Effects of daylight saving time changes on stock market volatility: a reply
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(2) 864. H. BERUMENT & N. DOGAN. ȱȱȱŘŘȱȱȱȱȱ ǰȱ ¢Ȭǰȱ
(3) ǰȱȱȱǻŘŖŗŖǼȱȱȱęȱ¢ȱęȱȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ǯȱ ȱ ǯǯȱ ǰȱ ǰȱ ǰȱȱȱǻŘŖŖŞǼȱȱȱȱěȱȱȱȱȱ ȱȱȱ¢ǰȱȱ ȱȱȱȱȱǯȱ¢ȱȱ ȱȱȱȱȱěȱȱȱȱ¢ǯȱ ȱ ęȱ ¢ȱ ęȱ ȱ ȱ ȱ ȱ ȱ ȱȱ¢ȱȱȱȱȱȱȱȱěȱȱȱǯȱ ȱ¢ȱȱȱȱěǰȱȱȱ¢ȱȱȱȱȱȱȱ¢ȱ ¡ǯȱ Ȃȱ ȱ ȱ ǻŘŖŖśǼǰȱ ǰȱ ǰȱ ȱ ȱ ǻŘŖŖŜǼǰȱȱ ȱǻŘŖŖŝǼȱȱȱȱȱȱȱ ȱ ȱȱȱǯȱȱȱȱȱ¢ȱȱȱȱȱ ȱ ȱȱȱȱĞȱȱȱȱȱȱȱȱȱ ȱǻȱȱ ȱȱǼȯȬȱȱȱȱ ȱȱȱȱĞȱȱȱǯȱ юѦљієѕѡȱюѣіћєȱіњђȱѕюћєђѠǰȱђѡѢџћǰȱюћёȱќљюѡіљіѡѦ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ¢ȱ Ĵȱ ȱ ¢ȱ ȱ ȱ ȱȱȱ¢ȱǯȱȱȱ ȱȱȱȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ğȱ ȱ ȱ ȱ ȱ ěȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ Ĝ¢ǰȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ǻǰȱ ŗşşŜǼǯȱȱ ȱ ȱ ȱ ȱ ǻŘŖŖŗǼȱ ȱ ȱ ȱ ȱ ěȱȱȱȱȱȱȱĴȱȱȱȱȱȱ ȱȱȱ¢ȱȱȱǯȱ¢ǰȱ¢ȱěȱȱęȱȱȱȱȱȱ¢ȱȱǯȱȱ¢ȱȱȱȱĞȱ ȱȱȱȱȱȱȱȱ ěȱȬȱ¢ȱ¢ǯȱǰȱ ǰȱȱ ȱ ǻŘŖŖŗǼȱȱȱȬ¢ȱȱȱȱȱǰȱȱȱȱǰȱȱȱȱȱȱȱǯȱȱȱ¢ȱěȱȱ¢ȱȱȱǻĞȱǭȱǰȱŘŖŖřǼǰȱ ȱȱȱȱȱȱȱȱ ȱȬȱȱ ȱȱǯȱ ¢ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ǰȱȱȱȱȱȱǯȱ ȱȱȱȱǯǯǰȱȱȱ ȱ ȱ Ĝ¢ȱ ȱ ŗşŜŜǯȱ ȱ ȱ ȱ ȱ ȱ ¢ȱȱȱȱȱĜȱǯȱȱȱȱ ǻŘŖŖŗǼȱȱȱęȱȱȱȱȱȱȱǯǯȱ¢ȱ ȱȱĞȱȱȱȱȱȱȱǯȱ¢ȱȱȱ ȱȱȱȱȱȱ¢ȱȱȱȱȱ ȱȱȃȱȱȱȱȱ¢ǰȄȱȱ ȱȱȱȱ sleep lȱȱȱǯȱȱǻŗşşŜǰȱŗşşŜǼȱȱȱęȱ-.
(4) DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY. 865. ȱȱĜȱȱȱȱȱȱȱ¢ȱȱȱȱȱ ǯǯȱ Ğȱ ȱ ǰȱ ȱ ȱ ȱ ¢ȱ ęȱ ěȱȱȱȱȱȱȱȱȱȱǯȱȱǰȱȱȱȱǻŗşŞŖǼȱȱ ǰȱǰȱȱ ȱǻŗşŞřǼǰȱȱ ȱ ȱ Ĝȱ ȱ ȱ ȱ ȱ Ğȱ ȱ ǰȱ ȱ ȱ ȱȱęȱ¢ȱ¢ȱęȱȱ ȱȱȱȱ ǯȱȱȱȱǰȱǰȱǰȱǰȱ ǰȱȱȱ ǻŗşşśǼǰȱȱȱȱǻŗşŞşǼǰȱȱ¢ěȱǻŗşŝŞǼȱ ȱȱȱȱȱǰȱę¢ȱȱȱǰȱĞȱȱȱ ǯȱȱȱǰȱȱȱȱȱȱęȱ ¢ȱ ǰȱ ǰȱ et al.ȱ ǻŘŖŖŖǼȱ ¡ȱ ȱ ȱ ěȱ ȱ ȱ ǯȱȱȱȱȱȱȱȱǯǯǰȱǯ ǯǰȱȱȱȱǰȱȱ ȱȱ¢¢ȱ ȱ¢ȱȱȱȱȱȱ¡ǰȱȱ¢ȱȱ ȱȱȱȱȱ ǯȱ ȱȱěȱȱȱȱȱȱȱ ȱ ¡ȱȱȱ ȱȱȱȱȱ¢ȱǯȱȱěȱȱ¢ȱȱǯȱȱȱȱ ¢ȱȱȱȱȱȱȱȱ ȱȱȱȱȱȱȱȱěȱȱȱȱȱǯȱȂȱȱȱǻŘŖŖśǼȱȱȱȱ ȱ ȱ ȱ ȱ ¢ȱ ę¢ȱ ȱ Ȭȱ ȱȱȱȱȬȱȱȱȱǰȱ ȱ ȱ ȱ ȱ ȱ ȱ Ĝȱ ǰȱ ȱ ǰȱ ȱ ȱ ǯȱ ǰȱ ¢ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ Ȭȱ ȱ ǰȱ ǰȱ et al. ǻŘŖŖŜǼȱ ȱȱȬȱȱȱȬȱǯȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ǰȱȱȱȱȱȱǰȱ ȱǻŘŖŖŝǼȱȱȱ ȱȱȱȱȱȬȱȱȬȱ ǯȱ ǰȱǰȱȱȱǻŗşŝŖǼȱ ȱȱȱȱ ȱ ȱ ¡ȱ ȱ ȱ ¢ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ¢ȱȱȱȱȱȱȱȱȱȱȱ the tired driver to pass. ȱęȱȱȱȱȱȱȱ ȱǯȱ ȱȱȱȱȱȱȱȱǰȱȂȱ ȱȱȱȱǻŗşŝřǼȱ ȱȱȱȱ¡ȱȱȱȱȱȱȱȱȱȱȱǻȱȱǼȱȱȱȱ ȱȱȱǻȱȱnent). In a later study, Merton (1980) argued that under certain conditions, ȱȱȱȱȱȱȱȱȱǰȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ.
