkle993 GJR-GARCH Sentena992 Generalized Quadratic GARCHAGARCH Engle Ng993 Nonlinear Asymmetric GARCHNGARCH 990 999 ARCH GARCH 997 999 Baharumshah and Wooi2007

(97) IMF International Financial Statistics 6 995 2002 2 EGARCH 995 997 6 997 7 2002 2 EGARCH, Box and Jenkins976 ARMAAutoregressive Moving Averege Engle 982 Autoregressive Conditional Heteroskedasticity, ARCH Bollerslev986Generalized Autoregressive Conditional Heteroskedasticity, GARCH Bollerslev et al. 992 ARCHGARCH Thick Tails,Heavy Tails,Fat TailsVolatility Clustering Nelson99Exponential Generalized Autoregressive Conditional Heteroskedasticity, EGARCH Engle & Ng993News Impact Curve Model ARCH ARCH-Family GARCH, EGARCH, AGARCH, VGARCH, NGARCH, GJR- GARCHGJR-GARCH EGARCH EGARCH EGARCH ARMA

(98) VLCC 30 Suezmax 5 Aframax 0.5 Clarkson Research Services Limited 2000 4 7 2007 2 28 404 VLCC 404 2000.4.7 2007.2.28 Clarkson Research Services Limited Suezmax 404 2000.4.7 2007.2.28 Clarkson Research Services Limited Aframax 404 2000.4.7 2007.2.28 Clarkson Research Services Limited H 0 VLCC H 02 Suezmax H 03 Aframax.ARMA-EGARCH ARMA-EGARCH Autoregressive Moving

(99) Averege, ARMAExponential Generalized Autoregressive Conditional Heteroskedasticity, EGARCH ARMA EGARCH ARMA EGARCH ARMA-EGARCH ARMA p,q-egarchm,n p q i= t i i= y = A0 + Ai y + Biε t i + ε t ε t ~ N(0, ht) t n m { ε t i ε t i 2 ln( ht) = α 0 + α i γ + [ ] } + β j ln( ht j) i= h j= t i h t i π ε t y t pqmn ht A0 Ai Bi α0 α i β j γ γ γ <0 EGARCHm,nε t i <0 y t <α 0 α 0 ε t i <0 γ <0 ε t i <0 γ <0 γ <0 ARMA Autocorrelation Function,ACF Partial Autocorrelation Function,PACF ARMA p q Q Kmenta,986Q p p 2 Q( p) = T ( T + 2) ρ ( i) /( T i) ~ χ 2 ( p) 2 i T ρ (i) i H 0 p p,q AIC Akaike Information Criterion SBC Schwartz Bayesian Information CriterionAIC SBC AIC=T lnsse+2k SBC=T lnsse+k lnt 3 4

(200) T k SSE EGARCH ARMA EGARCH Q 2 ARCH-LM Engle,982 p Q 2 2 Q( q) = T ( T + 2) ρ ( i) /( T i) ~ χ 2 ( q) 5 T ρ (i) i i ARCH-LM TR2 ~ χ 2 ( q) 6 T R2 q H 0 q q 2.News Impact Curve Model EGARCH EGARCH EGARCH ε ln( ht) = α + α i h ε m t i t i { γ + [ ] } + n 0 β j ln( ht j) i= j= t i t i π γ + α ht = C exp yt σ when y t >0 γ α ht = C exp yt σ when y t <0 h 2 2 2 C = σ β exp α 0 σ 7 π ε t y t mn ht σ ht α 0 α i β j γ γ γ <0

(20) VLCCSuezmaxAframax 2000 4 7 2007 2 28 404 t r t r t =lny t -lny t- r t =lny t / y t- y t t 2 3 VLCC 2 Suezmax

(202) 3 Aframax 2 3 VLCCSuezmaxAframax 2 2 VLCCSuezmaxAframax VLCC_RETURN SUEZMAX_RETURN AFRAMAX_RETURN Mean 0.0024 0.00570 0.00444 Median 0.000000 0.000000 0.000000 Maximum 0.252997 0.276253 0.3353 Minimum -0.56004-0.22344-0.0870 Std. Dev. 0.048088 0.034907 0.02575 Skewness.757932.8522 0.880965 Kurtosis 0.25904 2.06227 8.870258 Jarque-Bera 092.38 5572.559 630.7675 Probability <0.000** <0.000** <0.000** Observations 403 403 403 ** =0.05 2 VLCCSuezmaxAframax VLCCSuezmaxAframa 0

(203) 3 Jarque-Bera p-value ARMA-EGARCH VLCCSuezmaxAframax ARMA EGARCH.VLCC VLCC ARMA[, 6]- EGARCH3,2 3 4 R t = A Rt + B6ε t 6 + ε t ε t ~ N(0, ht) 2 3 ε t i ε t i 2 ln( ht) = α 0 + α i{ γ + [ ] } + β j ln( ht j) 8 i= h j= t i ht i π Rt VLCC ht 3 VLCC ARMA Variable Coefficient Std. Error t-statistic Prob. AR 0.36602 0.049625 2.752684 0.0062** MA6-0.44457 0.050-2.826876 0.0049** R-squared 0.036347 Mean dependent var 0.002042 Adjusted R-squared 0.033938 S.D. dependent var 0.04827 S.E. of regression 0.047303 Akaike info criterion -3.25956 Sum squared resid 0.895036 Schwarz criterion -3.239633 Log likelihood 657.628 Durbin-Watson stat 2.0202 ** =0.05

(204) 4 VLCC ARMA-EGARCH A 0.24899 β -.038586 B6-0.374 β 2 0.824906 α 0-2.687986 β 3 0.87585 α 0.380305 γ 0.004500 α 2 0.409562 σ ht 0.002052 4 ARMA[,6]- EGARCH3,2 7 4 4 VLCC 4 VLCC γ >0 γ 0 γ =0.004500VLCC >0 <0 0 0 VLCC H0VLCC

