nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒05‒28
eight papers chosen by

  1. Why Has Idiosyncratic Risk Been Historically Low in Recent Years? By Bartram, Sohnke M.; Brown, Gregory W.; Stulz, Rene M.
  2. Capital Market Anomalies and Quantitative Research By Birru, Justin; Gokkaya, Sinan; Liu, Xi
  3. Estimating Latent Asset-Pricing Factors By Lettau, Martin; Pelger, Markus
  4. Network Sensitivity of Systemic Risk By Domenico Di Gangi; D. Ruggiero Lo Sardo; Valentina Macchiati; Tuan Pham Minh; Francesco Pinotti; Amanah Ramadiah; Mateusz Wilinski; Giulio Cimini
  5. Bitcoin Risk Modeling with Blockchain Graphs By Cuneyt Akcora; Matthew Dixon; Yulia Gel; Murat Kantarcioglu
  6. Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings By Rangan Gupta; Patrick Kanda; Mark E. Wohar
  7. Channels of US monetary policy spillovers to international bond markets By Elias Albagli; Luis Ceballos; Sebastián Claro; Damian Romero
  8. Multiplex network analysis of the UK OTC derivatives market By Bardosci, Marco; Bianconi, Ginestra; Ferrara, Gerardo

  1. By: Bartram, Sohnke M. (University of Warwick); Brown, Gregory W. (University of North Carolina); Stulz, Rene M. (Ohio State University)
    Abstract: Since 1965, average idiosyncratic risk (IR) has never been lower than in recent years. In contrast to the high IR in the late 1990s that has drawn considerable attention in the literature, average market-model IR is 44% lower in 2013-2017 than in 1996-2000. Macroeconomic variables help explain why IR is lower, but using only macroeconomic variables leads to large prediction errors compared to using only firm-level variables. As a result of the dramatic change in the number and composition of listed firms since the late 1990s, listed firms are larger and older. Larger and older firms have lower idiosyncratic risk. Models that use firm characteristics to predict firm-level idiosyncratic risk estimated over 1963-2012 can largely or completely explain why IR is low over 2013-2017. The same changes that bring about historically low IR lead to unusually high market-model R-squareds.
    JEL: G10 G11 G12
    Date: 2018–01
  2. By: Birru, Justin (Ohio State University); Gokkaya, Sinan (Ohio University); Liu, Xi (Miami University of Ohio)
    Abstract: Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. Quant-backed analysts exhibit more efficient forecasting behavior on anomaly predictors--stock recommendations and target prices issued on anomaly-longs (anomaly-shorts) are more (less) favorable. Investment value of such analysts' research is higher and their research reports are more likely to discuss implications of quantitative modeling and market anomalies. Quant research facilitates "smart money" trades of institutional clients on anomaly stocks--Quant research is associated with an increased (decreased) likelihood of purchasing underpriced (overpriced) stocks. Market participants recognize Quants--thematic reports authored by Quants generate abnormal reactions for corresponding stocks. Finally, we provide evidence consistent with quantitative research increasing market efficiency by attenuating cross-sectional predictability of anomaly based long-short strategies.
    JEL: G00 G11 G14 G23 G24
    Date: 2018–03
  3. By: Lettau, Martin; Pelger, Markus
    Abstract: We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with significantly smaller pricing errors.
    Keywords: Anomalies; Cross Section of Returns; expected returns; high-dimensional data; Latent Factors; PCA; Weak Factors
    JEL: C14 C38 C52 C58 G12
    Date: 2018–05
  4. By: Domenico Di Gangi; D. Ruggiero Lo Sardo; Valentina Macchiati; Tuan Pham Minh; Francesco Pinotti; Amanah Ramadiah; Mateusz Wilinski; Giulio Cimini
    Abstract: The recent stream of literature of systemic risk in financial markets emphasized the key importance of considering the complex interconnections among financial institutions. Much efforts has been put to model the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets---either using real data, reconstruction techniques or simple toy networks. Here we address the more general problem of how the shock propagation dynamics depends on the topological details of the underlying network. To this end, we consider different network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks with varying density and mesoscale structures, and vary as well the details of the shock propagation dynamics. We show that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can thus aid in the design of regulatory policies to improve the robustness of financial markets.
    Date: 2018–05
  5. By: Cuneyt Akcora; Matthew Dixon; Yulia Gel; Murat Kantarcioglu
    Abstract: A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.
    Date: 2018–05
  6. By: Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Patrick Kanda (Laboratoire THéorie Économique, Modélisation et Applications (THEMA), Université de Cergy-Pontoise, France); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire, UK)
    Abstract: In this paper we analyze whether presidential approval ratings can predict the S&P500 returns over the monthly period of 1941:07 to 2018:04, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from presidential approval ratings to stock returns cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 69 percent of the sample period, approval ratings in fact do strongly predict the S&P500 stock return. Moreover, using the DCC-MGARCH model we find that presidential approval rating is also a strong predictor of the realized volatility of S&P500. Overall, our results highlight that presidential approval ratings is helpful in predicting stock return and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.
    Keywords: US Presidential Approval Ratings, DCC-MGARCH, Stock Returns, Realized Volatility, S&P500
    JEL: C32 G10
    Date: 2018–05
  7. By: Elias Albagli; Luis Ceballos; Sebastián Claro; Damian Romero
    Abstract: We document significant US monetary policy (MP) spillovers to international bond markets. Our methodology identifies US MP shocks as the change in short-term treasury yields within a narrow window around FOMC meetings, and traces their effects on international bond yields using panel regressions. We emphasize three main results. First, US MP spillovers to long-term yields have increased substantially after the global financial crisis. Second, spillovers are large compared to the effects of other events, and at least as large as the effects of domestic MP after 2008. Third, spillovers work through different channels, concentrated in risk neutral rates (expectations of future MP rates) for developed countries, but predominantly on term premia in emerging markets. In interpreting these findings, we provide evidence consistent with an exchange rate channel, according to which foreign central banks face a tradeoff between narrowing MP rate differentials, or experiencing currency movements against the US dollar. Developed countries adjust in a manner consistent with freely floating regimes, responding partially with risk neutral rates, and partially through currency adjustments. Emerging countries display patterns consistent with FX interventions, which cushion the response of exchange rates but reinforce capital flows and their effects in bond yields through movements in term premia. Our results suggest that the endogenous effects of FXI on long-term yields should be added into the standard cost-benefit analysis of such policies.
    Keywords: monetary policy spillovers, risk neutral rates, term premia
    JEL: E43 G12 G15
    Date: 2018–05
  8. By: Bardosci, Marco (Bank of England); Bianconi, Ginestra (School of Mathematical Sciences, Queen Mary University of London); Ferrara, Gerardo (Bank of England)
    Abstract: In this paper, we analyse the network of exposures constructed by using the UK trade repository data for three different categories of contracts: interest rate, credit, and foreign exchange derivatives. We study how liquidity shocks related to variation margins propagate across the network and translate into payment deficiencies. A key finding of the paper is that, in extreme theoretical scenarios where liquidity buffers are small, a handful of institutions may experience significant spillover effects due to the directionality of their portfolios. Additionally, we show that a variant of a recently introduced centrality measure — Functional Multiplex PageRank — can be used as a proxy of the vulnerability of financial institutions, outperforming in this respect the commonly used eigenvector centrality.
    Keywords: Central counterparty (CCP); liquidity shock; multiplex networks; systemic risk; financial networks
    JEL: D85 G01 G17 L14
    Date: 2018–05–18

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