(5) 866. H. BERUMENT & N. DOGAN. ȱ¢ȱ £ȱǻŗşśŘǼǰȱȱǻŗşŜŚ), Lintner (1965), and MosȱǻŗşŜŜǼȱȱȱĜȱȱǯȱȱȱȱǰȱȱȱȱȱǰȱ¢ȱȱȱȱ¡ȱȱȱ ȱȱǰȱȱȱȱȱ ȱȱȱ ȱȱȱȱȱǯȱ¢ǰȱȱȱȱȱǰȱ ¢ȱ ȱȱ¢ȱǰȱ ȱȱ¡ȱȱ¡ȱǰȱȱ then the relationship should be negative. ȱȱȱǻŗşŝřǼȱ¡ȱȱȱ ȱȱ ȱȱȱȱȱ ȱȱȱ¡ȱǻǼǯȱ¢ȱȱȱ ȱȱ¢ȱȱȱȱȱȱȱĚȱȱĴȱ ȱȬȱȱȱȱȱȱȱȃĜȄȱȱȱȱ¡ȱȱȱȱȱǯȱ
(6) ȱǻŗşŝŗǼȱ¡ȱȱĜȱ ȱ¢ȱȱȱǰȱȱȱȱ ȱȱęȱȱȱ ȱ£ȱȱȱǯȱ
(7) ǰȱǰȱȱȂȱȱǻŗşŝŘǼǰȱ ȱȱȱȱȱȱȱǰȱȱ ȱ¡ȱȱȱȱȱȱ ȱȱȱȱȱ ȱǯȱȱȱȱȱȱȱȱȱȱȱȱȱȱ ȱȱ ȱȱȱ¡¢ȱ ȱǰȱȱȱǰȱ ǰȱȱȱǻŗşŞŝǼǰȱ¢ȱǻŗşŞşǼǰȱ ȱȱ ȱǻŗşşŖǼǰȱȱȱȱ ¢£ȱǻŘŖŖŗǼǯȱ ȱȱȱȱȱȱȱȱ ȱȱ¡ȱȱȱ ȱȱȱȱǻ ȱȱȱȱ ȱ¢Ǽȱ ȱ ȱǯȱ ȱȱȱȱȱȱ ȱȬȱǰȱȱȱȱȱȱĞȱȱȱȱȱȱȱȱȱǰȱȱȬȱȱ ȱȱȱȱǯ юѡю ȱȱ¢ǰȱȱȱȱȱȱȱȱȱȱǰȱȱȱȱȱȱ¡ȱȱ ǰȱet al.ȱǻŘŖŖŖǼǰȱ ȱȱȱȱȱȱȱȱȱȱDZȱȱǰȱȱȱǭȱȂȱ śŖŖȱǻǭśŖŖǼǰȱȱȱȱȱȱȱȱ ȱǻǼǰȱȱȱȱȱ¡ȱǻǼǯȱȱ ȱȱȱȱȬȱȱȬ ȱ¢ȱ¡ȱȱȱȱ ȱ ȱȱ ȱ ȱ ȱ ¢ȱ ȱ ǻǼǯȱ ȱȱ ȱ ȱ ȱ ǰȱ ǭśŖŖǰȱ ȱ ȱ ¡ȱ ȱ ȱ
(8) ¢ȱ řǰȱ ŗşŜŝǰȱ ȱ ȱ ȱ ¡ȱ ȱ ȱ ȱ ŗśǰȱ ŗşŝŘǯȱ ȱ ȱ ȱȱȱ¡ȱȱȱ
(9) ȱŘşǰȱŘŖŖŝǯ ȱ ¡ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǰȱȱ¢ȱȱ¢ȱȱǰȱȱ¢ȱĴȱ Ĵȱȱǰȱȱǰȱȱ¢ȱǯȱȱ ȱ ȱ ǻŗşŞřǼǰȱ ȱ ȱ £ȱ ǻŗşŞśǼǰȱ ȱ ǰȱ ¢ǰȱǰȱȱȱǻŗşşŞǼȱȱ¢ȱěȱ ȱ.
(10) DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY. 867. Ȭ ȱȱȱȬ ȱǯȱ ȱȱȱȱǰȱȱȱȱȱȬȱȱȬ ȱ¡ǯ ¢ȱȱȱȱȱȱȱȱȱȱȱǯǯȱȱ World War I, and its use gradually increased. In the early 1960s, the obȱȱȱ ȱȱǰȱ ȱȱȱȱ ȱȱ ȱǯȱ¢ȱŗşŜŜǰȱȱȱȱȱȱȱȱȱȱ ¢ȱȱȱĴȱȱȱ¢ǯȱȱȱǰȱȱȱ ȱ ȱ ȱ ŗşŜŝǯȱ ȱ ŗşŞŜǰȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱȱȱȱȱȱȱȱ¢ȱȱǯȱǯǯȱ ȱ ȱ ȱȱŗşŞŜȱȱȱȱȱȱęȱ¢ȱȱǯȱǰȱȱȱŘŖŖŝǰȱȱȱȱȱȱȱȱȱȱȱ¢ȱ ȱȱȱȱȱȱȱȱȱęȱ¢ȱȱǯȱ ȱ ȱȱȱȱȱȱ¢ȱŗşŝŚǯ3ȱĞȱ¢£ȱȱǰȱȱ¢ȱȱȱȱ ȱȱȱȱęȱȱ¢ȱ Ğȱȱȱȱȱȱȱȱȱ£ȱ ȱȱ ȱ for analyses. ȱȱŗǰȱȱȱȱȱ¢ȱȱȱ ȱǯȱȱ ȱȱ¡ȱȱȱȱȱȱǻǰȱǭśŖŖǰȱǰȱ ȱ Ǽȱ ȱ ȱ Ȭȱ ȱ Ȭ ȱ ¡ȱ ȱ ¢ǰȱ ȱ ǻ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ Ǽǰȱ ȱ ȱ ǻ¢ȱ ȱ ȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ Ǽǰȱ ȱ ȱȱǻ¢ȱ ȱ¢ȱȱȱȱȱǰȱ ȱȱȱ ȱǼǯȱȱ¡ȱȱȱ¢ȱȱ ¢ȱǰȱ ȱ ȱȃȱ¢ǰȄȱ¡ȱȱȱȱȱȱ¡ǯȱȱȱ ǰȱȱǰȱȱȱȱ¢ȱȱ ¢ȱȱȱȱ ȱȱȱȱ¢ȱȱ¢ȱ¡ǯȱȱȱȱȱ ȱȱȱȱǰȱȱȱȱȱȱ¢ȱȱ ȱȱǯȱȱŘȱȱȱȱȱȱ¢ȱȱȱȱ¡ǯȱ ȱǰȱȱǰȱȱǰȱȱ¢ǰȱȱȱȱ are observed for DST Fall. On the other hand, regular Monday volatilities ȱ ¢ȱȱȱȱȱ¢Ȃȱȱȱȱ¡ǯȱ ќёђљȱюћёȱѠѡіњюѡђѠ ȱȱǰȱȱ¡ȱȱěȱȱȱȱȱȱȬ¢ȱ ǰȱ ȱ ȱ ȱ Ȃȱ ¡ȱ £ȱ ȱ ȱ ȱ ǻ Dzȱ ŗşşŗǼȱ ȱ ȱ ¢ȱ ȱǯȱ ȱȱȱȱȱȬȱ ȱȱȱǰȱȱȱȱ ȱȱȱ¢¢ȱȱ ȱȱȱȱȱȱǰȱht2ǯȱȱȱȱ ȱěȱȱȱȱǰȱRǰȱȱȱȱȱȱȱ ǰȱęǰȱht2 could be genȱȱȱȱęȱǰȱ¡ǰȱ ȱȱȱȱ¢DZȱĴDZȦȦǯǯ¢ǯȦȦȦ¢ȏǯȱ ȱ ȱ¡DZȱĴDZȦȦ ǯ ¡ǯȦ¢Ȧǯǯ. 3.
