Adrian, T., Crump, R.K., Vogt, E., 2016. Nonlinearity and flight-to-safety in the riskreturn tradeoff for stocks and bonds. CEPR Discussion Paper No. DP11401.
Aielli, G.P., 2013, Dynamic conditional correlation: on properties and estimation. Journal of Business & Economic Statistics, 31, 282-299.
Andersen, T.G., Bollerslev, T., Diebold, F.X., Vega, C., 2007. Real-time price discovery in global stock, bond, and foreign exchange markets. Journal of International Economics, 73, 251-277.
- Andersson, M., Krylova, E., Vähämaa, S., 2008. Why does the correlation between stock and bond returns vary over time? Applied Financial Economics, 18, 139-151.
Paper not yet in RePEc: Add citation now
Asgharian, H., Christiansen, C., Hou, A.J., 2015. Effects of macroeconomic uncertainty on the stock and bond markets. Finance Research Letters, 13, 10-16.
Asgharian, H., Christiansen, C., Hou, A.J., 2016. Macro-finance determinants of the long-run stock-bond correlation: The DCC-MIDAS specification. Journal of Financial Econometrics, 14, 617-642.
Baele, L., Bekaert, G., Inghelbrecht, K., 2010. The determinants of stock and bond return comovements. Review of Financial Studies, 23, 2374-2428.
Bauwens, L., Braione, M., Storti, G., 2016. Forecasting comparison of long term component dynamic models for realized covariance matrices. Annals of Economics and Statistics, 123-124, 103-134.
Bauwens, L., Braione, M., Storti, G., 2017. A dynamic component model for forecasting high-dimensional realized covariance matrices. Econometrics and Statistics, 1, 40-61.
Bauwens, L., Hafner, C. M., Pierret, D., 2013. Multivariate volatility modeling of electricity futures. Journal of Applied Econometrics, 28, 743-761.
- Bauwens, L., Otranto, E., 2016. Modeling the Dependence of Conditional Correlations on Market Volatility. Journal of Business & Economic Statistics, 34, 254-268.
Paper not yet in RePEc: Add citation now
Bekaert, G., Engstrom, E., Xing, Y., 2009. Risk, uncertainty, and asset prices. Journal of Financial Economics, 91, 59-82.
Bollerslev, T., 1990. Modelling the coherence in short-run nominal exchange rates: a multivariate generalised ARCH model. Review of Economics and Statistics, 72, 498-505.
Bollerslev, T., Patton, A.J., Quaedvlieg, R., 2016. Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions. SSRN Working Paper No. 2759388.
- Bollerslev, T., Wooldridge, J., 1992. Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews, 11, 143-172.
Paper not yet in RePEc: Add citation now
Boyd, J.H., Hu, J., Jagannathan, R., 2005. The stock market’s reaction to unemployment news: Why bad news is usually good for stocks. Journal of Finance, 60, 649-672.
Campbell, J., Ammer, J., 1993. What moves the stock and bond markets? A variance decomposition for long-term asset returns. Journal of Finance, 48, 3-37.
Cappiello, L., Engle, R.F., Sheppard, K., (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4, 537-572.
- Clarida, R., Waldman, D., 2008. Is bad news about inflation good news for the exchange rate? In: Asset Prices and Monetary Policy, edited by J. Y. Campbell, pp. 371-392. Chicago: University of Chicago Press.
Paper not yet in RePEc: Add citation now
Colacito, R., Engle, R.F., Ghysels, E., 2011. A component model for dynamic correlations. Journal of Econometrics, 164, 45-59.
Connolly, R., Stivers, C., Sun, L., 2005. Stock market uncertainty and the stock-bond return relation. The Journal of Financial and Quantitative Analysis, 40, 161-194.
Conrad, C., Lamla, M.J., 2010. The high-frequency response of the EUR-USD exchange rate to ECB communication. Journal of Money, Credit and Banking, 42, 1391-1417.
Conrad, C., Loch, K., 2015. Anticipating long-term stock market volatility. Journal of Applied Econometrics, 30, 1090-1114.
- Conrad, C., Loch, K., Rittler, D., 2014. On the macroeconomic determinants of the long-term oil-stock correlation. Journal of Empirical Finance, 29, 26-40.
Paper not yet in RePEc: Add citation now
Conrad, C., Zumbach, K.U., 2016. The effect of political communication on European financial markets during the sovereign debt crisis. Journal of Empirical Finance, 39, 209-214.
David, A., Veronesi, P., 2013. What ties return volatilities to price valuations and fundamentals ? Journal of Political Economy, 121, 682-746.
DeMiguel, V., Garlappi, L. Uppal, R., 2009. Optimal versus naive diversification: how inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22, 1915-1953.
