nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒06‒10
eight papers chosen by

  1. Common Risk Factors in Cryptocurrency By Yukun Liu; Aleh Tsyvinski; Xi Wu
  2. A simple return generating model in discrete time; implications for market efficiency testing By Alexandros E. Milionis
  3. Dynamics and Heterogeneity of Subjective Stock Market Expectations By Heiss, Florian; Hurd, Michael; Rossmann, Tobias; Winter, Joachim; van Rooij, Maarten
  4. Bond Risk Premiums at the Zero Lower Bound By Martin Møller Andreasen; Kasper Jørgensen; Andrew Meldrum
  5. Mildly Explosive Dynamics in U.S. Fixed Income Markets By Contessi, Silvio; De Pace, Pierangelo; Guidolin, Massimo
  6. Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method By Jurdi, Doureige; Kim, Jae
  7. The Effect of Possible EU Diversification Requirements on the Risk of Banks’ Sovereign Bond Portfolios By Craig, Ben R.; Giuzio, Margherita; Paterlini, Sandra
  8. Efficient Dynamic Yield Curve Estimation in Emerging Financial Markets By Makram El-Shagi; Lunan Jiang

  1. By: Yukun Liu; Aleh Tsyvinski; Xi Wu
    Abstract: We find that three factors – cryptocurrency market, size, and momentum – capture the cross-sectional expected cryptocurrency returns. We consider a comprehensive list of price- and market-related factors in the stock market, and construct their cryptocurrency counterparts. Nine cryptocurrency factors form successful long-short strategies that generate sizable and statistically significant excess returns. We show that all of these strategies are accounted for by the cryptocurrency three-factor model.
    JEL: G12
    Date: 2019–05
  2. By: Alexandros E. Milionis (Bank of Greece and University of the Aegean)
    Abstract: A linear return generating model is introduced. This model is a generalization in discrete time of the differential equation describing dynamical systems in continuous time. The model is useful in its own right, as it provides a simplified, yet credible, quantitative description of the reality. Further, the model is used as a tool for a theoretical study of market efficiency testing. This is obtained by modelling certain market conditions under which new information is released and reflected in asset prices on the one hand, and, on the other hand, by recording what established econometric testing approaches conclude, about the hypothesis of market efficiency. Amongst others it is argued that, contrary to the general belief, theoretically a random walk in asset prices, under certain conditions, could be associated with profoundly inefficient markets. Furthermore, an enhancement of the battery of statistical tests for market efficiency is proposed by the potential application of specific forms of the suggested linear dynamic model and the possible advantages over the existing techniques are discussed.
    Keywords: Market Efficiency Testing; Random Walks; Return Generating Model; Return Predictability.
    JEL: G14 G15
    Date: 2019–04
  3. By: Heiss, Florian (University of Dusseldorf); Hurd, Michael (RAND); Rossmann, Tobias (University of Munich); Winter, Joachim (LMU Munich); van Rooij, Maarten (De Nederlandsche Bank)
    Abstract: Between 2004 and 2016, we elicited individuals' subjective expectations of stock market returns in a Dutch internet panel at bi-annual intervals. In this paper, we develop a panel data model with a finite mixture of expectation types who differ in how they use past stock market returns to form current stock market expectations. The model allows for rounding in the probabilistic responses and for observed and unobserved heterogeneity at several levels. We estimate the type distribution in the population and find evidence for considerable heterogeneity in expectation types and meaningful variation over time, in particular during the financial crisis of 2008/09.
    Keywords: expectations; stock markets; financial crisis; mixture models; surveys;
    JEL: D12 D84 G11
    Date: 2019–05–28
  4. By: Martin Møller Andreasen (University of Aarhus and CREATES); Kasper Jørgensen (Board of Governors of the Federal Reserve System); Andrew Meldrum (Board of Governors of the Federal Reserve System)
    Abstract: This paper documents a significantly stronger relationship between the slope of the yield curve and future excess bond returns on Treasuries from 2008-2015 than before 2008. This new predictability result is not matched by the standard shadow rate model with Gaussian factor dynamics, but extending the model with regime-switching in the (physical) dynamics of the factors at the lower bound resolves this shortcoming. The model is also consistent with the downwards trend in surveys on short rate expectations at long horizons, but requires a break in the level of its factors to closely fit the low level of these surveys since 2015.
    Keywords: Dynamic term structure model, bond return predictability, shadow rate model, structural break, regime-switching
    JEL: E43 E44 G12
    Date: 2019–05–14
  5. By: Contessi, Silvio (Department of Economics, Pomona College); De Pace, Pierangelo (Department of Economics, Pomona College); Guidolin, Massimo (Department of Economics, Pomona College)