(11) Value Equal Value Equal Value Equal Value. NYSE. S&P500. S&P500. NASDAQ. NASDAQ. . . M No. observations M No. observations M No. observations M No. observations M No. observations M No. observations M No. observations M No. observations. 0.07 10,192 0.05 10,192 0.06 10,192 0.05 10,192 0.10 8,716 0.05 8,716 0.09 10,192 0.04 10,192. All. Other Days 0.11 8,080 0.07 8,080 0.08 8,080 0.06 8,080 0.15 6,915 0.09 6,915 0.14 8,080 0.09 8,080. Monday ƺŖǯŖŝȱ 2,112 ƺŖǯŖřȱ 2,112 ƺŖǯŖřȱ 2,112 ƺŖǯŖŗȱ 2,112 ƺŖǯŖş 1,801 ƺŖǯŗŗ 1,801 ƺŖǯŖŞȱ 2,112 ƺŖǯŗŘȱ 2,112. Spring & Fall ƺŖǯŘşȱ 78 ƺŖǯŘŗȱ 78 ƺŖǯŘŘȱ 78 ƺŖǯŘŘȱ 78 ƺŖǯřŜ 66 ƺŖǯŚŞ 66 ƺŖǯřśȱ 78 ƺŖǯřşȱ 78. Spring ƺŖǯŗśȱ 40 ƺŖǯŖŘȱ 40 ƺŖǯŖřȱ 40 ƺŖǯŖŘȱ 40 ƺŖǯŘŝ 34 ƺŖǯřŖ 34 ƺŖǯŘŚȱ 40 ƺŖǯŘŗȱ 40. ¢ȱȱ Fall ƺŖǯŚřȱ 38 ƺŖǯŚŗȱ 38 ƺŖǯŚŘȱ 38 ƺŖǯŚřȱ 38 ƺŖǯŚś 32 ƺŖǯŜŝ 32 ƺŖǯŚŝȱ 38 ƺŖǯśŝȱ 38. Note.—DZȱ ȱȱȱ¡DzȱǰȱǭśŖŖDZȱȱǭȱȂȱśŖŖDzȱDZȱȱȱȱȱȱȱ DzȱDZȱȱȱ¡ǯ. Equal. Weighted. NYSE. ¡. TABLE 1 ђюћȱюћёȱѢњяђџȱќѓȱяѠђџѣюѡіќћѠȱќѓȱюіљѦȱюѤȱђѡѢџћȱюѡюȱѣђџȱѡѕђȱ
(12) юћѢюџѦȱřǰȱŗşŜŝǰȱѡќȱ
(13) ѢћђȱŘşǰȱŘŖŖŝǰȱђџіќёȱ. 868 H. BERUMENT & N. DOGAN.
(14) 869. DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY. TABLE 2 юџіюћѐђȱќѓȱюіљѦȱюѤȱђѡѢџћȱюѡюȱќѣђџȱѡѕђȱ
(15) юћѢюџѦȱřǰȱŗşŜŝǰȱѡќȱ
(16) ѢћђȱŘşǰȱŘŖŖŝǰȱђџіќё ¡. NYSE NYSE S&P500 S&P500 NASDAQ NASDAQ . Weighted. Equal Value Equal Value Equal Value Equal Value. All. 0.57 0.77 0.86 0.91 0.57 1.46 0.57 0.77. Monday. 0.79 1.06 1.18 1.22 0.69 1.69 0.75 0.97. Other Days 0.51 0.70 0.78 0.83 0.52 1.39 0.52 0.71. ¢ȱȱ Spring & Fall. Spring. Fall. 1.68 1.91 2.26 2.04 2.01 3.92 1.71 2.11. 0.51 0.60 0.63 0.64 0.97 2.76 0.63 0.86. 2.91 3.25 3.96 3.48 3.16 5.20 2.88 3.41. Note.—DZȱ ȱ ȱ ȱ ¡Dzȱ ǭśŖŖDZȱ ȱ ǭȱ Ȃȱ śŖŖDzȱ DZȱ ȱȱȱȱȱȱDzȱDZȱȱȱ¡change.. ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǯȱȱǻŗşŞŚǼȱȱȱ ȱȱȱǻȱȱȱǼȱȱȱȱǯȱȱȱȱǻŗşŞŞǼȱȱ ȱȱȱ ȱ¡ȱȱȱȱȱǯȱ ǰȱ ȱȱȱȱǻŗşşŘǼǰȱȱȱȱȱȱȬ¡ȱȱȱǻǼȱȱȱȱǯ4ȱȱȱȱȱȱȱ DZ n. Rt = α 0 + α M M t + α DST DSTt + ∑ α i Rt −i + λht2 + λ M M t ht2 + λ DST DSTt ht2 + ε t ,. [1]. i =1. ȱMt and DSTt ȱ¢ȱȱȱ¢ȱȱȱȱȱ ȱȱȱȱȱȱt. In previous research, as in Cross (1973), French ǻŗşŞŖǼǰȱȱ ȱȱ ȱǻŗşŞŗǼǰȱȱȱȱȱȱȱ ȱ ¢ȱ ǯȱ ȱ ȱȱ ȱ ȱěȱ ȱ ¢ȱ ȱȱȱȱȱ¢ǰȱȱ¢ȱȱ ȱǯȱȱȱȱȱȱȱǰȱRtƺi, are included in ȱȱȱȱȱȱȱǯ5 A changing return-volatility relationship for regular Monday and DST changes in ȱȱȱȱ ǰȱȱȱȱ¢ȱȱǻȱ ¢ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱǼȱȱȱȱȱęǯȱǰȱȱ ȱȱȱǻŗşşŘǼȱȱȱȱ¢ȱȱȱ£ȱȱ errors İt/htȱȱ¢ȱȱęȱȱȱȱǯȱǰȱ¢ȱ ȱȱȱȱȱȱȱȱǯȱȱȱȱ¢ȱ ȱȱȱȱȱ¢ȱȱȱȱȱȱǯȱ 5
(17) ȱȱȱǻŗşŞŞǼȱȱȱȱȱȱ ȱěȱȱȱ¡ǰȱȱȱȱȱǻ ¢Ǽȱȱ ȱěǯȱȱȱȱȱ ȱȱ ȱȱȱȱǻǼȱǰȱ ȱȱȱȱȱȱȱȱ are no longer autocorrelated. 4.
(18) 870. H. BERUMENT & N. DOGAN. conditional variance ht2ȱȱȱȱȱȱȱ¢ȱȱȱǽŗǾǯȱ ȱȬ¢ȱȱȱȱȱȱ ȱDZȱ v ε ε ε log ht2 = κ + ∑ δ i log ht2−i + γ 1 t −1 − E t −1 + χ t −1 , ht −1 ht −1 i =1 ht −1. [2]. ȱΉt ȱȱ ȱȱȱǻ Ǽȱȱ¢ǰȱ. 2 Γ D E = Λ2 , ht 1 Γ D ȱ̆ǻǯǼȱȱȱȱǰ 1 − 2 Γ D Λ= 2D , 3 Γ D ȱȱȱȱȱȱȱ ǯ6 ȱ ȱȱȱ ȱȱěȱěȱȱȱȱ¢ȯȱȱěǯȱȱȱȤȱȱȱȱěǯȱ If ȤȶƽȶŖǰȱ ȱ ht2 ȱ ¢¢ȱ ȱ ǯȱ ȱ ƺŗȶǀȶȤȶǀȶŖǰȱ ȱȱȱ¢ȱȱȱȱȱDzȱȱȤȶǀȶƺŗǰȱ ȱȱȱȱ¢ȱ ȱȱȱȱ ¢ǯȱǰȱȱȱȱȱěǰȱȤȱȱȱǯȱ ȱ ȱęȱȱȱȱǽŗǾȱȱǽŘǾǰȱȱȱȱȱȱȬȱȱȬ ȱ¡ǰȱȱȱȱ Table 3.7ȱȱȱȱȱȱȱȱȱȱȱȱȱ ȱȱȱȱȱȱȱęǯȱǰȱ ȱȱȱȱ ȱȱp values of the robustness test statistics and a set ȱ Ȭȱ ǰȱ ¢ǯȱ Mt and DSTtȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ Ğȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱȱt. Moreover, Rƺ , ht2, and Ȥȱȱȱȱ for the lagged returns, the conditional variance of the returns, and the leȱěǰȱ¢ǯȱȱȱȱȱȱȱȱęǰȱȱ ȱȱȱȱĜȱȱ¢ȱ¢ȱȱȱ. εt. 1 D. 6 ȱ ȱȱȱȱȱȱ ȱȱȱȱt distributions are special cases. D ȱȱȱȱȱȱȱȱȱDzȱȱȶƽȶŘǰȱȱȱȱDzȱ ȱȶǀȶŘǰȱȱ¢ȱȱȱȱȱȱȱDzȱȱȱȶǁȶŘǰȱȱ¢ȱȱ thinner tails. 7 ȱ ȱęȱȱȱ¢ȱȬȱ¢ȱǰȱǰȱȱȱȱȱȱȱȱȱ¡£ȱǯȱȱȱȱȱȱȱ¢ȱȱȱȱȱȱȱĴDZȦȦ ǯǯǯȦDžȦŖŗǯDzȱĴDZȦȦ ǯ ǯǯȦDžȦŖŘǯDzȱȱĴDZȦȦ ǯǯǯȦDžȦŖřǯǯ.
