臺灣博碩士論文加值系統

本研究探討不同頻率的石油公司波動性連結性是否會對股票和債券報酬產生影響。Křehlík and Baruník (2017)認為，短期及長期的石油公司波動連結程度分別隱含不同的資訊，因此本研究採用Křehlík and Baruník (2017)的方法，計算AMEX原油指數成分股衡量不同頻率的波動連結程度。並透過加入平方項的普通最小平方 (ordinary least-squares, OLS) 迴歸模型以及Hansen (2017) 提出的抝折迴歸模型 (regression kink model) 檢驗Brown et al. (1988) 的資訊不定假說 (uncertain information hypothesis)。根據Chen (2014) 的研究，具有石油敏感性的股價會和市場情況及總體經濟資訊有關且可預測未來的油價變動。本研究的實證結果發現，石油股票的波動連結程度會影響股債關係，且波動連結程度和資訊傳遞機制有關，短期的波動連結程度和好消息相關，長期的波動連結程度則和壞消息相關。因此本研究的實證結果不僅支持Brown et al. (1988) 提出的資訊不確定假說，而且支持壞消息時會有安全性轉移 (flight-to-quality) 現象，在好消息時則會出現風險性轉移 (flight-from-quality) 現象。

This paper examines whether time-frequency dynamics of volatility connectedness for oil companies has an influence on stock and bond returns. Motivated by Křehlík and Baruník (2017), this paper hypothesizes that short- and long-term volatility connectedness contain different information flows. To measure the dynamic volatility connectedness in the frequency domain, the econometric model suggested by Křehlík and Baruník (2017) is employed. To take the extent of the uncertain information hypothesis proposed by Brown et al. (1988) into considerations, the ordinary least-squares (OLS) regression model with quadratic term and regression kink model proposed by Hansen (2017) are used in the paper. According to Chen (2014), the oil-sensitive stock price is associated with market conditions and macroeconomic news and is informative in tracing future changes in crude oil prices. This paper using the stocks of the AMEX oil index for the analysis of volatility connectedness in the frequency domain. The empirical results show volatility connectedness of oil stocks has impact on the stock-bond relation and volatility connectedness is related to information transmission mechanism, short-term volatility connectedness is associated with good news whereas long-term volatility connectedness is correlated with bad news. The results also reveal that investors’ reactions are an overreaction to bad news and an underreaction to good news on stock and bond markets, respectively. Therefore, our findings are in support of the uncertain information hypothesis proposed by Brown et al. (1988) and flight-to-quality in bad news as well as flight-from-quality in good news phenomena.

Contents

1. Introduction 1
2. Literature review 5
2.1 Information uncertainty and price behavior 5
2.2 Information contents of volatility connectedness 7
2.3 Relationship between oil and stock-bond market 8
2.4 The dynamics of stock-bond return relation 9
2.5 Discussions 10
3. Data 11
4. Methodology 12
4.1. Connectedness measures in the frequency domain 12
4.2. Ordinary Least Squares regression model 16
4.3. Regression kink model with an unknown threshold 17
5. Empirical results 21
5.1. Relationship among connectedness measures and VIX 21
5.2. OLS results without and with quadratic term 23
5.2.1. Impact of connectedness measures on subsequent stock index futures returns 23
5.2.2. Impact of connectedness measures on subsequent bond index futures returns 25
5.3. Results of the regression kink model 27
5.3.1. Impact of connectedness measures on subsequent stock index futures returns 27
5.3.2. Impact of connectedness measures on subsequent bond index futures returns 30
5.4. Robustness check 32
6. Conclusion 35
References 38


Lis of Tables

Table 1 Summary statistics of annualized daily volatility…………………………47
Table 2 Correlation for dynamic frequency measures and VIX…………………48
Table 3 OLS results without and with quadratic term for stock index futures returns49
Table 4 OLS results without and with quadratic term for bond index futures returns51
Table 5 Testing for a threshold effect in stock index futures returns………………53
Table 6 Results of regression kink model for stock index futures returns…………55
Table 7 Testing for a threshold effect in bond index futures returns………………57
Table 8 Results of regression kink model for bond index futures returns…………59
Table 9 Testing for a threshold effect in differences between stock and bond index futures returns………………………………………………………………………61
Table 10 Results of regression kink model for differences between stock and bond index futures returns………………………………………63