(205) 2.Suezmax Suezmax ARMA[, 0]- EGARCH8,5 5 6 R t = A Rt + ε t ε t ~ N (0, ht) 5 8 { ε t i ε t i 2 ln( ht) = α 0 + α i γ + [ ] } + β j ln( ht j) 9 i= h j = t i ht i π Rt Suezmax ht 5 Suezmax ARMA Variable Coefficient Std. Error t-statistic Prob. AR 0.73945 0.04976 3.53774 0.0005** R-squared 0.028287 Mean dependent var 0.00573 Adjusted R-squared 0.028287 S.D. dependent var 0.03495 S.E. of regression 0.034453 Akaike info criterion -3.895968 Sum squared resid 0.475985 Schwarz criterion -3.886027 Log likelihood 784.0896 Durbin-Watson stat 2.0443 ** =0.05 6 Suezmax ARMA-EGARCH A 0.55096 β -0.49908 α 0-4.978594 β 2 0.36274 α 0.522647 β 3 0.424475 α 2 0.497924 β 4-0.828 α 3 0.28675 β 5 0.387032 α 4 0.88766 β 6 0.82342 α 5 0.43859 β 7 0.290907 γ 0.05232 β 8-0.59930 σ ht 0.00498 6 ARMA,0- EGARCH8,5 7 5

(206) 5 Suezmax 5 Suezmax γ >0 Suezmax γ 0γ =0.05232 0 0 Suezmax H 02 Suezmax 3.Aframax Aframax ARMA[, 3]- EGARCH0, 7 8 R t = A Rt + B3ε t 3 + ε t ε t ~ N(0, ht) ε t i ε t i 2 ln( ht ) = α 0 + α i{ γ + [ ] } 0 i= ht i ht i π Rt Aframax ht

(207) 7 Aframax ARMA Variable Coefficient Std. Error t-statistic Prob. AR 0.93792 0.049255 3.934446 0.000** MA3 0.65989 0.049740 3.33726 0.0009** R-squared 0.0684 Mean dependent var 0.00448 Adjusted R-squared 0.065784 S.D. dependent var 0.025206 S.E. of regression 0.024363 Akaike info criterion -4.586556 Sum squared resid 0.23749 Schwarz criterion -4.566673 Log likelihood 923.8977 Durbin-Watson stat 2.022259 ** =0.05 8 Aframax ARMA-EGARCH A 0.20507 α 0.297778 β 3 0.2777 γ -0.4348 α 0-7.648860 σ ht 0.000203 8 ARMA,3- EGARCH0, 7 6 6 Aframax 6 VLCC γ <0 γ =-0.4348 Aframax r t >0

(208) 0 r t <0 Aframax H 03 Aframax VLCCSuezmaxAframax Aframax γ VLCCSuezmax γ 0 0 VLCCSuezmaxAframax ARMA EGARCH EGARCH VLCCSuezmaxAframax Aframax Suezmax Aframax Aframax

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(20) from major stock markets based on a double-threshold model. Journal of Economics and Business, 55, 487-502. 6. Chen, C. W. S., & Yu, T. H. K. (2005). Long-term dependence with asymmetric conditional heteroscedasticity in stock returns. Physica A: Statistical Mechanics and its Applications, 353, 43-424 7. Blenman, L. P., Chatterjee, A., & Ayadi, O. F. (2005). Volatility persistence, market anomalies and risk in Latin American equity markets. The International Journal of Finance, 7(2), 343-3445. 8. Pati, P. C. (2006). Maturity and volume effects on the volatility: Evidences from NSE fifty futures. 0th Capital Markets Conference, Indian Institute of Capital Markets Paper. 9. Mohanty, P. (2006). A study of asymmetric volatility in the Indian equity market: A GARCH approach. Decision, 33(), 2-34. 0. Leeves, G. (2007). Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia. International Review of Economics & Finance, 6(2), 272-286.. Baharumshah, A. Z., & Wooi, H. C. (2007). Exchange rate volatility and the Asian financial crisis: Evidence from South Korea and ASEAN-5. Review of Pacific Basin Financial Markets and Policies, 0(2), 237-264. 2. Black, F. (976). Studies in stock price volatility changes. Proceedings of the 976 Business Meeting of the Business and Economic Statistics Section. American StatisticalAssociation, 77 8. 3. Box, G. E. P., & Jenkins, G. M. (976). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, Calif. 4. Engle, R.F. (982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987-007. 5. Bollerslev, T. (986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 3, 307-327. 6. Nelson, D. B. (99). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370. 7. Engle, R. F., & Ng, V. K. (993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, 749-778. 8. Christie, A. (982).The Stochastic Behavior of Common Stock Variance: Value, Leverage and interest Rate Effects. Journal of Financial Economics, 0,407-432. 9. Nelson, D. B. (99). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370. 20. Tvedt, J. (2003).A new perspective on price dynamics of the dry bulk market. Maritime Policy and Management, 30 (3), 22-230. 2. Kavussanos, M.G. (996a).Comparisions of Freight Market Volatility in the Dry-Cargo Ship Sector. Spot v.s. Time-Charter and Smaller v.s.larger Vessels, Journal of Transport Economics and Policy, XXX, 67-82. 22. Bollerslev, T. (987). A Conditional Heteroskedastic Time Series Model for Speculative Price and Rates of Return. Reviews of Economics and Statistics, 69, 542-547. 23. Bollerslev, T., Chou, R., & Kroner, K. (992). ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence. Journal of Econometrics, 52, 5-59.