(19) DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY. 871. ȱȱ¢ȱęȱȱȱ¡ǯȱȱęȱȱ ȱęȱ¢ȱȱǻŗşŜŘǼǰȱȱǻŗşŝřǼǰȱȱǻŗşŞŖǼǰȱ ȱȱ. ȱǻŗşŞŗǼǰȱ
(20) ěǰȱęǰȱȱȱǻŗşŞşǼǰȱǰȱǰȱȱȱ ǻŗşşřǼǰȱ ȱ ȱ ȱ ǻŗşşŚǼǰȱ ȱ ȱ ȱ ȱ ǻŗşşŜǼǯȱȱȱȱǰȱȱȱĜȱȱȱȱȱȱ ȱ¢ȱęȱ¢ȱȱȱ¢ȱ ȱǭśŖŖȱ ¡ǯȱ ȱȱȱȱȱȱȱȱ ǰȱet al.ȱǻŘŖŖŖǼǰȱ ȱȱȱȱȱěȱȱǯȱȱȱȱȱȱȱȱȱ ȱȱȱ ǰȱet alǯǰȱȱȱęȱȱȱȱ¢ȱ ǯȱ ȱ ȱ ȱ ǰȱ ȱ ȱ ȱ ǻŗşŞŜǼȱ ȱ ȱ ȱ ¢£ȱǻŘŖŖŗǼȱȱȱ¢ȱȱȱ¢ǰȱ¢ǰȱ ȱ ǰȱet alǯǰȱȱȱȱȱĜȱȱǯȱȱȱ ȱȱȱȱ¢ȱȱȱ¢ǰȱȱȱȱȱȱ ȱȱęȱȱȱĜǯȱ ȱȱȱȱĜȱht2ȱȱ ¢ȱȱȱ¢ȱęȱȱȱ¡ǯȱȱȱȱȱȱȱȱ¢ȱ ȱ ǻŗşşŞǼǰȱ ȱ ȱ ȱ ǻŘŖŖŜǼǰȱ ȱ ¢ǰȱ ǰȱ ȱ ȱ ǻŘŖŖśǼǯȱ¢ȱȱȱȱȱĜȱȱȱȱȱȱ ȱȱȱǰȱȱȱȱȱǻ Ǽȱ¢ȱȱȱ ȱȱǻ ǼȱǯȱȱȱĜȱȱȱȱ¢ȱ¢ȱȱ ȱht2 ȱȱ¡ȱȱȱȬȱȱȬ ȱǭśŖŖȱȱȬ ȱȱ¡ǰȱ ȱȱȱ ¢ȱęǯ8ȱǰȱȱȱ¢ȱĜȱȱ¢ȱęȱȱȱȬ ȱȱȱȱ¡ȱȱȱŗƖȱȱŗŖƖȱǰȱȱȱȬȱȱȬ ȱȱ¡ȱȱȱŗƖȱǯȱ ¢ǰȱȱȱȱ¢ȱȱ ȱȱ¢ȱ ȱȱȱǯȱȱȱȱȱȱȱȱ ȱȱȱȱȱȱěȱȱ¢ȱęȱ¢ȱȱ ȱȬ ȱȱȱȱǭśŖŖȱ¡ǰȱȱȱȬ ȱȱ¡ȱȱȱśƖȱDzȱȱȱȱȬ ȱȱ ȱȱ¡ȱȱȱŗŖƖȱǯȱȱȱ ȱȱȱȱȱȱȱěȱȱȱȱȱ¢ǯȱȱȱĜȱȱȱǰȱ ȜȶƸȶȜMȶƸȶȜDSTǰȱȱ ¢ȱȱȱ¢ȱęȱȱȱŗŖƖȱȱȱęȱȱȱȱ¡ȱǯȱǰȱȱȬ¢ȱlationship changes on the dates of the DST changes,9 and therefore, the ȱȱȱęȱȱśƖǰȱȱ ȱǯ ȱǻŘŖŖŘǼȱȱȱȱȱ ǰȱet al. (2000), arguing that the DST efȱȱęȱ¢ȱȱȱȱȱȱȱěȱȱȱ¢ȱȱȱ ȱ ȱǯȱȱȱǰȱȱȱȱ ȱȱ ȱȱȱȱȱ ȱ¢ȱǰȱǰȱȱ£ȱǻŘŖŖŝǼǯȱȱȱ ħȂȱȱǻŗşşşǼȱ ȱȱ ȱȱȱȱȱȱȱȱȱȱ¡ǯȱȱȱȱȱǯȱ ȱȱȱȱȱȱȱȱȱȱȱȱȱǯ. 8 9.