Diebold, F.X., Mariano, R.S., 1995. Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 253-263.
Dovern, J., Fritsche, U., Slacalek, J., 2012. Disagreement among forecasters in G7 countries.
Dräger, L., Lamla, M.J., Pfajfar, D., 2016. Are survey expectations theory-consistent? The role of central bank communication and news. European Economic Review, 85, 84111.
Engle, C., West, K.D., 2006. Taylor rules and the Deutschmark-Dollar real exchange rate. Journal of Money, Credit and Banking, 38, 1175-1194.
- Engle, R.F., 2002. Dynamic conditional correlation - a simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20, 339-350.
Paper not yet in RePEc: Add citation now
- Engle, R.F., 2008. Anticipating correlations. Princeton University Press.
Paper not yet in RePEc: Add citation now
Engle, R.F., Colacito, R., 2006. Testing and valuing dynamic correlations for asset allocation. Journal of Business & Economic Statistics, 24, 238-253.
- Engle, R.F., Sheppard, K., 2005. Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. Working Paper, University of California, San Diego.
Paper not yet in RePEc: Add citation now
Fama, E.F., Schwert, G.W., 1977. Asset returns and inflation. Journal of Financial Economics, 5, 115-146.
Hafner, C.M., Linton, O., 2010. Efficient estimation of a multivariate multiplicative volatility model. Journal of Econometrics, 159, 55-73.
- hS,t+khB,t+k|FÄ−1) as in Engle (2008, equation (9.10)).
Paper not yet in RePEc: Add citation now
Kole, E., Markwat, T.D., Opschoor, A., van Dijk, D., 2015. Forecasting Value-at-Risk under temporal and portfolio aggregation. Tinbergen Institute Discussion Paper.
Kroner, K., Ng, V., 1998. Modeling asymmetric comovements of asset returns. Review of Financial Studies, 11, 817-844.
- Next, we need to derive an expression for ÃÂSB,t+k|Ä−1. Again, as in Engle (2008), we use the approximation ÃÂSB,t+k|Ä−1 = E[ZS,t+k−1ZB,t+k−1|FÄ−1] ≈ E[qSB,t+k|FÄ−1]. Engle and Sheppard (2005) show that this approximation works well if the diagonal elements of Qt are close to one and k is large. Finally, we apply the approximation to equation (4) and obtain ÃÂSB,t+k|Ä−1 ≈ ÃÂ̄SB,Ä + (αSB + βSB)(ÃÂSB,t+k−1|Ä−1 − ÃÂ̄SB,Ä ) (28) = ÃÂ̄SB,Ä + (αSB + βSB)k−1 (ÃÂSB,t+1|Ä−1 − ÃÂ̄SB,Ä ). (29) B Tables
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Paye, B.S., 2012. ‘DeÃŒÂja Vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics, 106, 527-546.
Perego, E.R., Vermeulen, W. N., 2016. Macro-economic determinants of European stock and government bond correlations: A tale of two regions. Journal of Empirical Finance, 37, 214-232.
Schwert, G.W., 1989. Why does stock market volatility change over time? Journal of Finance, 44, 1115-1153.
Shiller, R.J., Beltratti, A., 1992. Stock prices and bond yields: Can their comovements be explained in terms of present value models? Journal of Monetary Economics, 30, 25-46.
- Stürmer, K., 2016. Time-varying volatility persistence in a GARCH-MIDAS framework. Working paper.
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- Yang, J., Zhou, Y., Wang, Z., 2009. The stock-bond correlation and macroeconomic conditions: One and a half centuries of evidence. Journal of Banking and Finance, 33, 670-680. A Construction of Monthly Covariance Forecasts We define monthly stock and bond returns as rS,Ä = N(Ä) X k=1 rS,t+k and rB,Ä = N(Ä) X k=1 rB,t+k, where day t denotes the last day of month Ä − 1. The covariance between monthly stock and bond returns can be written as Cov(rS,Ä , rB,Ä |FÄ−1) = N(Ä) X k=1 Cov(rS,t+k, rB,t+k|FÄ−1) (24) = N(Ä) X k=1 E(ZS,t+kZB,t+k p hS,t+khB,t+k|FÄ−1) (25) ≈ N(Ä) X k=1 E(ZS,t+kZB,t+k|FÄ−1) q hS,t+k|Ä−1hB,t+k|Ä−1 (26) = N(Ä) X k=1 ÃÂSB,t+k|Ä−1 q hS,t+k|Ä−1hB,t+k|Ä−1 (27) where the first line follows because rS,t+k and rB,t+j are uncorrelated for k 6= j and the approximation in equation (26) is based on a first order Taylor series expansion of E(ZS,t+kZB,t+k p
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