臺灣博碩士論文加值系統

本文以美國 S&P500 指數的月報酬率實證分析適應性市場假說 (the Adaptive Market Hypothesis, AMH) 之下，效率與非效率兩種市場氛圍的交替轉換。我們以馬可夫轉換模型的 filtering probability 評估兩種市場的動態關係，並由避險與投資兩個面向切入，實證適應性市場假說。本研究針對馬卡夫轉換模型所辨認的兩個市場，採用不同的避險比率，其在兩個市場的避險效率表現都較傳統 (naïve) 和最小平方法 (OLS) 避險為佳。由適應性市場擇時之實證，我們亦發現若針對轉換模型指出之非效率期間採行不同的策略，所得之累積報酬率都較買入持有策略 (buy and hold) 為佳，且在進一步以馬可夫轉化模型評估實際波動度 (realized volatility) 來囊括波動度擇時後，其累積報酬率更隨
之增加，擇時策略的報酬率在考量 Fama-French-Carhart 的四因子與交易成本後，仍然有顯著的超額報酬，再次呼應適應性市場假說之存在。

In this studies, we have delved into the monthly logarithmic returns of S&P500 index to characterize the transition between efficient and inefficient market under the Adaptive Market Hypothesis (AMH). By utilizing the Markov switching regression, we identify the efficient and
inefficient market regimes. Through implementing different strategy in each regime, we find evidence for the AMH in the aspect of hedging and investment. For hedging, we apply distinct hedge ratio in two market regimes, and the AMH hedge outperforms the naïve and the OLS hedge. And for investment, besides estimating switching model of conditional returns, we also construct the switching model of conditional Realized Volatility (RV), which is inspired by the volatility timing. By applying different investment strategy during inefficient periods detected by the Markov switching model, the AMH timing portfolio outperforms the B&H portfolio with the regime regression of conditional returns, and the cumulative returns even increase after including the volatility timing by implementing different strategy during the sentimental periods identified with the switching model of conditional RV. Besides appealing cumulative returns, the AMH timing portfolio also has significant cross-sectional returns after explained by a four-factor model and considering transaction costs.

1. Introduction___1
1.1 The Adaptive Market Hypothesis___1
1.2 Characterize the AMH with the Markov Switching Model___3
1.3 The Hedge Performance with the AMH Hedge Ratio___6
1.4 The AMH Timing___7
2. Methodology___9
2.1 The Problems in Futures Hedging with Time Series Errors___9
2.2 The Markov Switching Regression in Mean Returns___9
2.3 The Markov Switching Regression in Realized Volatility___12
2.4 Hedging Performance in Minimum Variance Hedge Ratio (MVHR)___13
2.5 The AMH Timing___14
3. Data___15
3.1 Measurement of the Rational and Sentimental Market___15
3.2 Summary Statistics___16
4. Empirical Results___20
4.1 Estimate Two-regime Markov Switching Models with Sentiment Index___21
4.2 Estimate Two-regime Markov Switching Models with Sentiment Index and E/P ratios___25
4.3 Estimate Two-regime Markov Switching Models with Sentiment Index and D/P ratios___29
4.4 Expected Duration of Rational and Sentimental Market by Decades___32
4.5 Hedge Ratio and Hedge Effectiveness___33
4.6 The AMH Timing___36
4.7 The Fama-French-Carhart Four-Factor model___42
5. Robustness___43
5.1 Transaction Costs in the AMH Timing___44
6. Conclusions___44
7. References___45

[1] Akaike, H., 1974, “A New Look at Statistical Model Identification”, IEEE Trans Aut Control, 19, 716–23.
[2] Andersen, T.G., Bollerslev, T., Diebold, F.X., & Ebens, H., 2001, “The distribution of realized stock return volatility”, Journal of Financial Economics 61, 43–76.
