nep-fmk New Economics Papers
on Financial Markets
Issue of 2016‒12‒04
six papers chosen by



  1. Forecasting stock market returns by summing the frequency-decomposed parts By Faria, Gonçalo; Verona, Fabio
  2. Multiple Time Series Ising Model for Financial Market Simulations By Tetsuya Takaishi
  3. Asset Pricing with Endogenously Uninsurable Tail Risks By anmol bhandari; Hengjie Ai
  4. Decomposing Global Yield Curve Co-Movement By Byrne, Joseph P; Cao, Shuo; Korobilis, Dimitris
  5. Bond Liquidity at the Oslo Stock Exchange By Ødegaard, Bernt Arne
  6. Institutional Herding and Its Price Impact : Evidence from the Corporate Bond Market By Fang Cai; Song Han; Dan Li; Yi Li

  1. By: Faria, Gonçalo; Verona, Fabio
    Abstract: We forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-the-parts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically significant improvements over historical mean forecasts, with monthly out-of-sample R2 of 3.27% and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns.
    Keywords: predictability, stock returns, equity premium, asset allocation, frequency domain, wavelets
    JEL: G11 G12 G14 G17
    Date: 2016–11–28
    URL: http://d.repec.org/n?u=RePEc:bof:bofrdp:2016_029&r=fmk
  2. By: Tetsuya Takaishi
    Abstract: In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated.
    Date: 2016–11
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1611.08088&r=fmk
  3. By: anmol bhandari (university of minnesota); Hengjie Ai (University of Minnesota)
    Abstract: This paper studies asset pricing implications of idiosyncratic risk in labor productivities in a model where markets are endogenously incomplete. Well-diversified owners of firms provide insurance to workers using long-term wage contracts but cannot commit to ventures that yield a net present value of dividends. We show that under the optimal contract subject to limited commitment, workers are uninsured against tail risks in idiosyncratic productivities. Risk compensations are higher when we calibrate the model to replicate the feature that tail risk in labor income is more pervasive in recessions relative to expansions. Besides salient features of equity and bond markets, the model is consistent with other empirical facts such as the cyclicality of factor shares and limited stock market participation.
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:red:sed016:1523&r=fmk
  4. By: Byrne, Joseph P; Cao, Shuo; Korobilis, Dimitris
    Abstract: This paper explains the co-movement of global yield curve dynamics using a Bayesian hierarchical factor model augmented with macro fundamentals. Our novel modeling approach reveals the relative importance of global shocks through two transmission channels: the policy and risk channels. Global inflation is the most important traditional macro fundamentals for international yields and operates through a policy channel. Economic uncertainty and sentiment are also important in driving global yield co-movements, through a risk channel.
    Keywords: Global Yield Curves, Co-Movement, Transmission Channels, Global Fundamentals, Sentiment, Economic Uncertainty, Bayesian Factor Model
    Date: 2016–08
    URL: http://d.repec.org/n?u=RePEc:esy:uefcwp:18194&r=fmk
  5. By: Ødegaard, Bernt Arne (UiS)
    Abstract: We characterize the liquidity of bond trading at the Oslo Stock Exchange (OSE). We use the complete history of bond prices quoted at the OSE from 1990 to 2015. We first characterize the market place, summarize trading grouped by type of issuers. The OSE can be characterized as a market place with a few bonds traded often, the rest traded seldom. The active bonds are Treasury securities, which typically trade on a daily basis. A second category of active bonds are \emph{covered bonds}, a type of bond introduced as recent as 2008 (in the wake of the financial crisis). The remainder of bonds at the OSE are traded seldom. The activity of the bond market at the OSE has increased markedly in the post-2008 period. While Treasury securities remain the most active class, covered bonds has seen a marked increase in liquidity. We also see an increase in activity for the other bond groups. The number of bonds listed has doubled in the last ten years, with financial and industrial issuers increasing the most. The market had more than 3000 different bond issues active in the last five years. However, only half of these bonds trade more than five times a year. The second part of the paper investigates the feasibility of measuring liquidity in the Norwegian bond market. Is it possible to construct liquidity measures that are informative about the state of the Norwegian financial market? We calculate three different measures that can be calculated from daily data: Bid/Ask Spreads, the Amihud [2002] ILLIQ measure, and the Corwin and Schultz [2012] spread estimate from high/low prices. Except for Treasuries, the liquidity measures are hard to calculate due to limited trading interest. Of the three liquidity measures, the Corwin and Schultz measure seem to be the preferred, although the measures are clearly correlated. All measures show that aggregate bond market liquidity covary with slowdowns in the Norwegian economy, with liquidity worsening (trading costs/spreads increasing) around such events as the 1992 Banking Crisis and the 2008 Financial Crisis. We also compare estimates of trading costs for various types of bonds with equities, and find that the most expensive to trade is equities. Trading costs for corporate bonds are lower than equities, but higher than Treasury bonds, which is the category with lowest estimated transaction costs. This is contrary to the evidence from the US, and most European bond markets, where estimates of transaction costs for corporate bonds are much higher than trading costs for equities.
    Keywords: Bond Markets; Liquidity; Trading Costs; Oslo Stock Exchange
    JEL: G10 G20
    Date: 2016–11–23
    URL: http://d.repec.org/n?u=RePEc:hhs:stavef:2016_016&r=fmk
  6. By: Fang Cai; Song Han; Dan Li; Yi Li
    Abstract: Among growing concerns about potential financial stability risks posed by the asset management industry, herding has been considered as an important risk amplification channel. In this paper, we examine the extent to which institutional investors herd in their trading of U.S. corporate bonds and quantify the price impact of such herding behavior. We find that, relative to what is documented for the equity market, the level of institutional herding is much higher in the corporate bond market, particularly among speculative-grade bonds. In addition, mutual funds have become increasingly likely to herd when they sell, a trend not observed among insurance companies and pension funds. We also show that bond investors herd not only within a quarter, but also over adjacent quarters. Such persistence in trading is largely driven by funds imitating the trading behavior of other funds in the previous quarter. Finally, we find that there is an asymmetry in the price impact of herding. While buy herding is associated with a permanent price impact that is consistent with price discovery, sell herding results in transitory yet significant price distortions. The price destabilizing effect of sell herding is particularly strong for high-yield bonds, small bonds, and illiquid bonds and during the recent global financial crisis.
    Keywords: Corporate Bond ; Herding ; Institutional Investors ; Liquidity ; Return Reversal
    JEL: G01 G02 G12 G14 G20
    Date: 2016–10
    URL: http://d.repec.org/n?u=RePEc:fip:fedgfe:2016-91&r=fmk

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