Lis of Figures

Figure 1 Impact of spread on subsequent stock index futures returns as adding quadratic term in Spread_t………………………………………………………………………42
Figure 2 Impact of spread on subsequent bond index futures returns as adding quadratic term in Spread_t………………………………………43
Figure 3 Impact of spread of long- and short-term connectedness measures on subsequent stock index futures returns when 〖Spread〗_t is used as the threshold variable……….…………………………………………44
Figure 4 Impact of spread of long- and short-term connectedness measures on subsequent bond index futures returns when 〖Spread〗_t is used as the threshold variable……….…………………...……………………….…………………………45
Figure 5 Impact of spread of long- and short-term connectedness measures on subsequent differences between stock and bond index futures returns when 〖Spread〗_t is used as the threshold variable………………………………46

References
1.Adrian, T., Crump, R., and Vogt, E. (2015), “Nonlinearity and Flight to Safety in the Risk-Return Trade-Off for Stocks and Bonds”, FRB of NY Staff Report, No. 723.
2.Ahn, D., Boudoukh, J., Richardson, M., and Whitelaw, R.F. (2002), “Partial Adjustment or Stale Prices? Implications from Stock Index and Futures Return Autocorrelations”, Review of Financial Studies, Vol.15, No.2, pp.655–689.
3.Baele, L., Bekaert, G., and Inghelbrecht, K. (2010), “The determinants of stock and bond return comovements”, Review of Financial Studies, Vol.23, No.6, pp.2374-2428.
4.Bansal, N., Connolly, R.A., and Stivers, C. (2010), “Regime-switching in Stock Index and Treasury Futures Returns and Measures of Stock Market Stress”, Journal of Futures Markets, Vol.30, No.8 pp.753–779.
5.Bansal, N., Connolly, R.A., and Stivers, C. (2014), “The Stock-Bond Return Relation, the Term Structure’s Slope, and Asset-Class Risk Dynamics”, Journal of Financial and Quantitative Analysis, Vol.49, No.3, pp.699–724.
6.Baruník, J., and Křehlík, T. (2018), “Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk”, Journal of Financial Econometrics, Vol.16, No.2, pp.271-296.
7.Baur, D., and Lucey, B. (2009), “Flights and Contagion—An Empirical Analysis of Stock–Bond Correlations”, Journal of Financial Stability, Vol.5, No.4 pp.339-352.
8.Bekaert, G., Engstrom, E., and Xing, Y. (2009). “Risk, Uncertainty, and Asset prices”, Journal of Financial Economics, Vol.91, No.1, pp.59-82.
9.Branger, N., and Larsen, L. S. (2013), “Robust Portfolio Choice with Uncertainty about Jump and Diffusion Risk”, Journal of Banking and Finance, Vol.37, No.12, pp.5036-5047.
10.Brown, K., Harlow, W., and Tinic, M. (1988), “Risk Aversion, Uncertain Information, and Market Efficiency”, Journal of Financial Economics, Vol.22, No.2, pp.355-385.
11.Braun, P. A., Nelson, D. B., and Sunier, A. M. (1995), “Good News, Bad News, Volatility, and Betas”, The Journal of Finance, Vol.50, No.5, pp.1575–1603.
12.Campbell, J., Sunderam, A., and Viceira, L. (2017), “Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds”, Critical Finance Review, Vol.6, pp.263-301.
13.Chen, S.S. (2014), “Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks”, Economic Inquiry, Vol.52, No.2 pp.830-844.
14.Chen, C. D., Cheng, C. M., and Demirer, R. (2017), “Oil and Stock Market Momentum”, Energy Economics, Vol.68, pp.151-159.
15.Chordia, T., Sarkar, A., and Subrahmanyam, A. (2005), “An Empirical Analysis of Stock and Bond market liquidity”, Review of Financial Studies, Vol.18, No.1 pp.85-129.
