nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒02‒11
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Effect of Global Crises on Stock Market Correlations: Evidence from Scalar Regressions via Functional Data Analysis By Sonali Das; Riza Demirer; Rangan Gupta; Siphumlile Mangisa
  2. Time-Varying Risk Aversion and the Predictability of Bond Premia By Oğuzhan Çepni; Riza Demirer; Rangan Gupta; Christian Pierdzioch
  3. Forecasting Volatility with Time-Varying Leverage and Volatility of Volatility Effects By Leopoldo Catania; Tommaso Proietti
  4. Limitations of stabilizing effects of fundamentalists: Facing positive feedback traders By Baumann, Michael Heinrich; Baumann, Michaela; Erler, Alexander
  5. Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach By Faisal I Qureshi
  6. The impact of the Fundamental Review of the Trading Book: A preliminary assessment on a stylized portfolio By RChiara Pederzoli; Costanza Torricelli
  7. Uncertainty and the Cost of Bank vs. Bond Finance By Christian Grimme
  8. Blockchain, FinTechs and their relevance for international financial institutions By Davradakis, Emmanouil; Santos, Ricardo
  9. Nonresident Capital Flows and Volatility: Evidence from Malaysia’s Local Currency Bond Market By David A. Grigorian
  10. A Study on Neural Network Architecture Applied to the Prediction of Brazilian Stock Returns By Leonardo Felizardo; Afonso Pinto

  1. By: Sonali Das (Advanced Mathematical Modelling, Modelling and Digital Science, Council for Scientific and Industrial Research, Pretoria, South Africa and Department of Statistics, Nelson Mandela University, Port Elizabeth, South Africa); Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Siphumlile Mangisa (Department of Statistics, Nelson Mandela University, Port Elizabeth, South Africa)
    Abstract: This paper presents a novel, mixed-frequency based regression approach, derived from Functional Data Analysis (FDA), to analyze the effect of global crises on stock market correlations, using a long span of data, dating as far back as late 1800s, thus covering a wide range of global crises that have not yet been examined in the literature in this context. Focusing on the advanced nations in the G7 group, we observe heterogeneous effects of global crises on the time-varying correlations between the US stock market and its counterparts in the G7. While the post World War II period experienced a general rise in the level of correlations among developed stock market returns, we find that global crises in general have resulted in a stronger association of US stock market performance with that in the UK and Canada, whereas the opposite holds when it comes to how European and Japanese stock markets co-move with the US. Further analysis of sub-periods, however, reveals that the crises effect over stock market correlations is largely driven by the context and nature of the crises that possibly drive the perception of risk in financial markets. Overall, our results tend to suggest that in the wake of crises that are global in nature, diversification benefits will be limited by moving funds across the US and UK stock markets whereas possible diversification benefits would have been possible during the crises-ridden period of the early twentieth century by holding positions in equities in the remaining G7 nations to supplement positions in the US. However, these diversification benefits seem to have frittered away in the post World War II period, highlighting the role of emerging markets and alternative assets to improve diversification benefits in the modern era.
    Keywords: Functional data analysis, global crises, stock markets, comovements, G7
    JEL: C22 G01 G15
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:201908&r=all
  2. By: Oğuzhan Çepni (Central Bank of the Republic of Turkey, Anafartalar Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B.700822, 22008 Hamburg, Germany)
    Abstract: We show that time-varying risk aversion captures significant predictive information over excess returns on U.S. government bonds even after controlling for a large number of financial and macro factors. Including risk aversion improves the predictive accuracy at all horizons (one- to twelve-months ahead) for shorter maturity bonds and at shorter forecast horizons (one- to three-months ahead) for longer maturity bonds. Given the role of Treasury securities in economic forecasting models and portfolio allocation decisions, our findings have significant implications for investors, policy makers and researchers interested in accurately forecasting return dynamics for these assets.
    Keywords: Bond premia, Predictability, Risk aversion, Out-of-sample forecasts
    JEL: C22 C53 G12 G17
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:201906&r=all
  3. By: Leopoldo Catania (Aarhus University and CREATES); Tommaso Proietti (CEIS & DEF, University of Rome "Tor Vergata")
    Abstract: The prediction of volatility is of primary importance for business applications in risk management, asset allocation and pricing of derivative instruments. This paper proposes a novel measurement model which takes into consideration the possibly time-varying interaction of realized volatility and asset returns, according to a bivariate model aiming at capturing the main stylised facts: (i) the long memory of the volatility process, (ii) the heavy-tailedness of the returns distribution, and (iii) the negative dependence of volatility and daily market returns. We assess the relevance of "volatility in volatility"and time-varying "leverage" effects in the out-of-sample forecasting performance of the model, and evaluate the density forecasts of the future level of market volatility. The empirical results illustrate that our specification can outperform the benchmark HAR-RV, both in terms of point and density forecasts.
    Keywords: realized volatility, forecasting, leverage effect, volatility in volatility
    Date: 2019–02–06
    URL: http://d.repec.org/n?u=RePEc:rtv:ceisrp:450&r=all
  4. By: Baumann, Michael Heinrich; Baumann, Michaela; Erler, Alexander
    Abstract: The authors analyze financial interactions between fundamentalists and chartists within a heterogeneous agent model, focusing on the role of fundamentalists stabilizing prices. In contrast to related studies, which are based on simulations and calculations, they analytically prove that the presence of fundamentalists is not sufficient to avoid asset price bubbles. The behavior of trend followers with bounded leverage can result in exploding prices irrespective of fundamentalists' investment decisions. They derive upper boundaries for positive feedback traders' initial investment necessary to avoid exploding prices. In order to stabilize stock/asset markets, intervention measures might be helpful.
