nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒05‒06
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Towards a Better Financial System By Admati, Anat R.
  2. Asset Price Bubbles and Systemic Risk By Markus K. Brunnermeier; Simon C. Rother; Isabel Schnabel
  3. The dynamics of households' stock market beliefs By Hans-Martin von Gaudecker; Axel Wogrolly
  4. A Dynamic Model of Characteristic-Based Return Predictability By Aydoğan Alti; Sheridan Titman
  5. Rough volatility of Bitcoin By Tetsuya Takaishi
  6. Gated deep neural networks for implied volatility surfaces By Yu Zheng; Yongxin Yang; Bowei Chen
  7. Tick size change and market quality in the U.S. treasury market By Fleming, Michael J.; Nguyen, Giang; Ruela, Francisco
  8. Movements in International Bond Markets: The Role of Oil Prices By Saban Nazlioglu; Rangan Gupta; Elie Bouri
  9. Mapping bank securities across euro area sectors: comparing funding and exposure networks By Hüser, Anne-Caroline; Kok, Christoffer
  10. Pricing and hedging of VIX options for Barndorff-Nielsen and Shephard models By Takuji Arai

  1. By: Admati, Anat R. (Graduate School of Business, Stanford University)
    Abstract: A healthy and stable financial system enables efficient resource allocation and risk sharing. A reckless and distorted system, however, causes enormous harm. The cycles of boom, bust, and crisis that repeatedly plague banking and finance are symptoms of deep governance and policy failures. Reinhart and Rogoff (2009), who studied financial crises over many years and jurisdictions, conclude that crises are preventable but that governments are themselves part of the problem, either because they mishandle their own finances and borrow too much, or they fail to prevent recklessness by households and firms.
    Date: 2019–01
  2. By: Markus K. Brunnermeier; Simon C. Rother; Isabel Schnabel
    Abstract: We analyze the relationship between asset price bubbles and systemic risk, using bank-level data covering almost thirty years. Systemic risk of banks rises already during a bubble’s build-up phase, and even more so during its bust. The increase differs strongly across banks and bubble episodes. It depends on bank characteristics (especially bank size) and bubble characteristics, and it can become very large: In a median real estate bust, systemic risk increases by almost 70 percent of the median for banks with unfavorable characteristics. These results emphasize the importance of bank-level factors for the build-up of financial fragility during bubble episodes.
    JEL: E44 G01
    Date: 2019–04
  3. By: Hans-Martin von Gaudecker; Axel Wogrolly
    Abstract: We analyse a long panel of households’ stock market beliefs to gain insights into the nature of their expectations formation processes. We classify respondents into one of five groups based on their data and estimate group-wise models of expectations formation. Two of the groups are at opposite extremes in terms of optimism: Pessimists who expect substantially negative returns and financially sophisticated individuals whose expectations are close to the historical average. Two groups expect returns around zero and differ only in how they respond to information: Extrapolators who become more optimistic following positive information and mean-reverters for whom the opposite is the case. The final group is characterised by poor probability numeracy; its individuals are not willing or able to quantify their beliefs about future returns. None of the estimated belief formation processes passes a rational expectations test.
    Keywords: stock market expectations, household finance, heterogeneity, clustering methods
    JEL: D83 D84 D14 C38
    Date: 2019
  4. By: Aydoğan Alti; Sheridan Titman
    Abstract: We present a dynamic model that links characteristic-based return predictability to systematic factors that determine the evolution of firm fundamentals. In the model, an economy-wide disruption process reallocates profits from existing businesses to new projects and thus generates a source of systematic risk for portfolios of firms sorted on value, profitability, and asset growth. If investors are overconfident about their ability to evaluate the disruption climate, these characteristic-sorted portfolios exhibit persistent mispricing. The model generates predictions about the conditional predictability of characteristic-sorted portfolio returns and illustrates how return persistence increases the likelihood of observing characteristic-based anomalies.
    JEL: G02 G12
    Date: 2019–04
  5. By: Tetsuya Takaishi
    Abstract: Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than $1/2$, which indicates log-volatility increments that are rough. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly comes from the distributional property.
