nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒02‒10
thirteen papers chosen by

  1. How Does Information Affect Liquidity in Over-the-Counter Markets? By Michael Junho Lee; Antoine Martin
  2. Defining an intrinsic "stickiness" parameter of stock price returns By Naji Massad; Jørgen Vitting Andersen
  3. Sharpe Ratio in High Dimensions: Cases of Maximum Out of Sample, Constrained Maximum, and Optimal Portfolio Choice By Mehmet Caner; Marcelo Medeiros; Gabriel Vasconcelos
  4. A Mixed Frequency Approach for Stock Returns and Valuation Ratios. By Theologos Dergiades; Costas Milas; Theodore Panagiotidis
  5. Choosing the Right Return Distribution and the Excess Volatility Puzzle By Abootaleb Shirvani; Frank J. Fabozzi
  6. Does Index Arbitrage Distort the Market Reaction to Shocks? By Stanislav Anatolyev; Sergei Seleznev; Veronika Selezneva
  7. Refined model of the covariance/correlation matrix between securities By Sebastien Valeyre
  8. Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning By Hans Buehler; Lukas Gonon; Josef Teichmann; Ben Wood; Baranidharan Mohan; Jonathan Kochems
  9. The price of haircuts: private and official default By Silvia Marchesi; Tania Masi
  10. Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries By Sidra Mehtab; Jaydip Sen
  11. Winners and losers from Sovereign debt inflows: evidence from the stock market By Fernando Broner; Alberto Martin; Lorenzo Pandolfi; Tomas Williams
  12. The other side of the Coin: Risks of the Libra Blockchain By Louis Abraham; Dominique Guegan
  13. Dynamic Connectedness And Spillovers Across Sectors: Evidence From The Indian Stock Market By Ioannis Chatziantoniou; David Gabauer; Hardik A. Marfatia

  1. By: Michael Junho Lee; Antoine Martin
    Abstract: A large volume of financial transactions occur in decentralized markets that commonly depend on a network of dealers. Dealers face two impediments to providing liquidity in these markets. First, dealers may face informed traders. Second, they may face costs associated with maintaining large balance sheets, either due to inventory or liquidity costs. In a recent paper, we study a model of over-the-counter (OTC) markets in which liquidity is endogenously determined by dealers who must contend with both asymmetric information and liquidity costs. This post provides an intuitive explanation of our model and the dynamics of interdealer liquidity.
    Keywords: Liquidity; information; inter-dealer
    JEL: G1 G14
    Date: 2020–01–13
  2. By: Naji Massad (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Jørgen Vitting Andersen (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique)
    Abstract: We introduce a non-linear pricing model of individual stock returns that defines a "stickiness" parameter of the returns. The pricing model resembles the capital asset pricing model (CAPM) used in finance but has a non-linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding "stress" to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the index's price movements, can then be used to define their "stickiness".
    Keywords: non-linear CAPM,stickiness of stock returns
    Date: 2019–10
  3. By: Mehmet Caner; Marcelo Medeiros; Gabriel Vasconcelos
    Abstract: In this paper, we analyze maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. One obstacle in this large dimensional setup is the singularity of the sample covariance matrix of the excess asset returns. To solve this issue, we benefit from a technique called nodewise regression, which was developed by Meinshausen and Buhlmann (2006). It provides a sparse/weakly sparse and consistent estimate of the precision matrix, using the Lasso method. We analyze three issues. One of the key results in our paper is that mean-variance efficiency for the portfolios in large dimensions is established. Then tied to that result, we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets. Furthermore, we provide convergence rates and see that the number of assets slow down the convergence up to a logarithmic factor. Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio. Finally, we provide consistent estimates of the Sharpe ratios of global minimum variance portfolio and Markowitz's (1952) mean variance portfolio. In terms of assumptions, we allow for time series data. Simulation and out-of-sample forecasting exercise shows that our new method performs well compared to factor and shrinkage based techniques.
    Date: 2020–02
  4. By: Theologos Dergiades (Department of International & European Studies, University of Macedonia); Costas Milas (Liverpool University); Theodore Panagiotidis (Department of Economics, University of Macedonia)
    Abstract: We employ a Mixed-Frequency VAR to study the effect of four valuation ratios (the price-dividend ratio, the price-earnings ratio, the Cyclically Adjusted Price Earnings Ratio and the Total Return Cyclically Adjusted Price Earnings Ratio) on the US stock market. We quantify the interaction between high and low frequency data. We show that all valuation ratios (observed at a monthly frequency) significantly affect stock market returns (observed at a daily frequency) at both long and short horizons.
