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On the economic determinants of optimal stock-bond portfolios: international evidence. (2017). Conrad, Christian ; Stuermer, Karin .
In: Working Papers.
RePEc:awi:wpaper:0636.

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  1. Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models. (2023). Virbickait, Audron ; Nguyen, Hoang.
    In: Energy Economics.
    RePEc:eee:eneeco:v:124:y:2023:i:c:s0140988323002360.

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  2. Time-varying risk of nominal bonds: How important are macroeconomic shocks?. (2022). Ermolov, Andrey.
    In: Journal of Financial Economics.
    RePEc:eee:jfinec:v:145:y:2022:i:1:p:1-28.

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  3. Determinants of stock-bond market comovement in the Eurozone under model uncertainty. (2019). Skintzi, Vasiliki D.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:61:y:2019:i:c:p:20-28.

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References

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  38. hS,t+khB,t+k|Fτ−1) as in Engle (2008, equation (9.10)).
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  41. Next, we need to derive an expression for ρSB,t+k|τ−1. Again, as in Engle (2008), we use the approximation ρSB,t+k|τ−1 = E[ZS,t+k−1ZB,t+k−1|Fτ−1] ≈ E[qSB,t+k|Fτ−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 ρSB,t+k|τ−1 ≈ ρ̄SB,τ + (αSB + βSB)(ρSB,t+k−1|τ−1 − ρ̄SB,τ ) (28) = ρ̄SB,τ + (αSB + βSB)k−1 (ρSB,t+1|τ−1 − ρ̄SB,τ ). (29) B Tables
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  42. Paye, B.S., 2012. ‘Déja Vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics, 106, 527-546.

  43. 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.

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  46. Stürmer, K., 2016. Time-varying volatility persistence in a GARCH-MIDAS framework. Working paper.
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  47. 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,τ = N(τ) X k=1 rS,t+k and rB,τ = N(τ) X k=1 rB,t+k, where day t denotes the last day of month τ − 1. The covariance between monthly stock and bond returns can be written as Cov(rS,τ , rB,τ |Fτ−1) = N(τ) X k=1 Cov(rS,t+k, rB,t+k|Fτ−1) (24) = N(τ) X k=1 E(ZS,t+kZB,t+k p hS,t+khB,t+k|Fτ−1) (25) ≈ N(τ) X k=1 E(ZS,t+kZB,t+k|Fτ−1) q hS,t+k|τ−1hB,t+k|τ−1 (26) = N(τ) X k=1 ρSB,t+k|τ−1 q hS,t+k|τ−1hB,t+k|τ−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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