(21) 872. H. BERUMENT & N. DOGAN. ȱȱ¢ȱȬĜȱȱ¢ȱȱȱȱȬ£ǯȱȱ¢ȱĜȱȱȱȱȱ¢ȱȱěȱȱDzȱ ǰȱȱȱěȱ ȱȱ ȱȱǯ ȱȱȱȱȱřǰȱȱȱȱȱȱȱęȱȱǯȱȱȱ¢ȱȱȱȱȱȱ ȱȱȱ£ȱȱȱȱȱȱȱ ȱȱǯȱȱȱȱȱȱ¢ȱȱȱ ȱȱȱǰȱȱȱȱȱęȱȱȱ¡ȱǻȱǰȱŗşşŗǼǯȱȱȱĜȱȱȤȱȱ ¢ȱȱ ȱ¢ȱęȱȱȱŗƖȱȱȱȱȱ¡ȱǯȱ ȱęȱȱȱ ȱȱȱ¢DZȱȱȱ ȱ¢ȱȱȱȱǰȱ ȱȱ¢ȱȱ ǰȱȱȱȱȱȱǻŗşşŘǼȱȱ ȱȱ ȱǻŗşşŚǼǯȱ vey and Shephard (1996) and Yu (2005) point out that if Ȥȱȱěȱȱ £ǰȱȱȱȱȱ¢ȱȱȱȱĜȱȱ¢ǰȱȱȱȱȱȱȂȱȱȱ ȱȱ¢ȱ ȱǯȱȱȱȱȱȱ ȱȱȱěȱȱȱȱ ȱȱȱȱȱ¢ǯȱ ȱȱȱȱȱȱ ȱěȱ ȱǰȱȱ the pȱȱȱȱȱȱȱȱȱ ȱDzȱȱęȱ ȱȱȱȱȱȱȬ¡ȱȬȱȱȱ£ȱDzȱȱȱǰȱȱȱ£ȱDzȱȱȱǰȱ the Lagrange Multiplier (LM) test for the 5, 10, 20, and 60 lags. As for the ȱȱȱȬ¡ȱȬǰȱ ȱȱȱȱȱ¢ȱȱ £ȱȱȱȱȱȱ¢ȱ ȱȱ ¡ȱȱŜŖȱȱȱśƖǰȱ¢ȱ ȱȱ¡ȱȱśȱȱ ŘŖȱȱȱŗŖƖǰȱȱŜŖȱȱȱŗƖǰȱȱ¢ȱ ȱȱ¡ȱ ȱśǰȱŘŖǰȱȱŜŖȱȱȱŗƖȱȱŗŖȱȱȱŗŖƖǯȱȱȱȱȱȱ ȱ ȱ Ȭ¡ȱ Ȭȱ ȱ ȱ ȱ £ȱ ǯȱ ȱȱ¢ȱȱȱȱ£ȱȱȱȱȱȱ¢ȱȱȱȱȱ¢ȱ ȱǭśŖŖȱȱȱ ȱȱ¡ȱȱŜŖȱǯȱȱȱȱȱȱ Ȭ ȱȱȱȱ£ȱȱȱȱśǰȱŗŖǰȱŘŖǰȱȱŜŖȱǯȱȱ ȱȱ¢ȱęȱ ȱěȱȱȱ£ȱȱ¡ȱȱȱȬ ȱȱ¡ȱȱŗŖƖǰȱȱ ȱǭśŖŖȱ ¡ȱȱśȱȱŗŖƖȱȱŜŖȱǰȱȱ¢ȱ ȱȱ¡ȱȱ śȱȱȱŗŖƖȱȱŜŖȱȱȱśƖǰȱȱȬ ȱȱ¡ȱȱ ȱȱȱŗƖǰȱȱȱ ȱȱ¡ȱȱśȱȱŗŖȱȱȱśƖǯ ȱȱȱȬȱȬȱȱ£Ȭȱȱȱȱȱ ȱȱȱřǯȱȱȱȱȱȱ¢ȱȱ£ȱȱȱȱ¢ȱǻȱǰȱǰȱǭȱǰȱŘŖŖŝǰȱȱǼȱȱ¢ȱȱȱ¡ȱ ȱ ȱ¢ȱȱȱȬ£ȱDzȱ for equȬȱȱȬ ȱȱ¡ȱ ȱ ȱ¢ȱȬ.
(22) 873. DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY TABLE 3 юѦљієѕѡȱюѣіћєȱіњђȱѓѓђѐѡȱќћȱђѡѢџћȬѣќљюѡіљіѡѦȱђљюѡіќћѠѕіѝѠ ѣђџȱѡѕђȱ
(23) юћѢюџѦȱřǰȱŗşŜŝǰȱѡќȱ
(24) ѢћђȱŘşǰȱŘŖŖŝǰȱђџіќё NYSE Equal. Value. ȱDZȱȱę Constant 0.03‡ 0.04‡ t 3.08 2.83 ƺŖǯŖş‡ ƺŖǯŖŜ‡ Mt t ƺŚǯřŚ ƺŘǯŗŝ 0.06 0.16 DSTt t 0.69 1.35 0.31‡ 0.15‡ Rƺŗ t 30.23 14.74 ƺŖǯŖŘȘ ƺŖǯŖ؆ RƺŘ t ƺŗǯŝş ƺŘǯŗş 0.07‡ 0.01 Rƺř t 6.71 0.48 0.03‡ RƺŚ t 2.83 0.03‡ Rƺś t 2.94 0.0001 RƺŜ t 0.01 0.01 Rƺŝ t 1.16 0.02† RƺŞ t 1.96 0.002 Rƺş t 0.16 0.02† RƺŗŖ t 2.29 0.01 Rƺŗŗ t 1.12 0.03‡ RƺŗŘ t 3.43 Rƺŗř t RƺŗŚ t Rƺŗś t ƺŖǯŘś‡ ƺŖǯŖř Mtht2 t ƺŚǯŚŜ ƺŖǯŜŖ ƺŖǯŘŚ ƺŖǯřŚ† DSTtht2 t ƺŗǯŘś ƺŗǯşŞ. S&P500 Equal. Value. 0.04‡ 3.38 ƺŖǯŗŗ‡ ƺŚǯŘŚ 0.19† 1.78 0.19‡ 18.15 ƺŖǯŖŗ ƺŖǯşŝ 0.03† 2.49 0.02† 2.36. 0.03‡ 2.52 ƺŖǯŖŝ‡ ƺŘǯśŗ 0.19 1.46 0.10‡ 10.28 ƺŖǯŖ؆ ƺŗǯşŚ. NASDAQ Equal. . Value. Equal. Value. 0.07‡ 0.08‡ 9.76 7.05 ƺŖǯŘŚ‡ ƺŖǯŘ؇ ƺŗŞǯŗŝ ƺŗŖǯŘş 0.03 0.07 0.51 0.69 0.38‡ 0.23 34.40 21.43 ƺŖǯŖŗ ƺŖǯŖ؆ ƺŗǯŗś ƺŘǯŘŘ 0.08‡ 0.04‡ 7.22 3.86 0.04‡ 0.02† 3.69 2.05 0.05‡ 0.02† 4.82 1.97 0.04‡ 0.01 3.341 0.416 ƺŖǯŖŗ ƺŖǯŖŗ ƺŗǯŘŖ ƺŖǯŚś 0.02 0.01 1.43 0.59 0.01 0.01 1.17 0.70 0.05‡ 0.03‡ 4.76 2.59 0.03‡ 0.01 2.47 1.13 0.02† 0.04‡ 2.18 3.94 ƺŖǯŖŗ 0.02† ƺŖǯŝŝ 2.14 0.02‡ 0.02 2.45 1.61 0.04‡ ŖǯŖŘȘ 3.90 1.84 0.02 0.02 ƺŖǯŖŞȘ 0.02 0.43 0.50 ƺŗǯŞŗ 0.90 ƺŖǯřŚ† ƺŖǯřś† ƺŖǯŘŝ† ƺŖǯŗŝȘ ƺŘǯřŞ ƺŘǯŗś ƺŗǯşŞ ƺŗǯşŖ ǻȱȱ¡ȱǼ. 0.04‡ 4.53 ƺŖǯŗŜ‡ ƺŞǯŝŜ 0.003 0.04 0.35‡ 34.69 0.01 0.67 0.08‡ 7.98 0.04‡ 3.95 0.04‡ 4.40 0.01 1.205 0.01 0.58 0.01 1.24 0.001 0.09 0.03‡ 3.45 0.01 0.79 0.004 0.43 0.01 0.59 0.004 0.50 0.04‡ 4.84 ƺŖǯŘŞ‡ ƺŚǯşś ƺŖǯŗŚ ƺŖǯŝŞ. 0.04‡ 3.17 ƺŖǯŖş‡ ƺŚǯřŜ 0.11 1.08 0.27 26.31 ƺŖǯŖ؆ ƺŘǯřř 0.06‡ 5.39 0.02† 1.91 0.03‡ 3.05 ƺŖǯŖŖŗ ƺŖǯŖşŖ 0.01 0.50 0.01 1.02 0.0002 0.03 0.02† 2.28 ƺŖǯŖŖŚ ƺŖǯŚŗ ŖǯŖŘȘ 1.78 0.01 1.20 0.02† 2.07 0.01 1.33 ƺŖǯŗş‡ ƺŚǯřŝ ƺŖǯŘŞȘ ƺŗǯŜŝ. Note.—DZȱ ȱȱȱ¡DzȱǭśŖŖDZȱȱǭȱȂȱśŖŖDzȱDZȱȱȱȱȱȱȱDzȱDZȱȱȱ¡ǯȱ ȘȱȱęȱȱŗŖƖǯȱ†ȱȱęȱȱśƖǯȱ‡ȱȱęȱȱŗƖǯ.
