nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒04‒02
seven papers chosen by

  1. Expected Stock Returns and the Correlation Risk Premium By Buss, Adrian; Schönleber, Lorenzo; Vilkov, Grigory
  2. Does the time horizon of the return predictive effect of investor sentiment vary with stock characteristics? A Granger causality analysis in the frequency domain By Yong Jiang; Zhongbao Zhou
  3. Social media bots and stock markets By Rui Fan; Oleksandr Talavera; Vu Tran
  4. On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market By Saker Sabkha; Christian De Peretti
  5. Brexit and Uncertainty in Financial Markets By Guglielmo Maria Caporale; Luis A. Gil-Alana; Tommaso Trani
  6. The Relation between Monetary Policy and the Stock Market in Europe By Helmut Lütkepohl; Aleksei Netsunajev
  7. Forecasting the Volatility of the Chinese Gold Market by ARCH Family Models and extension to Stable Models By Marie-Eliette Dury; Bing Xiao

  1. By: Buss, Adrian; Schönleber, Lorenzo; Vilkov, Grigory
    Abstract: We show that the correlation risk premium can predict future market excess returns in-sample and out-of-sample for long horizons and contains information that is non-redundant relative to the variance risk premium. To exploit this predictability, we develop a novel estimation methodology that uses contemporaneous increments of option-implied variables, efficiently removing any lag in estimation of variance and correlation risk betas. The methodology leads to considerable out-of-sample predictability, with an R2 of 7.0% at an annual horizon, and substantial economic gains for investors. The results are supported by a multi-asset general-equilibrium model in which variance and correlation risk are endogenously priced.
    Keywords: correlation risk premium; diversification}; option-implied information; out-of-sample return predictability
    JEL: G11 G12 G13 G17
    Date: 2018–02
  2. By: Yong Jiang; Zhongbao Zhou
    Abstract: Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the frequency domain proposed by Lemmens et al. (2008), this paper examine whether the time horizon of the predictive effect of investor sentiment on the U.S. returns of stocks vary with different firm characteristics (e.g., firm size (Size), book-to-market equity (B/M) rate, operating profitability (OP) and investment (Inv)). The empirical results indicate that investor sentiment has a long-term (more than 12 months) or short-term (less than 12 months) predictive effect on stock returns with different firm characteristics. Specifically, the investor sentiment has strong predictability in the stock returns for smaller Size stocks, lower B/M stocks and lower OP stocks, both in the short term and long term, but only has a short-term predictability for higher quantile ones. The investor sentiment merely has predictability for the returns of smaller Inv stocks in the short term, but has a strong short-term and long-term predictability for larger Inv stocks. These results have important implications for the investors for the planning of the short and the long run stock investment strategy.
    Date: 2018–03
  3. By: Rui Fan (School of Management, Swansea University); Oleksandr Talavera (School of Management, Swansea University); Vu Tran (School of Management, Swansea University)
    Abstract: This study examines whether stock indicators are affected by information in social media such as Twitter. Using a daily sample of tweets with a FTSE 100 firm name over two years, we find insignificant associations between tweets/bot-tweets and stock returns whereas there is a strongly significant association with volatility and trading volume. Using a high-frequency sample, we detect a positive (negative) impact of tweets (bot-tweets) on stock returns. The impact of bot-tweets vanishes within 30 minutes. The results for volatility and trading volume are consistent with the daily data analysis. In addition, event study reveals a bounce-back pattern of price reactions in response to negative retweets. Abnormal increases in tweets/bottweets have significant effects on stock volatility, trading volume and liquidity.
    Keywords: Social media bots, investor sentiments, noise traders, text classification, computational linguistics
    JEL: G12 G14 L86
    Date: 2018–03–23
  4. By: Saker Sabkha (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Christian De Peretti (ISFA - Institut des Science Financière et d'Assurances - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - Université de Lyon)
