nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒12‒07
thirteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. How Has COVID-19 Affected Banking System Vulnerability? By Kristian S. Blickle; Matteo Crosignani; Fernando M. Duarte; Thomas M. Eisenbach; Fulvia Fringuellotti; Anna Kovner
  2. Momentum? What Momentum? By Theissen, Erik; Yilanci, Can
  3. The Solution of the Equity Premium Puzzle By Atilla Aras
  4. How news affects sectoral stock prices through earnings expectations and risk premia By Kristiansen, Kristian; Hvid, Anna Kirstine
  5. Stock market volatility and jumps in times of uncertainty By Megaritis, Anastasios; Vlastakis, Nikolaos; Triantafyllou, Athanasios
  6. Asset Pricing with Realistic Crises Dynamics By Goutham Gopalakrishna
  7. Portfolio Risk Measurement Using a Mixture Simulation Approach By Seyed Mohammad Sina Seyfi; Azin Sharifi; Hamidreza Arian
  8. What determines Initial Coin Offering success: A cross-country study By Muhammad Farooq AHMAD; Oskar KOWALEWSKI; Pawel PISANY
  9. Startup & Unicorn Growth Valuation By Andreas A. Aigner; Walter Schrabmair
  10. Pattern recognition in trading behaviors before stock price jumps: new method based on multivariate time series classification By Ao Kong; Robert Azencott; Hongliang Zhu
  11. CDS Central Counterparty Clearing Default Measures: Road to Recovery or Invitation to Predation? By Magdalena Tywoniuk
  12. Price Discovery in the U.S. Treasury Cash Market: On Principal Trading Firms and Dealers By James Collin Harkrader; Michael Puglia
  13. Systemic Risk and the COVID Challenge in the European Banking Sector By Nicola Borri; Giorgio Di Giorgio

  1. By: Kristian S. Blickle; Matteo Crosignani; Fernando M. Duarte; Thomas M. Eisenbach; Fulvia Fringuellotti; Anna Kovner
    Abstract: The COVID-19 pandemic has led to significant changes in banks’ balance sheets. To understand how these changes have affected the stability of the U.S. banking system, we provide an update of four analytical models that aim to capture different aspects of banking system vulnerability.
    Keywords: COVID-19; financial stability; fire sales; bank runs
    JEL: G2
    Date: 2020–11–16
  2. By: Theissen, Erik; Yilanci, Can
    Abstract: Risk-adjusted momentum returns are usually estimated by sorting stocks into a regularly rebalanced long-short portfolio based on their prior return and then running a full-sample regression of the portfolio returns on a set of factors (portfolio-level risk adjustment). This approach implicitly assumes constant factor exposure of the momentum portfolio. However, momentum portfolios are characterized by high turnover and time-varying factor exposure. We propose to estimate the risk exposure at the stock-level. The risk-adjusted return of the momentum portfolio in month t then is the actual return minus the weighted average of the expected returns of the component stocks (stock-level risk adjustment). Based on evidence from the universe of CRSP stocks, from sub-periods and size-based sub-samples, from volatility-scaled momentum strategies (Barroso and Santa-Clara 2015) and from an international sample covering 20 developed countries, we conclude that the momentum effect may be much weaker than previously thought.
    Keywords: Momentum,Risk adjustment,Time-series regression
    JEL: C58 G12
    Date: 2020
  3. By: Atilla Aras
    Abstract: In this paper, the solution of the equity premium puzzle was given. First, the Arrow-Pratt measure of relative risk aversion for detecting the risk behavior of investors was questioned, and then a new tool was developed to study the risk behavior of investors. This new tool in the new formulated model was tested for the equity premium puzzle for a solution. The results show that the calculated value of the coefficient of relative risk aversion is 2.201455 which is compatible with the empirical studies and as investors who invest in risk-free asset place disutility on the not sure wealth value, investors who invest in equity place utility on the not sure wealth value.