[3] Andersen, T.G., Bollerslev, T., Diebold, F.X., & Labys, P., 2001a, “The distribution of realized
exchange rate volatility”, Journal of the American Statistical Association, 96, 42–55.
[4] Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P., 2003, “Modeling and forecasting realized volatility”, Econometrica, 71(2), 579-625.
[5] Areal, N., Taylor, S.J., 2002, “The realized volatility of FTSE-100 futures prices”, Journal of
Futures Markets , 22(7), 627-648.
[6] Baker, Malcolm, and J. Wurgler, 2006, “Investor Sentiment and the Cross-Section of Stock Returns”, Journal of Finance, 61, 4, 1645–1680
[7] Baker, Malcolm, and J. Wurgler, 2007, “Investor sentiment in the stock market”, Journal of Economic Perspectives, 21, 129–151.
[8] Black, F., 1986, “Noise”, The Journal of Finance, 41(3), 528-543.
[9] Bollerslev, T., 1986, “Generalized autoregressive conditional heteroscedasticity”, Journal of Econometrics, 31, 3, 307-327.
[10] Brown, G.W. & M.T. Cliff, 2005, “Investor sentiment and asset valuation”, Journal of Business, 78 (2005), 405–440
[11] Busse, Jeffrey A., 1999, “Volatility timing in mutual funds: Evidence from daily returns”, Review of Financial Studies, 12, 1009–1041.
[12] Cai, J., 1994, “A Markov model of switching-regime ARCH”, Journal of Business & Economic Statistics, 12(3), 309-316.
[13] Cecchetti, S. G., Cumby, R. E., & Figlewski, S., 1988, “Estimation of the optimal futures hedge”, The Review of Economics and Statistics, 70, 4, 623-630.
[14] Clark, P.K., 1973, “A subordinated stochastic process model with finite variance for speculative prices”, Econometrica, 41, 135–155.
[15] Cochrane, J. H., 2009, Asset Pricing:(Revised Edition). Princeton university press.
[16] De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J., 1990, “Noise trader risk in financial markets”, Journal of political Economy, 703-738.
[17] Ederington, L. H., 1979, “The hedging performance of the new futures markets”, Journal of Finance, 34, 157–170
[18] Engle, C., & Hamilton, J. D., 1990, “Long swings in the dollar: Are they in the data and do markets know it?” The American Economic Review, 689-713.
[19] Fama, E., 1970, “Efficient capital markets: A review of theory and empirical work”, The Journal of Finance, 25(2), 383–417.
[20] Fama, E., 1972, “Components of Investment Performance”, Journal of Finance, 27, 2, 551–567.
[21] Fama, E. F., & French, K. R., 1988, “Dividend yields and expected stock returns”, Journal of financial economics, 22(1), 3-25.
[22] Fama, E. F., & French, K. R., 1988, “Permanent and temporary components of stock prices”, The Journal of Political Economy, 246-273.
[23] Fleming, J., Kirby, C., & Ostdiek, B., 2001, “The economic value of volatility timing”, Journal of Finance, 56(1), 329-352.
[24] Fleming, J., Kirby, C., & Ostdiek, B., 2003, “The economic value of volatility timing using “realized” volatility”, Journal of Financial Economics, 67(3), 473-509.
[25] Gray, S. F., 1996, “Modeling the conditional distribution of interest rates as a regime-switching process”, Journal of Financial Economics, 42(1), 27-62.
[26] Hamilton, J. D., 1988, “Rational-expectations econometric analysis of changes in regime: An investigation of the term structure of interest rates”, Journal of Economic Dynamics and Control, 12(2-3), 385-423.
[27] Hamilton, J. D., 1989, “A New Approach to the Economic Analysis of Non-stationary Time Series”, Econometrica, 57, 357– 84.
[28] Hamilton, J. D., & Susmel, R., 1994, “Autoregressive conditional heteroskedasticity and changes in regime”, Journal of Econometrics, 64(1), 307-333.