    Keywords: heterogeneous agents,feedback trading,fundamentalists,chartists,trend followers,financial bubbles,financial crisis
    JEL: D84 G01 G11
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:zbw:ifwedp:20193&r=all
  5. By: Faisal I Qureshi
    Abstract: With the proliferation of algorithmic high-frequency trading in financial markets, the Limit Order Book has generated increased research interest. Research is still at an early stage and there is much we do not understand about the dynamics of Limit Order Books. In this paper, we employ a machine learning approach to investigate Limit Order Book features and their potential to predict short term price movements. This is an initial broad-based investigation that results in some novel observations about LOB dynamics and identifies several promising directions for further research. Furthermore, we obtain prediction results that are significantly superior to a baseline predictor.
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.10534&r=all
  6. By: RChiara Pederzoli; Costanza Torricelli
    Abstract: The aim of this paper is to gauge the impact in terms of capital requirements of the Fundamental Review of the Trading Book (FRTB). To this end we take a stylized portfolio sensible to the risk factors mostly affected by the review and we implement the new regulation both under the Standard Approach (SA) and the Internal Model Approach (IMA). Our results provide an order of magnitude of the increase across the two regulations and the two approaches (SA and IMA), and disentangle the expected increase implied by the FRTB in its main effects both for the SA and IMA approach. Our analyses prove a very relevant increase especially under the SA and underscore possible implications of the review both in terms of regulamentary model’s choice and business strategies.
    Keywords: trading portfolio, VaR, Expected shortfall, bank regulation
    JEL: E30 E44 G01 G10 G28 E30 E44 G01 G10 G28
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:mod:wcefin:0075&r=all
  7. By: Christian Grimme
    Abstract: How does uncertainty affect the costs of raising finance in the bond market and via bank loans? Empirically, this paper finds that heightened uncertainty is accompanied by an increase in corporate bond yields and a decrease in bank lending rates. This finding can be explained with a model that includes costly state verification and a special informational role for banks. To reduce uncertainty, banks acquire additional costly information about borrowers. More information increases the value of the lending relationship and lowers the lending rate. Bond investors demand compensation for the increased risk of firm default.
    Keywords: uncertainty shocks, financial frictions, relationship banking, bank loan rate setting, information acquisition
    JEL: E32 E43 E44 G21
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_7456&r=all
  8. By: Davradakis, Emmanouil; Santos, Ricardo
    Abstract: The purpose of this working paper is to provide a primer on financial technology and on Blockchain, while shading light on the impact they may have on the financial industry. FinTechs, the financial technology and innovation that competes with traditional financial methods in the delivery of financial services, has the potential to improve the reach of financial services to the broader public and facilitate the creation of a credit record, especially in the developing world. Some Blockchain applications like cryptocurrencies, could be problematic as cryptocurrencies cannot substitute traditional money due to the high risk of debasement, luck of trust and high inefficiencies relating to the high cost in electricity and human effort required to clear cryptocurrency transactions. Cryptocurrencies' high volatility renders it a poor means of payment and store of value, while resembling a fraudulent investment operation. Yet, other Blockchain applications, like Blockchain securities, could facilitate the functioning of an International Financial Institutions (IFI) due to the volume of securities they issue as Blockchain securities enable an almost instantaneous trade confirmation, affirmation, allocation and settlement and reconciliations are superfluous releasing collateral to be used for other purposes in the market. IFIs could promote awareness and understanding about Blockchain technology among different IFI services and launch Blockchain labs in order to pilot projects that can improve governance and social outcomes in the developing world. Financial inclusion, at the core of IFI's mandate, could be enhanced by investing into FinTechs who facilitate access to payment systems. IFIs could also ponder the development of Blockchain software aimed at improving transparency and efficiency in public resources that finance development projects. IFIs could promote Blockchain applications in several sectors like agricultural lending where Blockchain technology is used in the supply chain in order to improve transparency and efficiency in agricultural and commodity production. Other sectors include transport and logistics and even energy distribution. IFIs could benefit by utilizing FinTechs' knowhow in the analysis of big data in order to understand better the investment gaps and the financing needs of prospective clients. Finally, FinTechs' knowhow could be used by IFIs in order to streamline their internal processes concerning credit underwriting and risk management.
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:zbw:eibwps:201901&r=all
  9. By: David A. Grigorian
    Abstract: Malaysia’s local currency debt market is one of the most liquid public debt markets in the world. In recent years, the growing share of nonresident holders of debt has been a source of concern for policymakers as a reason behind exchange rate volatility. The paper provides an overview of the recent developments in the conventional debt market. It builds an empirical two-stage model to estimate the main drivers of debt capital flows to Malaysia. Finally, it uses a GARCH model to test the hypothesis that nonresident flows are behind the observed exchange rate volatility. The results suggest that the public debt market in Malaysia responds adequately to both pull and push factors and find no firm evidence that nonresident flows cause volatility in the onshore foreign exchange market.
    Date: 2019–01–25
    URL: http://d.repec.org/n?u=RePEc:imf:imfwpa:19/23&r=all
  10. By: Leonardo Felizardo; Afonso Pinto
    Abstract: In this paper we present a statistical analysis about the characteristics that we intend to influence in the performance of the neural networks in terms of assertiveness in the prediction of Brazilian stock returns. We created a population of architectures for analysis and extracted the sample that had the best assertive performance. It was verified how the characteristics of this sample stand out and affect the neural networks. In addition, we make inferences about what kind of influence the different architectures have on the performance of neural networks. In the study, the prediction of the return of a Brazilian stock traded on the stock exchange of S\~ao Paulo to measure the error committed by the different architectures of constructed neural networks. The results are promising and indicate that some aspects of the neural network architecture have a significant impact on the assertiveness of the model.
    Date: 2019–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1901.09143&r=all

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