nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒12‒02
ten papers chosen by

  1. A Moving Average Heterogeneous Autoregressive Model for Forecasting the Realized Volatility of the US Stock Market: Evidence from Over a Century of Data By Afees A. Salisu; Rangan Gupta; Ahamuefula E. Ogbonna
  2. Credit Variance Risk Premiums By Manuel Ammann; Mathis Mörke
  3. Safe Asset Carry Trade By Benedikt Ballensiefen; Angelo Ranaldo
  4. Cyber bonds and their pricing models By Oleg Kolesnikov; Alexander Markov; Daulet Smagulov; Sergejs Solovjovs
  5. OTC discount By de Roure, Calebe; Mönch, Emanuel; Pelizzon, Loriana; Schneider, Michael
  6. Asymmetric Information, Dynamic Debt Issuance, and the Term Structure of Credit Spreads By Benzoni, Luca; Garlappi, Lorenzo; Goldstein, Robert S.
  7. Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War? By Vasilios Plakandaras; Elie Bouri; Rangan Gupta
  8. Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram By Riza Demirer; Rangan Gupta; Hossein Hassani; Xu Huang
  9. Mean Reversion in Asia-Pacific Stock Prices: New Evidence from Quantile Unit Root Tests By Gilbert V. Nartea; Harold Glenn A. Valera; Maria Luisa G. Valera
  10. Trade shocks, product mix adjustment and productivity growth in Italian manufacturing By Maria Gabriela Ladu; Andrea Linarello; Filippo Oropallo

  1. By: Afees A. Salisu (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Ahamuefula E. Ogbonna (Centre for Econometric & Allied Research, University of Ibadan and Department of Statistics, University of Ibadan)
    Abstract: This study forecasts the monthly realized volatility of the US stock market covering the period of February, 1885 to September, 2019 using a recently developed novel approach – a moving average heterogeneous autoregressive (MAT-HAR) model, which treats threshold as a moving average generated time varying parameter rather than as a fixed or unknown parameter. The significance of asymmetric information in realized volatility of stock market forecasting is also considered by examining the case of good and bad realized volatility. The Clark and West (2007) forecast evaluation approach is employed to evaluate the forecast performance of the proposed predictive model vis-à-vis the conventional HAR and threshold HAR (T-HAR) models. We find evidence in favour of the MAT-HAR model relative to the HAR and T-HAR models. Also observed is the significant role of asymmetry in modeling the realized volatility as good realized volatility and bad realized volatility yield dissimilar predictability results. Our results are not sensitive to the choice of sample periods and realized volatility measures.
    Keywords: Realized volatility, US stock market, Forecast evaluation, HAR models
    JEL: C22 C53 G12
    Date: 2019–11
  2. By: Manuel Ammann; Mathis Mörke
    Abstract: This paper studies variance risk premiums in the credit market. Using a novel data set of swaptions quotes on the CDX North America Investment Grade index, we find that returns of credit variance swaps are negative and economically large. Shorting variance swaps yields an annualized Sharpe ratio of almost six, eclipsing its counterpart in fixed income or equity markets. The returns remain highly statistically significant when accounting for transaction costs, cannot be explained by established risk-factors, and hold for various investment horizons. We also dissect the overall variance risk premium into payer and receiver variance risk premiums. We find that exposure to both parts is priced. However, the returns for payer variance, associated with bad economic states, are roughly twice as high in absolute terms.
    Keywords: Variance risk premium, CDS implied volatility, CDS variance swap
    JEL: G12 G13
    Date: 2019–06
  3. By: Benedikt Ballensiefen; Angelo Ranaldo
    Abstract: We provide an asset pricing analysis of one of the main categories of near-money or safe assets, the repurchase agreement (repo). Heterogeneity in repo rates allows for a remunerative carry trade. The return on this carry trade, our carry factor, together with a market factor explain the temporal and cross-sectional variation in repo rates within a no-arbitrage framework: While the market factor determines the level of short-term interest rates, the carry factor accounts for the cross-sectional dispersion. Consistent with the safe asset literature, the carry factor reflects heterogeneity in convenience premia and is explained by the safety premium, the liquidity premium, and the opportunity cost of holding money.
    Keywords: Safe Asset, Near-Money Asset, Repo, Carry Trade, Asset Pricing, Short-Term Interest Rates, Convenience Premium
    JEL: E40 E41 G00 G01 G10 G11
    Date: 2019–07
  4. By: Oleg Kolesnikov; Alexander Markov; Daulet Smagulov; Sergejs Solovjovs
    Abstract: Motivated by the developments in cyber risk treatment in the finance industry, we propose a general framework of cyber bond, whose main purpose is to insure (compensate) losses of a cyber attack. Based on a database of publicly available cyber events, we determine cyber loss distribution parameters and use them to numerically simulate cyber bond price, yield, and other characteristics. We also consider two possible approaches to cyber bond coupon calculation.
    Date: 2019–11
  5. By: de Roure, Calebe; Mönch, Emanuel; Pelizzon, Loriana; Schneider, Michael
    Abstract: We study price dispersion and venue choice in the interdealer market for German sovereign bonds, where an exchange and over-the-counter segments coexist. We show that 85% of OTC traded prices are favorable with respect to exchange quotes, indicating the prevalence of an OTC discount. This discount is sizeable and driven by both search and information frictions. More than 75% of volume is transacted via interdealer brokers in trades that are larger, have less price impact, and less discount than comparable bilateral OTC trades. Dealers trade on the exchange for immediacy, highlighting the complementary roles played by the different segments.
    Keywords: Market Microstructure,Hybrid Markets,Venue Choice,Interdealer Brokerage,Fixed-Income,OTC Markets,Intermediation Frictions,Search Frictions,Information Frictions
    JEL: D4 D47 G1 G14 G24
    Date: 2019
  6. By: Benzoni, Luca (Federal Reserve Bank of Chicago); Garlappi, Lorenzo (University of British Columbia, School of Business); Goldstein, Robert S. (University of Minnesota)
    Abstract: We propose a tractable model of a firm’s dynamic debt and equity issuance policies in the presence of asymmetric information. Because “investment-grade” firms can access debt markets, managers who observe a bad private signal can both conceal this information and shield shareholders from infusing capital into the firm by issuing new debt to service existing debt, thus avoiding default. The implication is that the “asymmetric information channel” can generate jumps to default (from the creditors’ perspective) only for those "high-yield" firms that have exhausted their ability to borrow. Thus, our model deepens the “credit spread puzzle” for investment-grade firms.
    Keywords: Credit spreads; Capital structure; Corporate Default; Debt; Jumps to Default; Investments
    JEL: G12 G32 G33
    Date: 2019–09–02
  7. By: Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, University Campus, Komotini, Greece); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)
    Abstract: Previous studies provide evidence that trade related uncertainty tends to predict an increase in Bitcoin returns. In this paper, we extend the related literature by examining whether the information on the U.S. – China trade war can be used to forecast the future path of Bitcoin returns controlling for various explanatory variables. We apply ordinary least square (OLS) regression, support vector regression (SVR), and the least absolute shrinkage and selection operator (LASSO) techniques that stem from the field of machine learning, and find weak evidence of the role of the trade war in forecasting Bitcoin returns. Given that out-of-sample tests are more reliable than in-sample tests, our results tend to suggest that future Bitcoin returns are unaffected by trade related uncertainties, and investors can use Bitcoin as a safe haven in this context.
    Keywords: Bitcoin, forecasting, machine learning, U.S. – China trade war
    JEL: C53 G11 G17
    Date: 2019–11
  8. By: Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Hossein Hassani (The Statistical Research Centre, Bournemouth University, Bournemouth, UK); Xu Huang (Faculty of Business and Law, De Montfort University, Leicester LE1 9BH, UK)
    Abstract: This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology of Han et al., (2016). Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also find that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.
    Keywords: Quantile, Correlogram, Dependence, Predictability
    JEL: C22 F31
    Date: 2019–11
  9. By: Gilbert V. Nartea (University of Canterbury); Harold Glenn A. Valera; Maria Luisa G. Valera
    Abstract: We investigate the stationarity of real stock prices among 12 Asia-Pacific countries over the period 1991–2018. The methodology employed is driven by the need to address three key concerns: (i) the identification of which positive or negative shocks are linked to stationarity; (ii) the identification of different speeds of adjustment towards long-run equilibrium; and (iii) the identification of mean reversion and potential asymmetric speed of adjustment before and after the 2008-2009 global financial crisis. To meet these concerns, we examine the time series properties of the data within a quantile unit root testing framework. Our results generally indicate that real stock prices are stationary at the upper quantiles only. There is also evidence of a varied speed of adjustment process across the quantiles where stationarity is present. Further analysis indicates that real stock prices became much more reverting and with a faster speed of adjustment after the global financial crisis, except for Japan and New Zealand.
    Keywords: Stock prices, Mean reversion, Quantile unit root regression
    JEL: C1 C5 G1
    Date: 2019–11–01
  10. By: Maria Gabriela Ladu (University of Sassari and ISTAT); Andrea Linarello (Bank of Italy); Filippo Oropallo (ISTAT)
    Abstract: In this paper we use firm-level data on the universe of Italian manufacturing multi-product exporters to test whether demand shocks in export markets lead multi-product exporters to increase their productivity. The main mechanism behind the documented productivity gains is the reallocation of resources across products within firms (Mayer et al., 2014 and 2016). Intuitively, the increased demand stemming from foreign markets will induce firms to adjust their product-mix by moving inputs from low to high productive/profitable uses. We find that these productivity gains are significant and account for about 30 per cent of aggregate productivity growth in the manufacturing sector.
    Keywords: Italian manufacturing sector, export, trade shocks, productivity
    JEL: D22 F14
    Date: 2019–10

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