[29] Henriksson R. and R. Merton, 1981, “On Market Timing and Investment Performance”, Journal of Business, 57, 4, 513-534.
[30] Hull, M., & McGroarty, F., 2014, “Do emerging markets become more efficient as they develop? Long memory persistence in equity indices”, Emerging Markets Review, 18, 45–61.
[31] Ito, M., & Sugiyama, S., 2009, “Measuring the degree of time varying market inefficiency”, Economics Letters, 103(1), 62–64.
[32] Jensen M., 1968 “The Performance of Mutual Funds in the Period 1945 – 1964”, Journal of Finance, 23, 2, 389 – 461.
[33] Jensen M., 1969 “Risk, the Pricing of Capital Assets and the Evaluation of Investment Portfolios”, Journal of Business, 42, 2, 167 – 247.
[34] Johnson L.L., 1960, “The theory of hedging and speculation in commodity futures”, Review of Economic Studies, 27, 139–151
[35] Kim, J. H., Shamsuddin, A., & Lim, K., 2011, “Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data”, Journal of Empirical Finance, 18, 868–879.
[36] Kumar, A. & C.M.C. Lee, 2006, “Retail investor sentiment and return comovements”, Journal of Finance, 61 (2006), 2451–2486
[37] Lam, P. S., 1990, “The Hamilton model with a general autoregressive component: estimation and comparison with other models of economic time series: Estimation and comparison with other models of economic time series”, Journal of Monetary Economics, 26(3), 409-432.
[38] Lien, D. and X. Luo, 1993, “Estimating Multi-Period Hedge Ratios in Cointegrated Markets”, Journal of Futures Markets, 13, 909-920.
[39] Lim, K. -P., 2007, “Ranking market efficiency for stock markets: A nonlinear perspective”, Physica A, 376, 445–454.
[40] Lim, K. -P., Luo, W., & Kim, J. H., 2013, “Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests”, Applied Economics, 45(8), 953–962.
[41] Lintner, John, 1965, “The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets”, Review of Economics and Statistics, 47:1, 13–37.
[42] Lo, A. W., 2004, “The adaptive markets hypothesis”, Journal of Portfolio Management, 30, 15–29.
[43] Lo, A. W., 2005, “Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis”, Journal of Investment Consulting, 7(2), 21-44.
[44] Manahov, V., & Hudson, R., 2014, “A note on the relationship between market efficiency and adaptability — New evidence from artificial stock markets” Expert Systems with Applications, 41(16), 7436–7454.
[45] Markowitz, H., (1952), “Portfolio Selection”, The Journal of Finance, 7:1, 77–91.
[46] Myers, R.J. and S.R. Thompson, 1989, “Generalised Optimal Hedge Ratio Estimation”, American Journal of Agricultural Economics, 71, 858-868.
[47] Quandt, R.E., 1972, “A New Approach to Estimating Switching Regressions”, Journal of the American Statistical Association, 67, 306-310.
[48] Stein, J.L., 1961, “The Simultaneous Determination of Spot and Futures Prices”, The American
Economic Review, 51:5, 1012–1025.
[49] Treynor J.L. and J. Mazuy, 1996, “Can Mutual Funds Outguess the Market?” Harvard Business Review, 44, 4, 131 – 136.
[50] Tsay, R. S., 2010, “Analysis of Financial Time Series”, 3rd ed., Wiley, Hoboken, NJ.
[51] Urquhart, A., & Hudson, R., 2013, “Efficient or adaptive markets? Evidence from major stock markets using very long historic data”, International Review of Financial Analysis, 28, 130–142.
[52] Park, T.H. and L.N. Switzer, 1995, “Bivariate GARCH Estimation of the Optimal Hedge Ratios for Stock Index Futures: A Note”, Journal of Futures Markets, 15, 1, 61-67.
[53] Zhou, J., & Lee, J. M., 2013, “Adaptive market hypothesis: Evidence from the REIT market”, Applied Financial Economics, 23(21), 1649–1662.