(25) 874. H. BERUMENT & N. DOGAN TABLE 3 (ѐќћѡȂё) юѦљієѕѡȱюѣіћєȱіњђȱѓѓђѐѡȱќћȱђѡѢџћȬѣќљюѡіљіѡѦȱђљюѡіќћѠѕіѝѠȱ ѣђџȱѡѕђȱ
(26) юћѢюџѦȱŖřǰȱŗşŜŝǰȱѡќȱ
(27) ѢћђȱŘşǰȱŘŖŖŝǰȱђџіќё NYSE Equal. Value. S&P500 Equal. Value. NASDAQ Equal. Value. Equal. 0.13‡ 0.04† 0.04† 0.03 0.11‡ ŖǯŖŘȘ 0.16‡ t 5.21 1.99 2.11 1.63 5.65 1.77 6.60 ƺŖǯřŜȘ ƺŖǯřřȘ ƺŖǯŘŞ† ƺŖǯřŖ ƺŖǯŘŚȘ ƺŖǯŗř ƺŖǯŘŝ ȜȶƸȶȜMȶƸȶȜDST t ƺŗǯşŘ ƺŗǯşś ƺŘǯŖř ƺŖǯśř ƺŗǯŞŚ ƺŗǯŚŜ ƺŗǯřř ȱDZȱȱę Constant ƺŖǯŖř‡ ƺŖǯŖŗ‡ ƺŖǯŖŗ‡ ƺŖǯŖŖŘ ƺŖǯŖř‡ ƺŖǯŖŖŖŖŖř ƺŖǯŖś‡ t ƺŜǯřŚ ƺřǯŗŘ ƺŘǯŘś ƺŗǯŘş ƺŚǯśŝ ƺŖǯŖŘ ƺŝǯşŗ 0.75‡ 0.99‡ 0.73‡ 0.99‡ 0.70‡ 0.99‡ 0.95‡ log h2tƺŗ t 9.45 472.30 6.42 565.70 10.03 660.60 209.80 0.21‡ 0.25‡ 0.29‡ log h2tƺŘ t 2.74 2.22 3.88 ε t −1 ε t −1 ε t −1 χ E 0.12‡ 0.15‡ 0.11‡ 0.29‡ 0.16‡ 0.25‡ h − h + h 0.23‡ t −1 t −1 t −1 t 12.78 13.45 9.49 13.35 14.28 15.52 18.97 Ȥ ƺŖǯśŖ‡ ƺŖǯśŞ‡ ƺŖǯśŚ‡ ƺŖǯśŝ‡ ƺŖǯřއ ƺŖǯŚř‡ ƺŖǯřŝ‡ t ƺŗŖǯŝŞ ƺşǯŖŚ ƺşǯřş ƺŞǯŝŜ ƺŞǯŚŘ ƺŝǯşŚ ƺşǯşś Function ƺřǰŖśŝǯŗŖ ƺśǰśşřǯŚ ƺśǰşşŗǯś ƺŜǰŚŚŘǯŘř ƺŗǰŚřşǯŗŖ ƺśǰşŖŚǯŝř ƺŘǰŘŘśǯŜŖ Value Panel C: Robustness Statistics Lags Ȭ¡ȱȬǰȱp 5 0.10 0.62 0.55 0.80 ŖǯŖşȘ 0.84 0.01‡ 10 0.40 0.77 0.75 0.88 0.11 0.96 ŖǯŗŖȘ 20 0.17 0.79 0.55 0.84 ŖǯŖŞȘ 0.93 0.01 ‡ 60 0.04† 0.48 0.20 0.57 0.003‡ 0.19 0.00 ‡ Lags Ȭ¡ȱȬȱȱȱȱ£ȱǰȱp 5 0.68 0.91 0.81 0.81 0.20 0.83 0.94 10 0.56 0.96 0.94 0.94 0.21 0.69 0.86 20 0.80 1.00 1.00 0.99 0.26 0.67 0.93 60 0.34 0.79 0.04† 0.76 ŖǯŗŖȘ 0.15 0.90 Lags ARCH-LM Tests, p 5 0.22 0.17 0.14 0.11 ŖǯŖŝȘ 0.00 0.03† 10 0.23 0.43 0.40 0.33 0.10 0.00 ŖǯŖşȘ 20 0.54 0.85 0.85 0.68 0.13 0.00 0.33 60 0.22 ŖǯŖŜȘ 0.02† 0.04† 0.04† 0.00 0.47 ȱDZȱȬȱǰȱp Sign bias 0.96 0.82 0.61 0.95 0.97 0.19 0.42 ȱ£ 0.82 0.52 0.98 0.59 0.88 0.75 0.61 ȱ£ ŖǯŖŝȘ 0.01† 0.05† 0.01‡ ŖǯŖşȘ ŖǯŖşȘ 0.36
(28) ȱ 0.17 0.001‡ 0.02† 0.002‡ 0.19 0.00‡ 0.31 ht2. Value ŖǯŖŞȘ 3.92 ƺŖǯřş† ƺŘǯřŜ ƺŖǯŖŗ‡ ƺŚǯŗŞ 0.73‡ 8.22 0.24‡ 2.80 0.22‡ 11.85 ƺŖǯŚř‡ ƺŞǯşŝ ƺŚǰŞŘŘǯřŖ. 0.77 0.98 0.98 0.44 0.13 0.16 0.26 0.68 0.03† 0.06† 0.15 0.55 0.57 0.92 0.22 0.12. Note.—DZȱ ȱ ȱ ȱ ¡Dzȱ ǭśŖŖDZȱ ȱ ǭȱ Ȃȱ śŖŖDzȱ DZȱ ȱȱȱȱȱȱDzȱDZȱȱȱ¡ǯȱȘȱȱęȱȱŗŖƖǯȱ†ȱȱęȱȱśƖǯȱ‡ȱȱęȱȱ ŗƖǯ.