    Date: 2020–11
  4. By: Kristiansen, Kristian; Hvid, Anna Kirstine
    Abstract: A growing body of literature analyses the impact of news on companies’ equity prices. We add to this literature by showing that the transmission channel of news to prices differs across sectors. First, we disentangle sectoral equity prices into components of expected future earnings and equity risk premia. Then, we evaluate how these react to general and sector specific sentiment shocks constructed from Reuters news articles. We find that price changes for especially the financial sector are mainly driven by changes in equity risk premia, while changes in earnings expectations play a comparatively larger role for other sectors. JEL Classification: G10, G12, G14
    Keywords: dividend discount models, equity risk premia, news sentiment, stock returns, text analysis
    Date: 2020–11
  5. By: Megaritis, Anastasios; Vlastakis, Nikolaos; Triantafyllou, Athanasios
    Abstract: In this paper we examine the predictive power of latent macroeconomic uncertainty on US stock market volatility and jump tail risk. We find that increasing macroeconomic uncertainty predicts a subsequent rise in volatility and price jumps in the US equity market. Our analysis shows that the latent macroeconomic uncertainty measure of Jurado et al. (2015) has the most significant and long-lasting impact on US stock market volatility and jumps in the equity market when compared to the respective impact of the VIX and other popular observable uncertainty proxies. Our study is the first to show that the latent macroeconomic uncertainty factor outperforms the VIX when forecasting volatility and jumps after the 2007 US Great Recession. We additionally find that latent macroeconomic uncertainty is a common forecasting factor of volatility and jumps of the intraday returns of S&P 500 constituents and has higher predictive power on the volatility and jumps of the equities which belong to the financial sector. Overall, our empirical analysis shows that stock market volatility is significantly affected by the rising degree of unpredictability in the macroeconomy, while it is relatively immune to shocks in observable uncertainty proxies.
    Keywords: Jumps, Bipower variation, Realized volatility, Macroeconomic Uncertainty
    Date: 2020–11–26
  6. By: Goutham Gopalakrishna (Swiss Finance Institute (EPFL); Ecole Polytechnique Fédérale de Lausanne)
    Abstract: What causes deep recessions and slow recovery? I revisit this question and develop a macro-finance asset pricing model that quantitatively matches the salient empirical features of financial crises such as a large drop in the output, a high risk premium, reduced financial intermediation, and a long duration of economic distress. The model features leveraged intermediaries who are subjected to both capital and productivity shocks, and face a regime-dependent exit rate. I show that the model without time varying intermediary productivity and exit, which reduces to Brunnermeier and Sannikov (2016), suffers from a tension between the amplification and the persistence of financial crises. In particular, there is a trade-off between the unconditional risk premium, the conditional risk premium, and the probability and duration of crisis. Features that generate high financial amplification also induce faster recovery, at odds with the data. I show that my model resolves this tension and generates realistic crises dynamics. The model is solved using a novel numerical method with active machine learning that is scalable and alleviates the curse of dimensionality.
    Keywords: Financial Intermediation, Intermediary Asset Pricing, Machine Learning
    JEL: E44 G12 C63
    Date: 2020–11
  7. By: Seyed Mohammad Sina Seyfi; Azin Sharifi; Hamidreza Arian
    Abstract: Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that the Gmm-based VaR model is computationally efficient and accurate. From a managerial perspective, our model can efficiently mimic the turbulent behavior of the market. As a result, our VaR measures before, during and after crisis periods realistically reflect the highly non-normal behavior and non-linear correlation structure of the market.
    Date: 2020–11
  8. By: Muhammad Farooq AHMAD (SKEMA Business School – Université Côte d’Azur, Lille, France); Oskar KOWALEWSKI (IESEG School of Management, Paris, France; LEM-CNRS 9221, Lille, France; Institute of Economics, Polish Academy of Sciences, Warsaw, Poland); Pawel PISANY (Institute of Economics, Polish Academy of Sciences, Warsaw, Poland)
    Abstract: We investigate the determinants of ICO campaigns' presence and success using data on 503 initial coin offerings (ICOs) from 60 countries that took place between 2015 and 2018. We took individual project perspective and country-wide perspective into account. Our findings show that expert ratings, insider retention, and resource-related signals, such as the number of team members and advisors, contribute positively to ICO funding success and post-ICO activity. Conversely, organizing presale and bonuses contribute negatively. Moreover, we established that countries' financial system development and ICO-related legal certainty boost the crypto-market. More importantly, we also document that countries' cultures foster ICO market development.
    Keywords: Initial Coin Offering, corporate finance, innovation, entrepreneurship
    JEL: G10 L26 M13 O30
    Date: 2020–11
  9. By: Andreas A. Aigner; Walter Schrabmair
    Abstract: How do you value companies which have IPOed recently? How do you compare them amongst their peers? Valuing companies using a linear extrapolation of their revenues and profits leads to an ingenious method to benchmark stocks against each other. Here we present such a method, dubbed the growth average U1.