    Date: 2019–04
  6. By: Yu Zheng; Yongxin Yang; Bowei Chen
    Abstract: In this paper, we propose a gated deep neural network model to predict implied volatility surfaces. Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture design and calibration including no static arbitrage, boundaries, asymptotic slope and volatility smile. They are also satisfied empirically by the option data on the S&P 500 over a ten years period. Our proposed model outperforms the widely used surface stochastic volatility inspired model on the mean average percentage error in both in-sample and out-of-sample datasets. The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a framework of integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields.
    Date: 2019–04
  7. By: Fleming, Michael J. (Federal Reserve Bank of New York); Nguyen, Giang (Pennsylvania State University); Ruela, Francisco (Federal Reserve Bank of New York)
    Abstract: This paper studies a recent tick size reduction in the U.S. Treasury securities market and identifies its effects on the market’s liquidity and price efficiency. Employing difference-indifference regressions, we find that the bid-ask spread narrows significantly after the change, even for large trades, and that trading volume increases. Market depth declines markedly at the inside tier and across the book, but cumulative depth close to the top of the book changes little or even increases slightly. Furthermore, the smaller tick size enables prices to adjust more easily to information and better reflect true value, resulting in greater price efficiency. Price informativeness remains largely similar before and after, suggesting that the reduction in trading costs does not result in increased information acquisition. However, there is clear evidence of an information shift from the futures market toward the smaller-tick-size cash market. Overall, we conclude that the tick size reduction improves market quality.
    Keywords: tick size reduction; bid-ask spread; market liquidity; price efficiency; Treasury securities
    JEL: D14 G01 G12 G18
    Date: 2019–04–01
  8. By: Saban Nazlioglu (Department of International Trade and Finance, Faculty of Economics and Administrative Sciences, Pamukkale University, Denizli, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)
    Abstract: In this paper, we analyze daily data-based price transmission and volatility spillovers between crude oil and bond markets of major oil exporters and importers, by accounting for structural shifts as a smooth process in causality and volatility spillover estimations. In general, we find that, oil prices tend to predict bond prices in majority of oil exporting countries, and for the two major oil importers of India and China. But, the feedback from bond to oil prices is weak, and is detected for China and USA. Regarding volatility spillovers, oil volatility affects the bond market volatility of some major oil exporters (Kuwait, Norway and Russia), and an importer (France). However, the most prominent volatility spillovers are from bond to oil, except for Kuwait and Saudi Arabia. We also reveal that taking into account for smooth structural shifts - accounting for structural breaks - strengthens our findings and particularly is important for volatility spillover analysis. Our results have important implications for academics, investors, and policy makers.
    Keywords: Bond and oil markets, price and volatility spillovers, major oil exporters and importers, structural changes
    JEL: C32 G12 Q02
    Date: 2019–04
  9. By: Hüser, Anne-Caroline; Kok, Christoffer
    Abstract: We present new evidence on the structure of euro area securities markets using a multilayer network approach. Layers are broken down by key instruments and maturities as well as the secured nature of the transaction. This paper utilizes a unique dataset of banking sector crossholdings of securities to map these exposures among banks and economic and financial sectors. We can compare and contrast funding and exposure networks among banks themselves and of banks, non-banks and the wider economy. The analytical approach presented here is highly relevant for the design of appropriate prudential measures, since it supports the identification of counterparty risk, concentration risk and funding risk within the interbank network and the wider macro-financial network. JEL Classification: D85, E44, G21, L14
    Keywords: Interbank networks, macro-financial networks, macroprudential analysis., market microstructure, multilayer networks
    Date: 2019–04
  10. By: Takuji Arai
    Abstract: The VIX call options for the Barndorff-Nielsen and Shephard models will be discussed. Derivatives written on the VIX, which is the most popular volatility measurement, have been traded actively very much. In this paper, we give representations of the VIX call option price for the Barndorff-Nielsen and Shephard models: non-Gaussian Ornstein--Uhlenbeck type stochastic volatility models. Moreover, we provide representations of the locally risk-minimizing strategy constructed by a combination of the underlying riskless and risky assets. Remark that the representations obtained in this paper are efficient to develop a numerical method using the fast Fourier transform. Thus, numerical experiments will be implemented in the last section of this paper.
    Date: 2019–04

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