nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒01‒21
five papers chosen by

  1. Emergence of stylized facts during the opening of stock markets By Sebastian M. Krause; Jonas A. Fiegen; Thomas Guhr
  2. The Risk of Becoming Risk Averse: A Model of Asset Pricing and Trade Volumes By Alvarez, Fernando; Atkeson, Andrew
  3. Empirical Asset Pricing via Machine Learning By Shihao Gu; Bryan Kelly; Dacheng Xiu
  4. International Stock Price Movements with Endogenous Clusters By Coroneo, Laura; Jackson, Laura E.; Owyang, Michael T.
  5. A macro-financial analysis of the corporate bond market By Hans Dewachter; Leonardo Iania; Wolfgang Lemke; Marco Lyrio

  1. By: Sebastian M. Krause; Jonas A. Fiegen; Thomas Guhr
    Abstract: Financial markets show a number of non-stationarities, ranging from volatility fluctuations over ever changing technical and regulatory market conditions to seasonalities. On the other hand, financial markets show various stylized facts which are remarkably stable. It is thus an intriguing question to find out how these stylized facts emerge. As a first example, we here investigate how the bid-ask-spread between best sell and best buy offer for stocks develops during the trading day. For rescaled and properly smoothed data we observe collapsing curves for many different NASDAQ stocks, with a slow power law decline of the spread during the whole trading day. This effect emerges robustly after a highly fluctuating opening period. Some so called large-tick stocks behave differently because of technical boundaries. Their spread closes to one tick shortly after the market opening. We use our findings for identifying the duration of the market opening which we find to vary largely from stock to stock.
    Date: 2018–12
  2. By: Alvarez, Fernando (University of Chicago); Atkeson, Andrew (Federal Reserve Bank of Minneapolis)
    Abstract: We develop a new general equilibrium model of asset pricing and asset trading volume in which agents’ motivations to trade arise due to uninsurable idiosyncratic shocks to agents’ risk tolerance. In response to these shocks, agents trade to rebalance their portfolios between risky and riskless assets. We study a positive question — When does trade volume become a pricing factor? — and a normative question — What is the impact of Tobin taxes on asset trading on welfare? In our model, economies in which marketwide risk tolerance is negatively correlated with trade volume have a higher risk premium for aggregate risk. Likewise, for a given economy, we find that assets whose cash flows are concentrated on states with high trading volume have higher prices and lower risk premia. We then show that Tobin taxes on asset trade have a first-order negative impact on ex-ante welfare, i.e., a small subsidy to trade leads to an improvement in ex-ante welfare. Finally, we develop an alternative version of our model in which asset trade arises from uninsurable idiosyncratic shocks to agents’ hedging needs rather than shocks to their risk tolerance. We show that our positive results regarding the relationship between trade volume and asset prices carry through. In contrast, the normative implications of this specification of our model for Tobin taxes or subsidies depend on the specification of agents’ preferences and non-traded endowments.
    Keywords: Liquidity; Trade volume; Asset pricing; Tobin taxes
    JEL: G12
    Date: 2018–12–31
  3. By: Shihao Gu; Bryan Kelly; Dacheng Xiu
    Abstract: We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. Our implementation establishes a new standard for accuracy in measuring risk premia summarized by an unprecedented out-of-sample return prediction R2. We identify the best performing methods (trees and neural nets) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premia measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.
    JEL: C45 C58 G11 G12
    Date: 2018–12
  4. By: Coroneo, Laura (Department of Economics and Related Studies, University of York); Jackson, Laura E. (Bentley University); Owyang, Michael T. (Federal Reserve Bank of St. Louis)
    Abstract: We use an endogenous cluster factor model to examine international stock return comovements of country-industry portfolios. Our model allows country-industry portfolio comovements to be driven by a global and a cluster component, with the cluster membership endogenously determined. Results indicate that country-industry portfolios tend to cluster mainly within geographical areas that can include one or more countries. The cluster component was the main driver of country-industry portfolio returns for most of the sample, except from mid-2000 to mid-2010s when the global component had a more prominent role. At the end of the sample, a large cluster among European countries emerges.
    Keywords: diversification; risk; international financial markets; clustered factor model
    JEL: C38 G15
    Date: 2018–10–24
  5. By: Hans Dewachter (National Bank of Belgium; Center for Economic Studies, University of Leuven and CESifo); Leonardo Iania (Louvain School of Management); Wolfgang Lemke (European Central Bank); Marco Lyrio (Insper Institute of Education and Research)
    Abstract: We assess the contribution of economic and financial factors in the determination of euro area corporate bond spreads over the period 2001-2015. The proposed multi-market, no-arbitrage affine term structure model is based on the methodology proposed by Dewachter, Iania, Lyrio, and Perea (2015). We model jointly the ‘risk-free curve’, measured by overnight index swap (OIS) rates, and the corporate yield curves for two rating classes (A and BBB). The model includes four spanned and six unspanned factors. We find that, in general, both economic (real activity and inflation) and financial factors (proxying risk aversion, flight to liquidity and general financial market stress) play a significant role in the determination of the spanned factors and hence in the dynamics of the risk-free yield curve and corporate bond spreads. Across the risk-free OIS curve, macroeconomic and financial factors are each responsible on average for explaining 30 and 65 percent of yield variation, respectively. For A-and BBB-rated corporate debt, the selected financial variables explain on average 50 percent of the variation in corporate spreads during the last decade.
    Keywords: Euro area corporate bonds; yield spread decomposition; unspanned macro factors
    JEL: E43 E44
    Date: 2018–12

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