16.Connolly, R., Stivers, C., and Sun, L. (2005), “Stock Market Uncertainty and the Stock-Bond Return Relation”, Journal of Financial and Quantitative Analysis, Vol.40, No.1, pp.161-194.
17.Dew-Becker, I., and Giglio, S. (2016), “Asset Pricing in the Frequency Domain: Theory and Empirics”, Review of Financial Studies, Vol.29, No.8 pp.2029-2068.
18.Diebold, F.X., and Yilmaz, K. (2009), “Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets”, Economic Journal, Vol.119, No.534 pp.158-171.
19.Diebold, F.X., and Yilmaz, K. (2012), “Better To Give Than To Receive: Predictive Directional Measurement of Volatility Spillovers”, International Journal of Forecasting, Vol.28, No.1 pp.57-66.
20.Diebold, F.X., and Yilmaz, K. (2014) “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms”, Journal of Econometrics, Vol.182, No.1, pp.119-134.
21.Ederington, L. H., and Guan, W. (2010), “How Asymmetric Is US Stock Market Volatility?”, Journal of Financial Markets, Vol.13, No.2, pp.225–248.
22.Fleming, J., Kirby, C., and Ostdiek, B. (1998), “Information and Volatility Linkages in the Stock, Bond, and Money Markets”, Journal of financial economics, Vol.49, No.1 pp.111-137.
23.Gulko, L. (2002), “Decoupling”, Journal of Portfolio Management, Vol.28, No.3 pp.59-66.
24.Hansen B. (2017), “Regression Kink with an Unknown Threshold”, Journal of Business and Economic Statistics, Vol.35, No.2 pp.228-240.
25.Kang, W., Ratti, R. A., and Yoon, K. H. (2014), “The Impact of Oil Price Shocks on U.S. Bond Market Returns”, Energy Economics, Vol.44, pp.248-258.
26.Křehlík, T., and Baruník, J. (2017), “Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets”, Energy Economics, Vol.65, pp.208-218.
27.Li, J., and Yu, J. (2012), “Investor Attention, Psychological Anchors, and Stock Return Predictability”, Journal of Financial Economics, Vol.104, No.2 pp.401-419.
28.Lin, F., Yang, S., Marsh, T., and Chen, Y. (2018), “Stock and Bond Return Relations and Stock Market Uncertainty: Evidence from Wavelet Analysis”, International Review of Economics and Finance, Vol.55, pp.285-294.
29.Miller, J. I., and Ratti, R. A. (2009), “Crude Oil and Stock Markets: Stability, Instability, and Bubbles”, Energy Economics, Vol.31, No.4 pp.559-568.
30.Nandha, M., and Faff, R. (2008), “Does Oil Move Equity Prices? A Global View”, Energy Economics, Vol.30, No.3 pp.986-997.
31.Pesaran, H.H., and Shin, Y. (1998), “Generalized Impulse Response Analysis in Linear Multivariate Models”, Economics Letter, Vol.58, No.1 pp.17-29.
32.Ross, S.A. (1989), “Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy”, Journal of Finance, Vol.44, No.1, pp.1-17.
33.Sadorsky, P. (1999), “Oil Price Shocks and Stock Market Activity”, Energy Economics, Vol.21, No.5 pp.449-469.
34.Segal, G., I. Shaliastovich, and Yaron A. (2015), “Good and Bad Uncertainty: Macroeconomic and Financial Market Implications”, Journal of Financial Economics, Vol.117, No.2 pp.369–397
35.Sim, N., and Zhou, H. (2015), “Oil Prices, US Stock Return, and the Dependence between Their Quantiles”, Journal of Banking and Finance, Vol.55, pp.1-8.
36.Stiassny, A. (1996), “A Spectral Decomposition for Structural VAR Models”, Empirical Economics, Vol.21, No.4 pp.535-555.
37.Vayanos, D. (2004), “Flight to Quality, Flight to Liquidity, and the Pricing of Risk”, Working Paper, National Bureau of Economic Research.
38.Veronesi, P. (1999), “Stock Market Overreactions to Bad News in Good Times: A Rational Expectations Equilibrium Model”, Review of Financial Studies, Vol.12, No.5, pp.975-100.