nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒11‒04
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Option-based Equity Risk Premiums By Alan L. Lewis
  2. Insider trading and gender By Eckbo, B. Espen; Ødegaard, Bernt Arne
  3. Credit risk with asymmetric information and a switching default threshold By Imke Redeker; Ralf Wunderlich
  4. Systemic Risk from Interbank Credit Markets? By Gries, Thomas; Mitschke, Alexandra
  5. ECB Announcements and Stock Market Volatility By Neugebauer, Frederik
  6. Sparsity and Stability for Minimum-Variance Portfolios By Sven Husmann; Antoniya Shivarova; Rick Steinert
  7. Net Capital Flows and Portfolio Diversification By Constantin Bürgi; Vida Bobic; Min Wu; Constantin Bürgi
  8. The dynamic impact of FX interventions on financial markets By Rieth, Malte; Menkhoff, Lukas; Stöhr, Tobias
  9. Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality By Olivier Gu\'eant; Iuliia Manziuk
  10. Cannabis Stock Behavior and Investor’s Expectations on the TSX: A Mixed Method Approach By Oludamola Durodola; Deepika Chotee
  11. Weekly idiosyncratic risk metrics and idiosyncratic momentum: Evidence from the Chinese stock market By Huai-Long Shi; Wei-Xing Zhou
  12. Credit scoring in SME asset-backed securities: An Italian case study By Bedin, Andrea; Billio, Monica; Costola, Michele; Pelizzon, Loriana

  1. By: Alan L. Lewis
    Abstract: We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting $N$-component Gaussian mixture models to option quotes, where $N$ is a small integer (here 4 or 5). These densities are transformed to their real-world equivalents by exponential tilting with a single parameter: the Coefficient of Relative Risk Aversion $\kappa$. From history, I estimate $\kappa = 3 \pm 0.5$. From the inferred real-world densities, the equity risk premium is readily calculated. Three term structures serve as examples.
    Date: 2019–10
  2. By: Eckbo, B. Espen (Tuck School of Business at Darthmouth College); Ødegaard, Bernt Arne (University of Stavanger)
    Abstract: We test for systematic gender-differences in trading propensity and performance using the population of primary insiders on the Oslo Stock Exchange (OSE), 1986--2016. We use Norway's 2005 board gender quota law, which nearly tripled the population of female directors, as an exogenous shock to female directors' access to information through the expanded director network. Moreover, we use differences in trading activity following the exogenous increase in trading risk caused by the 2008 financial crisis to identify gender-based differences in risk aversion. We find no significant gender-based difference in insider trading performance, whether before or after the mandatory board gender-balancing. However, we find that female insiders are significantly more likely to buy shares during the financial crisis, which is consistent with female directors and executives exhibiting less (not more) risk aversion than their male counterparts.
    Keywords: Insider trading; gender; risk aversion; portfolio performance; director network; board gender- balancing
    JEL: G14 M14
    Date: 2019–10–24
  3. By: Imke Redeker; Ralf Wunderlich
    Abstract: We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of a constant default threshold. The default threshold at which the firm is liquidated is modeled as a random variable whose value is chosen by the management of the firm and dynamically adjusted to account for changes in the economy or the appointment of a new firm management. Investors on the market have no access to the value of the threshold and only anticipate the distribution of the threshold. We distinguish different information levels on the firm's assets and derive explicit formulas for the conditional default probability given these information levels. Numerical results indicate that the information level has a considerable impact on the estimation of the default probability and the associated credit yield spread.
    Date: 2019–10
  4. By: Gries, Thomas; Mitschke, Alexandra
    JEL: E44 E52 G11 G21
    Date: 2019
  5. By: Neugebauer, Frederik
    JEL: E52 E58 G12 G14
    Date: 2019
  6. By: Sven Husmann; Antoniya Shivarova; Rick Steinert
    Abstract: The popularity of modern portfolio theory has decreased among practitioners because of its unfavorable out-of-sample performance. Estimation errors tend to affect the optimal weight calculation noticeably, especially when a large number of assets is considered. To overcome these issues, many methods have been proposed in recent years, although most only address a small set of practically relevant questions related to portfolio allocation. This study therefore sheds light on different covariance estimation techniques, combines them with sparse model approaches, and includes a turnover constraint that induces stability. We use two datasets - comprising 319 and 100 companies of the S&P 500, respectively - to create a realistic and reproducible data foundation for our empirical study. To the best of our knowledge, this study is the first to show that it is possible to maintain the low-risk profile of efficient estimation methods while automatically selecting only a subset of assets and further inducing low portfolio turnover. Moreover, we provide evidence that using the LASSO as the sparsity-generating model is insufficient to lower turnover when the involved tuning parameter can change over time.
