nep-fmk New Economics Papers
on Financial Markets
Issue of 2017‒10‒29
four papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Asset Price Bubbles and Systemic Risk By Brunnermeier, Markus K; Rother, Simon; Schnabel, Isabel
  2. Asset Pricing and Excess Returns over the Market Return By Seung C. Ahn; Alex R. Horenstein
  3. Networks of Volatility Spillovers among Stock Markets By Eduard Baumöhl; Evžen Kocenda; Stefan Lyócsa; Tomás Vyrost
  4. Herding behaviour of Dutch pension funds in sovereign bond investments By I. Koetsier; J.A. Bikker

  1. By: Brunnermeier, Markus K; Rother, Simon; Schnabel, Isabel
    Abstract: This paper empirically analyzes the effects of asset price bubbles on systemic risk. Based on a broad sample of banks from 17 OECD countries between 1987 and 2015, we show that asset price bubbles in stock and real estate markets raise systemic risk at the bank level. The strength of the effect depends strongly on bank characteristics (bank size, loan growth, leverage, and maturity mismatch) as well as bubble characteristics (length and size). These findings suggest that the adverse effects of bubbles can be mitigated substantially by strengthening the resilience of financial institutions.
    Keywords: Asset price bubbles; CoVaR; Credit Booms; Financial crises; systemic risk
    JEL: E32 G01 G12 G20 G32
    Date: 2017–10
  2. By: Seung C. Ahn (Arizona State University); Alex R. Horenstein (University of Miami)
    Abstract: Some studies have found that the estimated market betas from multi-factor models have much smaller cross-sectional variations than those from the Capital Asset Pricing Model. This paper provides a theoretical explanation for this empirical finding. For the cases in which the market portfolio (of stocks) is a well-diversified but mean-variance inefficient one, we show that the market betas become unitary when the Capital Asset Pricing Model is augmented with the common factors in the space of excess returns. Consequently, the market betas have no power to explain the cross-sectional variation of expected stock returns. Based on this finding, we propose an alternative method that can identify the relevant factors for asset pricing. Specifically, we show that the relevant factors can be extracted by the principal components from a large set of excess stock returns over the market return. Analyzing US data on individual and portfolio stock returns, we develop a benchmark model with five principal component factors. We use the model to study if the five-factor model of Fama and French (2015) captures all the relevant information to span the space of excess returns. We find that the Fama-French model contains a large fraction of the relevant information, but there is still some room for improvement.
    Keywords: Excess returns, market portfolio, well-diversified portfolio, principal components. Publication Status: Submitted
    JEL: C58 G11 G12
    Date: 2017–09–20
  3. By: Eduard Baumöhl; Evžen Kocenda; Stefan Lyócsa; Tomás Vyrost
    Abstract: In our network analysis of 40 developed, emerging and frontier stock markets during 2006–2014, we describe and model volatility spillovers during global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We show significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market – volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.
    Keywords: volatility spillovers, stock markets, shock transmission, Granger causality network, spatial regression, financial crisis
    JEL: C31 C58 F01 G01 G15
    Date: 2017
  4. By: I. Koetsier; J.A. Bikker
    Abstract: This study investigates herding behaviour exhibited by Dutch pension funds in the sovereign bond market. It uses a unique dataset on sovereign bond holdings of pension funds, mutations and transactions between December 2008 and December 2014. It covers 67 large Dutch pension funds that invest in 109 countries. We find evidence of intensive herding behaviour of Dutch pension funds in sovereign bonds. Our findings also show that institutional factors, the macroeconomic environment and the financial market environment are among the determinants of herding behaviour in sovereign bonds. Our results also indicate that high diversification is not without costs as it intensifies herding behaviour. We find mixed evidence on whether pension funds are stabilising actors. The destabilising effect is most pronounced on the sell side, while stabilisation is most prominent under more extreme price shocks. The distinction between developing and emerging economies and developed economies does not change these results.
    Date: 2017–09

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