    Date: 2020–11
  10. By: Ao Kong; Robert Azencott; Hongliang Zhu
    Abstract: This paper extends the work of Boudt and Pertitjean(2014) and investigates the trading patterns before price jumps in the stock market based on a new multivariate time classification technique. Different from Boudt and Pertitjean(2014), our analyzing scheme can explore the "time-series information" embedded in the trading-related attributes and provides a set of jump indicators for abnormal pattern recognition. In addition to the commonly used liquidity measures, our analysis also involves a set of technical indicators to describe the micro-trading behaviors. An empirical study is conducted on the level-2 data of the constituent stocks of China Security Index 300. It is found that among all the candidate attributes, several volume and volatility-related attributes exhibit the most significant abnormality before price jumps. Though some of the abnormalities start just shortly before the occurrence of the jumps, some start much earlier. We also find that most of our attributes have low mutual dependencies with each other from the perspective of time-series analysis, which allows various perspectives to study the market trading behaviors. To this end, our experiment provides a set of jump indicators that can effectively detect the stocks with extremely abnormal trading behaviors before price jumps. More importantly, our study offers a new framework and potential useful directions for trading-related pattern recognition problem using the time series classification techniques.
    Date: 2020–11
  11. By: Magdalena Tywoniuk (Swiss Finance Institute, Students; University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute, Students)
    Abstract: Following the 2008 financial crisis, regulation mandates the clearing of the CDS market through Central Clearing Counter-parties (CCPs). Large CCPs are now designated as ’Global Systemically Important Institutions’ (GSIIs), whose unlikely-but-plausible failure threatens global financial market stability. This work examines CCP resilience following a large dealer member’s default and the ensuing default contagion. In unwinding the defaulter’s positions, the CCP faces the price impact of constrained member liquidations and unconstrained members’ predatory selling. The variation margin captures the effect of price-mediated contagion and its amplification. A novel spatial measure captures the covariance between members’ CDS holdings and the CDS being unwound. Key results show: Liquidations by constrained members lower the CCP’s profits and make cds-spreads less informative. There exists a strong conflict between predatory competition and dealer distress, which inadvertently makes dealers prey on themselves. In turn, the adoption of a risk-sharing guarantee fund structure would provide a natural disciplinary mechanism for predation – minimizing overall CCP and member losses. A dynamic simulation, calibrated to OTC market data, supports these theoretical results with parameter magnitudes and sensitivities. Examination of three market liquidity scenarios provides intuition for effective liquidity injection by a Lender of Last Resort.
    Keywords: Systemic Risk, CCP Recovery, CDS, CDS Spread Fire Sales, Liquidation, Predation, Price Impact, Contagion, Financial Network, Over the Counter Markets
    JEL: G00 G01 G02 G14 G10 G18 G20 G23 G33
    Date: 2020–11
  12. By: James Collin Harkrader; Michael Puglia
    Abstract: We explore the following question: does the trading activity of registered dealers on Treasury interdealer broker (IDB) platforms differ from that of principal trading firms (PTFs), and if so, how and to what effect on market liquidity? To do so, we use a novel dataset that combines Treasury cash transaction reports from FINRA’s Trade Reporting and Compliance Engine (TRACE) and publicly available limit order book data from BrokerTec. We find that trades conducted in a limit order book setting have high permanent price impact when a PTF is the passive party, playing the role of liquidity provider. Conversely, we find that dealer trades have higher price impact when the dealer is the aggressive party, playing the role of liquidity taker. Trades in which multiple firms (whether dealers or PTFs) participate on one or both sides, however, have relatively low price impact. We interpret these results in light of theoretical models suggesting that traders with only a “small†informational advantage prefer to use (passive) limit orders, while traders with a comparatively large informational advantage prefer to use (aggressive) market orders. We also analyze the events that occurred in Treasury markets in March 2020, during the onset of the COVID-19 pandemic.
    Keywords: Treasury markets; High frequency trading; Market microstructure; Price discovery; Price impact; PTFs; Dealers; TRACE; BrokerTec
    JEL: G12 G14 G32
    Date: 2020–11–16
  13. By: Nicola Borri (LUISS University); Giorgio Di Giorgio (LUISS University)
    Abstract: This paper studies the systemic risk contribution of a set of large publicly traded European banks. Over a sample covering the last twenty years and three different crises, we find that all banks in our sample significantly contribute to systemic risk. Moreover, larger banks and banks with a business model more exposed to trading and financial market volatility, contribute more. In the shorter sample characterized by the Covid-19 shock, sovereign default risks significantly affected the systemic risk contribution of all banks. However, the ECB announcement of the Pandemic Emergency Purchasing Programme restored calm in the European banking sector.
    Keywords: CoVaR, systemic risk, Covid-19, banking regulation
    JEL: G01 G18 G21 G38
    Date: 2020–10

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