    Date: 2019–10
  7. By: Constantin Bürgi; Vida Bobic; Min Wu; Constantin Bürgi
    Abstract: This paper presents a new explanation for the sustained pattern of international net capital flows by modifying the standard consumption capital asset pricing model (CCAPM) to create net capital flows beyond the initial period. In addition to the well established link between asset returns and the cyclical correlation between countries in standard CCAPM models, our model links asset flows to the cyclical correlation. In particular, the model predicts that a country that has a low correlation with the global cycle should see net capital inflows. We provide strong empirical evidence in support of this link and a 0.1 increase in the correlation leads to a 0.5-0.7 percentage point decrease in the net capital inflows as a % of GDP.
    Keywords: net capital flows, productivity, growth, portfolio diversification
    JEL: F36 F43
    Date: 2019
  8. By: Rieth, Malte; Menkhoff, Lukas; Stöhr, Tobias
    JEL: F31 F33 E58
    Date: 2019
  9. By: Olivier Gu\'eant; Iuliia Manziuk
    Abstract: In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for a large number of bonds to asset managers from all around the globe. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. Useful models exist, most of them inspired by that of Avellaneda and Stoikov. These models describe the complex optimization problem faced by market makers: proposing bid and ask prices in an optimal way for making money out of the difference between bid and ask prices while mitigating the market risk associated with holding inventory. While most of the models only tackle one-asset market making, they can often be generalized to a multi-asset framework. However, the problem of solving numerically the equations characterizing the optimal bid and ask quotes is seldom tackled in the literature, especially in high dimension. In this paper, our goal is to propose a numerical method for approximating the optimal bid and ask quotes over a large universe of bonds in a model \`a la Avellaneda-Stoikov. Because we aim at considering a large universe of bonds, classical finite difference methods as those discussed in the literature cannot be used and we present therefore a discrete-time method inspired by reinforcement learning techniques. More precisely, the approach we propose is a model-based actor-critic-like algorithm involving deep neural networks.
    Date: 2019–10
  10. By: Oludamola Durodola (Business Faculty Lakeland College, Canada); Deepika Chotee (Business Faculty Lakeland College, Canada)
    Abstract: This study examines the behavior of cannabis stock on the Toronto Stock Exchange and why investors are interested in cannabis stock. The theory of heterogeneous beliefs, bounded rationality theory and the theory of addiction grounded the study. We employed basic descriptive statistics and the Kruskal-Wallis test including an in-depth interview of investors using convenience sampling methods. The study findings showed that cannabis stocks exhibit a higher-level of risk volatility when compared to speculative and growth stocks on the Toronto Stock Exchange within the period under investigation. Other findings show that cannabis stocks share similar characteristics with other speculative stocks but also possess unique features. Finally, investors are interested in cannabis stocks because of its potential for future strong earnings on the platform of the theory of addiction as discussed in the study.
    Keywords: cannabis, Toronto Stock Exchange (TSX), addiction, Canada
    Date: 2019–08
  11. By: Huai-Long Shi; Wei-Xing Zhou
    Abstract: This paper focuses on the weekly idiosyncratic momentum (IMOM) as well as its risk-adjusted versions with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum and idiosyncratic momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOMportfolios with respect to various risk metrics. At last, we explore the possible explanations to the IMOM as well as risk-based IMOM portfolios. We find that 1) there is a prevailing contrarian effect and a IMOM effect for the whole sample; 2) a negative relation exists between most of the idiosyncratic risk metrics and the cross-sectional returns, and better performance is found that is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit a better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of the IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment.
    Date: 2019–10
  12. By: Bedin, Andrea; Billio, Monica; Costola, Michele; Pelizzon, Loriana
    Abstract: We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
    Keywords: credit scoring,probability of default,small and medium enterprises,asset-backed securities
    Date: 2019

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