(29) DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY. 875. £ȱ ȱ ȱ Dzȱ ȱ ȱ Ȭ ȱ ¡ȱ ȱ ȱ ȱ ȱ ȱ ȱ¢ȱȱǯȱR2s are not useful in the EGARCH specęȱȱȱĞȬȬȱȱȱ ȱ ȱȱ ȱȱǻȱ¢ȱȱȱ¢ȱȱȱ¡ǰȱȱ ȱ ǰȱ ŘŖŖşǰȱ ǯȱ ŘŝŞȱ ȱ ęǼǯȱȱ ȱ ȱ ȱ Ěȱ ȱȱȱȱȂȱȱȱǻŗşşśǼȱȱǰȱR2s are not reported here. ќћѐљѢѠіќћ ǰȱet al.ȱǻŘŖŖŖǼȱȱȱ¢ȱȱȱȱȱ ȱ ȱ ȱǯȱȱȱȱȱȱȱǻŘŖŖŘǼȱȱȱęȱȱȱěǰȱȱȱ¢ȱ¡ȱ ȱȱ ȱȱȬ¢ȱǯȱȱȱȱ ȱȱȬ¢ȱȱȱȱęȱȱȱěȱȱȱȱȱȱ ȱ ȱ ǯȱ ǰȱ ȱ ȱ ěȱ ȱ ȱ ȱ Ğȱȱȱȱ ȱȱȱȱǻŘŖŖŘǼȱǰȱȱ ȱ ȱȱ ȱȱǯȱȱȱ¢ȱȱȱ ȱȱȬȱȱȬ ȱǰȱǭśŖŖǰȱǰȱȱ ȱ¡ȱȱȱȱȱȱȱȱȱ ȱȱȱ ȱȱȱȱ ȱȱȱȱ¢ȱȱȱǯȱȱȱȱ¡ȱ¢ȱǰȱ ȱȱȱȱȱȱȱǯȱȱěȱȱ¢ȱęȱȱȱ ŗŖƖȱȱȱȱȱ¡ȱȱǰȱ¡ȱȱȬ ȱ ¡ȱȱȱȱǯȱ REFERENCES. єџюѤюљǰȱǯǰȱ&ȱюћёќћǰȱ ǯȱǻŗşşŚǼȳȱȱǵȱȱȱȱȱȱȱǯȱJournal of International Money and Finance, 13, 83-106. юіљљіђǰȱǯȱǯǰȱ&ȱђ ђћћюџќǰȱǯȱǯȱǻŗşşŖǼȳȱȱȱ¢ǯȱJournal of Financial and Quantitative Analysis, 25, 203-214. юљіǰȱǯȱ ǯǰȱ&ȱђћєǰȱǯȱǻŘŖŖŜǼȳ ȱȱȱȬȱěǵȱȱȱȬquency data. Journal of Applied Econometrics, 21, 1169-1198. ђџѢњђћѡǰȱ ǯǰȱќѠјѢћǰȱǯȱǯǰȱ&ȱюѕіћǰȱǯȱǻŘŖŖŝǼȳ¢ȱȱȱ ȱěȱȱȱ ¡ȱȱ¢DZȱȱȱ¢ǯȱResearch in International Business and Finance, 21, 87-97. ђџѢњђћѡǰȱ ǯǰȱќєюћǰȱǯǰȱ&ȱћюџǰȱǯȱǻŘŖŖŞǼȳȱěȱȱ¢ȱȱȱȱ on stock market volatilityǯȱ ȱ ȱ ȱ ȱ ȱ DZȱ ĴDZ ȦȦǯȦȶƽȶŗŗřŝŖŞŘǯ ђџѢњђћѡǰȱ ǯǰȱ&ȱ іѦњюѧǰȱ ǯȱǻŘŖŖŗǼȳȱ¢ȱȱȱ ȱěȱȱȱȱity. Journal of Economics and Finance, 25, 181-193. љѢњђǰȱǯǰȱ&ȱѡюњяюѢєѕǰȱǯȱǯȱǻŗşŞřǼȳȱȱȱDZȱȱȱȱ ȱ£ȱěǯȱJournal of Financial Economics, 12, 357-369. ќіёќǰȱǯǰȱ&ȱюѠюћќǰȱǯȱǻŘŖŖśǼȳȱDZȱ¢ȱȱěǯȱȱ presented at the 2005 FMA European Conference, Siena, Italy..
(30) 876. H. BERUMENT & N. DOGAN. ќљљђџѠљђѣǰȱ ǯǰȱ &ȱ ќќљёџіёєђǰȱ
(31) ǯȱ ǯȱ ǻŗşşŘǼȳȬ¡ȱ ȱ ȱ ȱȱȱ¢ȱȱ ȱȬ¢ȱǯȱEconometric Reviews, 11, 143-172. џђћћюћǰȱǯȱ
(32) ǯǰȱ&ȱѐѕѤюџѡѧǰȱǯȱǯȱǻŗşŞśǼȳȱȱȱȱ¡DZȱȱǯȱJournal of Financial and Quantitative Analysis, 20, 119-122. џќѢєѕѡќћǰȱ
(33) ǯǰȱ&ȱђёњюћǰȱǯȱ
(34) ǯȱǻŗşŞşǼȳȱȱěȱȱȱȱȱȱȱȱǯȱȱȱŘŘŞǰȱ ȱȱȱȱȱ Laboratory. џќѤћǰȱ ǯȱǯǰȱіѐјћђџǰȱǯȱ ǯǰȱ&ȱіњњќћёѠǰȱǯȱǯȱǯȱǻŗşŝŖǼȳȱěȱȱȱȱȱȱǯȱErgonomics, 13, 239-242. юћіћюǰȱ ǯǰȱ іѐѕюђљѦǰȱ ǯǰȱ ѕюљђџǰȱ ǯǰȱ &ȱ ќњюѐјǰȱ ǯȱ ǻŗşşŞǼȳȱ DZȱ ȱ ȱȱȱȱ¢ȱȱȬȱ ¡ȱȱȱȬȱ ¡ȱǯȱJournal of Finance, 53, 403-416. юџћђџќǰȱǯǰȱђћюǰȱǯǰȱ&ȱѢіѧǰȱǯȱǻŘŖŖŝǼȳěȱȱȱȱȱęȱȱ ȱȱ ȱǯȱJournal of Time Series Analysis, 28, 471-497. ѕюћєǰȱǯǰȱіћђєюџǰȱǯȱ
(35) ǯǰȱ&ȱюѣіѐѕюћёџюћǰȱǯȱǻŗşşřǼȳ ȱȱȱȱ ȱȱȱ¢ȬȬȬ ȱěǯȱJournal of Financial and Quantitative Analysis, 28, 497-513. ѕђѢћєǰȱǯǰȱ&ȱєǰȱǯȱ ǯȱǻŗşşŘǼȳȱȱ¢ȱȱęȱ£DZȱȱȱtigation. Journal of Finance, 47, 1985-1997. ќћћќљљѦǰȱǯȱǯȱǻŗşŞşǼȳȱ¡ȱȱȱȱȱȱ ȱěǯȱJournal of Financial and Quantitative Analysis, 24, 133-169. ќџђћǰȱǯȱǻŗşşŜǼȳȱȱȱȱĞȱȱ¢ȱȱǯȱPerceptual and Motor Skills, 83, 921-922. ќџђћǰȱǯȱǻŗşşŜǼȳ¢ȱȱȱȱĜȱǯȱThe New England Journal of Medicine, 334, 924-925. ќџђћǰȱǯȱǻŗşşŜǼȳSleep thieves.ȱ ȱDZȱȱȱǯȱ џќѠѠǰȱǯȱǻŗşŝřǼȳȱȱȱȱȱȱ¢ȱȱ¢ǯȱFinancial Analysts Journal, 29(6), 67-69. џѢњњќћёǰȱǯȱǯȱǯǰȱ іљљіћǰȱ
(36) ǯȱǯǰȱ&ȱџќѤћǰȱ ǯȱ ǯȱǻŘŖŖŗǼȳ ȱȱȱ ȱ ȱ ȱ Ĵȱ ȱ ȱ ȱ ǯȱJournal of Sleep Research, 10(2), 85-92. ѢяќіѠǰȱ ǯǰȱ &ȱ ќѢѣђѡǰȱ ǯȱ ǻŗşşŜǼȳȱ ¢ȬȬȬ ȱ ěDZȱ ȱ ǯȱ Journal of Banking and Finance, 20, 1463-1484. ћєљђǰȱǯȱǻŗşşśǼȳARCH selected readingsǯȱ¡ǰȱ DZȱ¡ȱǯȱǯȱ юњюǰȱǯȱǯǰȱ&ȱюѐђѡѕǰȱ
(37) ǯȱǯȱǻŗşŝřǼȳǰȱȱȱǯȱJournal of Political Economy, 1, 607-636. ђџєѢѠќћǰȱǯȱǯǰȱџђѢѠѠђџǰȱǯȱǯǰȱѢћёǰȱǯȱ ǯǰȱ юёќџǰȱǯȱǯǰȱ&ȱљњђџǰȱǯȱ ǯȱǻŗşşśǼȳ¢ȱȱȱȱȱȱDZȱȱȱȱȱȱhicle occupant fatalities. American Journal of Public Health, 85, 92-95. џюћѠђѠǰȱǯȱ ǯǰȱ&ȱ ѕӒѠђљѠǰȱ ǯȱǻŗşşşǼȳȱǰȱ ǰȱȱȱtility. International Journal of Forecasting, 15, 1-9. џђћѐѕǰȱ ǯȱǯȱǻŗşŞŖǼȳȱȱȱȱ ȱěǯȱJournal of Financial Economics, 8, 55-69. џђћѐѕǰȱ ǯȱǯǰȱ&ȱќљљǰȱǯȱǻŗşŞŜǼȳȱȱDZȱȱȱȱȱȱ the reaction of traders. Journal of Financial Economics, 17(1), 5-26..