    Keywords: Stock Index Returns; Valuation Ratios; MF-VAR; Impulse Response Analysis.
    JEL: G1 C12 C13
    Date: 2019–11
  5. By: Abootaleb Shirvani; Frank J. Fabozzi
    Abstract: Proponents of behavioral finance have identified several "puzzles" in the market that are inconsistent with rational finance theory. One such puzzle is the "excess volatility puzzle". Changes in equity prices are too large given changes in the fundamentals that are expected to change equity prices. In this paper, we offer a resolution to the excess volatility puzzle within the context of rational finance. We empirically show that market inefficiency attributable to the volatility of excess return across time is caused by fitting an improper distribution to the historical returns. Our results indicate that the variation of gross excess returns is attributable to poorly fitting the tail of the return distribution and that the puzzle disappears by employing a more appropriate distribution for the return data. The new distribution that we introduce in this paper that better fits the historical return distribution of stocks explains the excess volatility in the market and thereby explains the volatility puzzle. Failing to estimate the historical returns using the proper distribution is only one possible explanation for the existence of the volatility puzzle. However, it offers statistical models within the rational finance framework which can be used without relying on behavioral finance assumptions when searching for an explanation for the volatility puzzle.
    Date: 2020–01
  6. By: Stanislav Anatolyev; Sergei Seleznev; Veronika Selezneva
    Abstract: We show that ETF arbitrage distorts the market reaction to fundamental shocks. We confirm this hypothesis by creating a new measure of the intensity of arbitrage transactions at the individual stock level and using an event study analysis to estimate the market reaction to economic shocks. Our measure of the intensity of arbitrage is the probability of simultaneous trading of ETF shares with shares of underlying stocks estimated using high frequency data. Our approach is direct, and it accounts for statistical arbitrage, passive investment strategies, and netting of arbitrage positions over the day, which the existing measures cannot do. We conduct several empirical tests, including the use of a quasi-natural experiment, to confirm that our measure captures uctuations in the intensity of arbitrage transactions. We focus on oil shocks because they contain a large idiosyncratic component which facilitates identication of our mechanism and interpretation of the results. Oil shocks are identified using weekly oil inventory announcements.
    Keywords: high-frequency data; stock market; ETF; arbitrage intensity; oil shock; market efficiency;
    JEL: G12 G14 G23 Q43
    Date: 2019–12
  7. By: Sebastien Valeyre
    Abstract: A new methodology has been introduced to clean the correlation matrix of single stocks returns based on a constrained principal component analysis using financial data. Portfolios were introduced, namely "Fundamental Maximum Variance Portfolios", to capture in an optimal way the risks defined by financial criteria ("Book", "Capitalization", etc.). The constrained eigenvectors of the correlation matrix, which are the linear combination of these portfolios, are then analyzed. Thanks to this methodology, several stylized patterns of the matrix were identified: i) the increase of the first eigenvalue with a time scale from 1 minute to several months seems to follow the same law for all the significant eigenvalues with 2 regimes; ii) a universal law seems to govern the weights of all the "Maximum variance" portfolios, so according to that law, the optimal weights should be proportional to the ranking based on the financial studied criteria; iii) the volatility of the volatility of the "Maximum Variance" portfolios, which are not orthogonal, could be enough to explain a large part of the diffusion of the correlation matrix; iv) the leverage effect (increase of the first eigenvalue with the decline of the stock market) occurs only for the first mode and cannot be generalized for other factors of risk. The leverage effect on the beta, which is the sensitivity of stocks with the market mode, makes variable the weights of the first eigenvector.
    Date: 2020–01
  8. By: Hans Buehler (JP Morgan); Lukas Gonon (ETH Zurich); Josef Teichmann (ETH Zurich; Swiss Finance Institute); Ben Wood (JP Morgan Chase); Baranidharan Mohan (JP Morgan); Jonathan Kochems (JP Morgan)
    Abstract: This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work, here using notation more common in the machine learning literature. The objective is to maximize a non-linear risk-adjusted return function by trading in liquid hedging instruments such as equities or listed options. The approach presented here is the first efficient and model-independent algorithm which can be used for such problems at scale.