    Abstract: The study of an efficient financial assets' modeling method is still an open hot issue especially during recent crises. Using credit risk data from 33 worldwide countries, this paper investigates the performance of 9 Dynamic Conditional Correlation models taking into account different properties of financial markets (long memory behavior, asymmetry and/or leverage effects...). This comparative study is based on the results of several multivariate diagnostic tests. Findings show that no model outperforms the others in all situations, though, the straightforward DCC-GARCH model seems to provide the most relevant estimator parameters. Yet, the innovations distributions assumption significantly impacts the statistical fit of the model. Our work is useful for financial markets' participants so as to making decision in terms of arbitrage, hedging or speculation. JEL Classification G11, G12, F02, C58
    Keywords: Time-varying correlation, Multivariate diagnostic tests,DCC-class models, Sovereign credit market
    Date: 2018–02–15
  5. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Tommaso Trani
    Abstract: This paper applies long-memory techniques (both parametric and semi-parametric) to examine whether Brexit has led to any significant changes in the degree of persistence of the FTSE 100 Implied Volatility Index (IVI) and of the British pound’s implied volatilities (IVs) vis-à-vis the main currencies traded in the FOREX, namely the euro, the US dollar and the Japanese yen. We split the sample to compare the stochastic properties of the series under investigation before and after the Brexit referendum, and find an increase in the degree of persistence in all cases except for the British pound-yen IV, whose persistence has declined after Brexit. These findings highlight the importance of completing swiftly the negotiations with the EU to achieve an appropriate Brexit deal.
    Keywords: Brexit, uncertainty, IVI index, British pound’s implied volatilities, financial markets
    JEL: C22 F30
    Date: 2018
  6. By: Helmut Lütkepohl; Aleksei Netsunajev
    Abstract: We use a cointegrated structural vector autoregressive model to investigate the relation between euro area monetary policy and the stock market. Since there may be an instantaneous causal relation we consider long-run identifying restrictions for the structural shocks and also use (conditional) heteroskedasticity in the residuals for identification purposes. Heteroskedasticity is modelled by a Markov-switching mechanism. We find a plausible identification scheme for stock market and monetary policy shocks which is consistent with the second order moment structure of the variables. The model indicates that contractionary monetary policy shocks lead to a long-lasting down-turn of real stock prices.
    Keywords: Cointegrated vector autoregression, heteroskedasticity, Markov-switching model, monetary policy analysis
    JEL: C32
    Date: 2018
  7. By: Marie-Eliette Dury (UCA - Université Clermont Auvergne); Bing Xiao (CRCGM - Centre de Recherche Clermontois en Gestion et Management - Clermont Auvergne - École Supérieure de Commerce (ESC) - Clermont-Ferrand - UCA - Université Clermont Auvergne)
    Abstract: Gold plays an important role as a precious metal with portfolio diversification; also it is an underlying asset in which volatility is an important factor for pricing option. The aim of this paper is to examine which autoregressive conditional heteroscedasticity model has the best forecast accuracy applied to Chinese gold prices. It seems that the Student's t distribution characterizes better the heavy-tailed returns than the Gaussian distribution. Assets with higher kurtosis are better predicted by a GARCH model with Student's distribution while assets with lower kurtosis are better forecasted by using an EGARCH model. Moreover, stochastic models such as Stable processes appear as good candidates to take heavy-tailed data into account. The authors attempt to model and forecast the volatility of the gold prices at the Shanghai Gold Exchange (SGE) during 2002–2016, using various models from the ARCH family. The analysis covers from as in-sample and out-of-sample sets respectively. The results have been estimated with MAE, MAPE and RMSE as the measures of performance.
    Keywords: Forecasting, Return, Volatility, Gold Market, ARCH, GARCH, GARCH-M,IGARCH, NGARCH, EGARCH, PARCH, NPARCH, TARCH, Student's t distribution,Symmetric Stable models, H-self-similar processes
    Date: 2018–02–14

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