nep-fmk New Economics Papers
on Financial Markets
Issue of 2018‒07‒09
nine papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Changing risk-return profiles By Crump, Richard K.; Giannone, Domenico; Hundtofte , Sean
  2. Volatility-of-volatility risk By Huang, Darien; Schlag, Christian; Shaliastovich, