nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒10‒08
seven papers chosen by

  1. Stocks and Bonds: Flight-to-Safety for Ever? By Sessi Tokpavi; Christophe Boucher
  2. Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK By Chia-Lin Chang; Tai-Lin Hsieh; Michael McAleer
  3. Temporal Relational Ranking for Stock Prediction By Fuli Feng; Xiangnan He; Xiang Wang; Cheng Luo; Yiqun Liu; Tat-Seng Chua
  4. Tail probabilities for short-term returns on stocks By Henrik O. Rasmussen; Paul Wilmott
  5. Underwriter Competition and Bargaining Power in the Corporate Bond Market By Manconi, Alberto; Neretina, Ekaterina; Renneboog, Luc
  6. Impact of the Credit Rating Revision on the Eurozone Stock Markets By Trabelsi, Mohamed Ali; Hmida, Salma
  7. How do Stock Prices and Metal Prices Contribute to Economic Activity in Turkey? The Importance of Linear and Non-linear ARDL By Tursoy, Turgut; Faisal, Faisal; Berk, Niyazi; Shahbaz, Muhammad

  1. By: Sessi Tokpavi; Christophe Boucher
    Abstract: This paper gives new insights about flight-to-safety from stocks to bonds, asking whether the strength of this phenomenon remains the same in the current environment of low yields. The motivations lie on the conjecture that when yields are low, the traditional motives of flight-to-safety (wealth protection, liquidity) could not be sufficient, inducing weaker flight-to-safety events. Empirical applications using data for US government bonds and the S&P 500 index, show indeed that when yields are low, the strength of flight-to-safety from stocks to bonds weakens. Moreover, we develop a bivariate model of flight-to-safety transfers that measures to what extent the strength of flight-to-safety from stocks to bonds is related to the strength of flight-to-safety from stocks to other safe haven assets (gold and currencies). Results show that when the strength of flight-to-safety from stocks to bonds decreases the strength of flight-to-safety from stocks to gold increases. This result holds only in the current low-yield environment, suggesting a shift in the historical attractiveness of bonds as safe haven.
    Keywords: Bonds stocks relationship, Flight-to-Safety, Low-yield environment, Bond alternatives, Currencies, Gold.
    JEL: G11 G12 E43 E44
    Date: 2018
  2. By: Chia-Lin Chang (Department of Applied Economics Department of Finance National Chung Hsing University, Taiwan.); Tai-Lin Hsieh (Department of Applied Economics, National Chung Hsing University Taiwan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)
    Abstract: As stock market indexes are not tradeable, the importance and trading volume of Exchange Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the diagonal BEKK model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis, which is presented for the full data set, as well as for the three sub-periods Before, During, and After the Global Financial Crisis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns. For the European Markets, the estimates of the mean equations tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are quite similar for the whole sample period and at least two of the three sub-periods. For the US Markets, the estimates of the mean equations also tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are very similar for the whole sample period and the three sub-periods.
    Keywords: Stock market indexes; Exchange Traded Funds; Volatility Index (VIX); Global Financial Crisis; Vector Autoregressions; Moving Average processes; Conditional Heteroskedasticity; Diagonal BEKK.
    JEL: C32 C58 G12 G15
    Date: 2018–09
  3. By: Fuli Feng; Xiangnan He; Xiang Wang; Cheng Luo; Yiqun Liu; Tat-Seng Chua
    Abstract: Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.
    Date: 2018–09
  4. By: Henrik O. Rasmussen; Paul Wilmott
    Abstract: We consider the tail probabilities of stock returns for a general class of stochastic volatility models. In these models, the stochastic differential equation for volatility is autonomous, time-homogeneous and dependent on only a finite number of dimensional parameters. Three bounds on the high-volatility limits of the drift and diffusion coefficients of volatility ensure that volatility is mean-reverting, has long memory and is as volatile as the stock price. Dimensional analysis then provides leading-order approximations to the drift and diffusion coefficients of volatility for the high-volatility limit. Thereby, using the Kolmogorov forward equation for the transition probability of volatility, we find that the tail probability for short-term returns falls off like an inverse cubic. Our analysis then provides a possible explanation for the inverse cubic fall off that Gopikrishnan et al. (1998) report for returns over 5-120 minutes intervals. We find, moreover, that the tail probability scales like the length of the interval, over which the return is measured, to the power 3/2. There do not seem to be any empirical results in the literature with which to compare this last prediction.
    Date: 2018–09
  5. By: Manconi, Alberto (Tilburg University, Center For Economic Research); Neretina, Ekaterina (Tilburg University, Center For Economic Research); Renneboog, Luc (Tilburg University, Center For Economic Research)
    Abstract: We develop a new measure of underwriter bargaining power and a novel empirical approach, based on underwriters’ comparative ability to place bonds. When an issuer has few “outside options” to take her bond to the market, the underwriter enjoys a stronger bargaining power over her. The key feature of our approach is that bargaining power varies for a given underwriter at a given point in time across different issuers, allowing us to separate the effects of bargaining power from those of reputation and certification with a fixed effects strategy. Using our measure, we document that powerful underwriters are able to extract rents at the expense of bond issuers. For issues with the highest underwriter bargaining power, fees and bond offering yields increase by a combined cost of USD 1.5 million, or about 7% of the average costs for the issuer. We rule out alternative mechanisms based on issuer-underwriter “loyalty”. Our findings suggest that lack of competition increases underwriter bargaining power, resulting in material costs for corporate bond issuers.
    Keywords: Bargaining power; corporate bonds; underwriting
    JEL: G32 G24
    Date: 2018
  6. By: Trabelsi, Mohamed Ali; Hmida, Salma
    Abstract: The contagion generated by the US subprime crisis and the European sovereign debt crisis that hit the Eurozone stock markets is still a highly debated subject. In this paper, we try to analyze the revision effect of the credit ratings of the Eurozone countries. To this end, we used a bivariate DCC-GARCH model to measure the extent of dynamic correlations between stock returns of our sample. Our results indicate that credit ratings revisions have a relatively limited effect on the dynamic correlations of the Eurozone stock markets.
    Keywords: Financial contagion; European debt crisis; Dynamic conditional correlations
    JEL: C22 G01 G15
    Date: 2018
  7. By: Tursoy, Turgut; Faisal, Faisal; Berk, Niyazi; Shahbaz, Muhammad
    Abstract: This study explores the association between stock prices, metal prices and economic activity, i.e. industrial production, for the Turkish economy for the period 1896M1-2016M12. The linear and non-linear analysis is conducted by applying the autoregressive distributed lag (ARDL) and non-linear autoregressive distributed lag (NARDL) approaches. The combined cointegration approach is applied to test the robustness of the ARDL and NARDL approaches. The FMOLS, DOLS and CCR regressions are applied to examine the long-run effect of stock prices and metal prices on economic activity. The empirical results reveal that metal and stock prices have a positive impact on economic activity. Metal prices have a negative impact and economic activity has positive effects on stock prices. Furthermore, the NARDL model corroborates the findings obtained from the ARDL model. This paper presents policy guidelines to utilise metals as an economic tool to boost economic activity and stock prices.
    Keywords: Stock Price, Metals Price, Industrial Production, ARDL, NARDL
    JEL: C32 G12
    Date: 2018–09–08

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