    Keywords: Reinforcement Learning, Imperfect Hedging, Derivatives Pricing, Derivatives Hedging, Deep Learning
    JEL: C61 C58
    Date: 2019–05
  9. By: Silvia Marchesi (University of Milan Bicocca); Tania Masi (University of Milan Bicocca)
    Abstract: This paper studies the relationship between sovereign debt default and sovereign credit risk by taking into account the depth of a debt restructuring and by distinguishing between commercial and officialdebt. We take rating agencies as well as bond yield spreads(EMBIG)as measures of a countrys creditworthiness. Our results show that defaults with private creditors seem to involve some reputational costs up to seven years since the last agreement,while offcial defaulters may even benefit from the restructuring episodes.Therefore,we find evidence that official and private defaults may have different costs and then induces elective defaults.
    Date: 2020–02–06
  10. By: Sidra Mehtab; Jaydip Sen
    Abstract: Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four years, from January 2015 till December 2019. Based on the NIFTY data during the said period, we build various predictive models using machine learning approaches, and then use those models to predict the Close value of NIFTY 50 for the year 2019, with a forecast horizon of one week. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual Close values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.
    Date: 2020–01
  11. By: Fernando Broner; Alberto Martin; Lorenzo Pandolfi; Tomas Williams
    Abstract: This paper analyzes the effects on firms of sovereign debt inflows in emerging countries. To deal with the endogeneity between capital inflows and economic activity, we focus on capital inflows driven by countries’ inclusions into well-known local currency sovereign debt market indexes. These events convey little information about the future economic prospects of countries but induce large capital flows from institutional investors tracking the indexes. We show that inclusion-driven flows significantly reduce government bond yields and appreciate the domestic currency. In turn, these flows have heterogenous impact on firms’ stock market returns. Government related firms, financial firms and firms with larger financial constraints experience positive abnormal returns following the announcement of these events. Instead, companies operating in export-intensive sectors have negative abnormal returns. Our findings shed novel light on the channels through which capital inflows to sovereign debt markets affect firms in the economy.
    Keywords: Sovereign debt; capital inflows; exchange rate; government bond yields; external financial dependence
    JEL: F31 F32 F36 G15 G23
    Date: 2019–12
  12. By: Louis Abraham (X - École polytechnique); Dominique Guegan (UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy])
    Abstract: Libra was presented as a cryptocurrency on June 18, 2019 by Facebook. On the same day, Facebook announced plans for Calibra, a subsidiary in charge of the development of an electronic wallet and financial services. In view of the primary risk of sovereignty posed by the creation of Libra, the Central Banks quickly took very clear positions against the project and adressed a lot of questions to the responsible of the project focusing on regulation aspects and national sovereignty. The purpose of this paper is to provide a holistic analysis of the project to encompass several aspects of its implementation and the issues it raises. We address a set of questions that are part of the cryptocurrency environment and blockchain technology that supports the Libra project. We identify the main risks considering at the same time: political risk, financial risks, economical risks, technological risks and ethics focusing on the governance of the project based on two levels: one for the Association and the other for the Libra Blockchain. We emphazise the difficulty to regulate such a project as soon as it will depend on several countries whose legislations are very different. The future of this kind of project is discussed through the emergence of the Central Bank Digital Currencies.
    Keywords: Risk,Regulation,Libra,Blockchain Protocol,Centrel Bank Digital Currency,Cryptocurrency,Governance
    Date: 2019–10
  13. By: Ioannis Chatziantoniou (Portsmouth Business School); David Gabauer (Johannes Kepler University); Hardik A. Marfatia (Northeastern Illinois University)
    Abstract: This paper explores stock market sectoral connectedness for the emerging market economy of India. We use the time-varying parameter vector autoregressive dynamic connectedness of Antonakakis and Gabauer (2017). Results show that the stock market sectoral connectedness varies across time. Connectedness is strongest among sectors during the 2008 crisis, the double-digit inflation and stock market crash of 2011, national elections of 2014, and the historic demonetization of 2016. In addition, consumers’ spending, industry, finance, and basic materials appear to be net transmitters of shocks. By contrast, information technology, fast moving consumer goods, healthcare, and telecommunications are net receivers of shocks. This paper can help formulate policies aiming at alleviating sectoral imbalances and promoting balanced growth, and also benefit investors with devising optimal portfolio diversification strategies.
    Keywords: Emerging Markets, Sectoral Spillover, Variance Decomposition, Dynamic Connectedness, Stock Market Returns, TVP-VAR
    JEL: C32 C50 G15
    Date: 2020–01–28

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