    Abstract: We use a recently developed right-tail variation of the Augmented Dickey-Fuller unit root test to identify and date-stamp periods of mildly explosive behavior in the weekly time series of eight U.S. fixed income yield spreads between September 2002 and April 2018. We find statistically significant evidence of mildly explosive dynamics in six of these spreads, two of which are short/medium-term mortgage- related spreads. We show that the time intervals characterized by instability that we estimate from these yield spreads capture known episodes of financial and economic distress in the U.S. economy. Mild explosiveness migrates from short-term funding markets to medium- and long-term markets during the Great Financial Crisis of 2007-09. Furthermore, we statistically validate the conjecture, originally suggested by Gorton (2009a,b), that the initial panic of 2007 migrated from segments of the ABX market to other U.S. fixed income markets in the early phases of the financial crisis.
    Keywords: finance, investment analysis, fixed income markets, yield spreads, mildly explosive behavior
    Date: 2019–02–04
  6. By: Jurdi, Doureige; Kim, Jae
    Abstract: We examine whether the stock market return is predictable from a range of financial indicators and macroeconomic variables, using monthly U.S. data from 1926 to 2012. We adopt the improved augmented regression method for parameter estimation, statistical inference, and out-of-sample forecasting. By employing moving sub-sample windows, we evaluate the time-variation of predictability free from data snooping bias and report changes in predictability dynamics over time. Although we may find statistically significant in-sample predictability from time to time, the associated effect size estimates are fairly small in most cases. We also find weak predictability of the stock market return from multistep ahead (out-of-sample) forecasts. In addition, we find that mean-variance investors realize sporadic economic gains in utility based on predictive regression forecasts relative to naive model historic average forecasts
    Keywords: Bias-correction; Financial ratios; Forecasting; Return predictability; Utility gains
    JEL: G17
    Date: 2019–05–20
  7. By: Craig, Ben R. (Federal Reserve Bank of Cleveland); Giuzio, Margherita (European Central Bank); Paterlini, Sandra (University of Trento)
    Abstract: Recent policy discussion includes the introduction of diversification requirements for sovereign bond portfolios of European banks. In this paper, we evaluate the possible effects of these constraints on risk and diversification in the sovereign bond portfolios of the major European banks. First, we capture the dependence structure of European countries’ sovereign risks and identify the common factors driving European sovereign CDS spreads by means of an independent component analysis. We then analyze the risk and diversification in the sovereign bond portfolios of the largest European banks and discuss the role of “home bias,” i.e., the tendency of banks to concentrate their sovereign bond holdings in their domicile country. Finally, we evaluate the effect of diversification requirements on the tail risk of sovereign bond portfolios and quantify the system-wide losses in the presence of fire-sales. Under our assumptions about how banks respond to the new requirements, demanding that banks modify their holdings to increase their portfolio diversification may mitigate fire-sale externalities, but it may be ineffective in reducing portfolio risk, including tail risk.
    Keywords: Bank regulation; sovereign-bank nexus; sovereign risk; home bias; diversification;
    JEL: G01 G11 G21 G28
    Date: 2019–05–28
  8. By: Makram El-Shagi (Center for Financial Development and Stability at Henan University, and School of Economics at Henan University, Kaifeng, Henan); Lunan Jiang (Center for Financial Development and Stability at Henan University, and School of Economics at Henan University, Kaifeng, Henan)
    Abstract: The current state-of-the-art estimation of yield curves relies on the dynamic state space version of the Nelson and Siegel (1987) model proposed in the seminal paper by Diebold et al. (2006). However, things become difficult when applying their approach to emerging economies with less frequently bond issuance and more sparse maturity available. Therefore, the traditional state space representation, which requires dense and fixed grids of maturities, may not be possible. One remedy is to use the traditional Nelson and Siegel (1987) OLS estimation instead, though it sacrifices efficiency by ignoring the time dimension. We propose a simple augmentation of the Diebold et al. (2006) framework, which is more efficient than OLS estimation as it allows exploiting information from all available bonds and the time dependency of yields. We demonstrate the efficiency gains generated by our method in five case studies for major emerging economies including four of the BRICS.
    Keywords: Yield curve, dynamic modeling, state space model, efficiency, BRICS
    JEL: E52 E43
    Date: 2019–05

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