(38) DAYLIGHT SAVING TIME AND STOCK MARKET VOLATILITY. 877. џђћѐѕǰȱ ǯȱǯǰȱѐѕѤђџѡǰȱ ǯȱǯǰȱ&ȱѡюњяюѢєѕǰȱǯȱǯȱǻŗşŞŝǼȳ¡ȱȱȱȱ volatility. Journal of Financial Economics, 19, 3-29. ѕѦѠђљѠǰȱǯǰȱљюџюǰȱǯȱǯǰȱ&ȱюљјюћќѣǰȱǯȱǻŘŖŖśǼȳȱȱȱȬȱěȱĞȱǯȱ Journal of Financial Economics, 76, 509-548. іяяќћѠǰȱǯȱǯǰȱ&ȱ ђѠѠǰȱǯȱǻŗşŞŗǼȳ¢ȬȬȬ ȱěȱȱȱǯȱThe Journal of Business, 54, 579-596. џђєќџѦȬљљђћǰȱǯǰȱ
(39) юѐќяѠђћǰȱǯǰȱ&ȱюџўѢђџіћєǰȱǯȱǻŘŖŗŖǼȳȱ¢ȱȱȱ ¢ȱȱȱDZȱȱȱęǵȱJournal of Financial Research, 33, 403-427.. юџѣђѦǰȱǯȱǯǰȱ&ȱѕђѝѕюџёǰȱǯȱǻŗşşŜǼȳȱȱȱ¢ȱȱ¢ȱȱȱȱǯȱJournal of Business & Economic Statistics, 14, 429-434.. іѐјѠǰȱ ǯȱ ǯǰȱ іћёѠђѡѕǰȱ ǯǰȱ &ȱ юѤјіћѠǰȱ
(40) ǯȱ ǻŗşŞřǼȳ¢ȱ Ȭȱ ȱ ȱĜȱǯȱPerceptual and Motor Skills, 56, 64-66.
(41) юѐќяǰȱ ǯȱ ǻŗşŝŗǼȳȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ DZȱ ȱȱǯȱJournal of Financial and Quantitative Analysis, 6, 815-833.
(42) юѓѓђǰȱ
(43) ǯǰȱђѠѡђџѓіђљёǰȱǯǰȱ&ȱюǰȱǯȱǻŗşŞşǼȳȱ ȱȱȱ¢ȱěȱȱȱDZȱ ȱȱȱǯǯȱȱȱȱǯȱJournal of Banking and Finance, 13, 641-650.
(44) юћѠђћǰȱ ǯȱ ǯǰȱ &ȱ ќѠіњќћюǰȱ ǯȱ ǯȱ ǻŗşŞŞǼȳȱ ȱ ȱ ȱ ȱ ǯǯȱ Ěȱ ȱȱȱ ȱDZȱȱǯȱJournal of Money, Credit and Banking, 20, 409-421.
(45) ђћѠђћǰȱǯȱǯǰȱљюѐјǰȱǯǰȱ&ȱѐѕќљђѠǰȱǯȱǻŗşŝŘǼȳȱȱȱȱDZȱȱ ȱǯȱ ȱǯȱ
(46) ȱǻǯǼǰȱStudies in the theory of capital marketsǯȱ ȱDZȱ Praeger. Pp. 79-121. юњѠѡџюǰȱǯȱ
(47) ǯǰȱ џюњђџǰȱǯȱǯǰȱ&ȱђѣіǰȱǯȱǯȱǻŘŖŖŖǼȳȱȱȱȱDZȱȱ ¢Ȭȱ¢ǯȱAmerican Economic Review, 90, 1005-1011. юњѠѡџюǰȱǯȱ
(48) ǯǰȱ џюњђџǰȱǯȱǯǰȱ&ȱђѣіǰȱǯȱǯȱǻŘŖŖŘǼȳȱȱȱȱDZȱȱ ¢Ȭȱ¢DZȱ¢ǯȱAmerican Economic Review, 92, 1257-1263. іљљєќџђǰȱ ǯȱ ǯȱ ǻŘŖŖŝǼȳěȱ ȱ ȱ ȱ ȱ Ȭȱ ȱȱȬǯȱPsychological Reports, 100, 613-626. іљљєќџђǰȱǯȱǯȱǯǰȱюљјіћǰȱǯȱ
(49) ǯǰȱ&ȱђѠђћѠѡђћǰȱǯȱ
(50) ǯȱǻŘŖŖŜǼȳ ȱȬȱ ȱŚşȱȱȱȱǯȱJournal of Sleep Research, 15, 7-13. іњǰȱǯǰȱ&ȱ ќћǰȱǯȱ
(51) ǯȱǻŗşşŚǼȳȱȱȱȱȱ¢ȱ ȱȱǯȱThe Journal of Business, 67, 563-598. юњяǰȱǯȱǯǰȱ Ѣяђџǰȱǯȱǯǰȱ&ȱ юћёюџǰȱ
(52) ǯȱǯȱǻŘŖŖŚǼȳȂȱȱȱȱDZȱȬ¡ȱ ȱȱ¢ȱȱ¢ǯȱApplied Financial Economics, 14, 443-446. іћѡћђџǰȱ
(53) ǯȱǻŗşŜśǼȳ¢ȱǰȱǰȱȱ¡ȱȱȱęǯȱJournal of Finance, 20, 587-615. юџјќѤіѡѧǰȱ ǯȱǻŗşśŘǼȳȱǯȱThe Journal of Finance, 12, 77-91. ђџѡќћǰȱǯȱǯȱǻŗşŝřǼȳȱȱȱȱȱǯȱEconometrica, 41, 867-887. ђџѡќћǰȱǯȱǯȱǻŗşŞŖǼȳȱȱȱ¡ȱȱȱȱDZȱȱ¡¢ȱ investigation. Journal of Financial Economics, 8, 323-361. ђѦђџѕќѓѓǰȱǯȱ
(54) ǯȱǻŗşŝŞǼȳȱĚȱȱ¢ȱȱȱȱȱȱ ȱĜȱǯȱAccident Analysis and Prevention, 10, 207-221. ќћјǰȱǯȱ ǯȱǻŗşŞŖǼȳĜȱȱȱȱȱȱȱȱ¢ǯȱ Chronobiologia, 7, 527-529..
(55) 878. H. BERUMENT & N. DOGAN. ќќџѐџќѓѡǰȱǯȱ ǯǰȱ&ȱђљѐѕђџǰȱǯȱǻŘŖŖřǼȳUnderstanding sleep and dreamingǯȱ ȱDZȱ ǯ ќѠѠіћǰȱ
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Yaprakların İVKMSD %28.56 ile % 67.73 arasında değişmiş olup en yüksek İVKMSD’i Ocak ve Nisan aylarında en düşük İVKMSD’ne ise Ekim ayında hasat edilen
As explained in the previous chapter, home and parent related factors (educational level of father, educational level of mother, home educational resources), school types
Bu proje çalışmasında, Emotiv EEG Neuroheadset cihazı kullanılarak kararlı durum görsel uyaranlar kullanılarak elde edilen EEG işaretlerinin doğru bir şekilde
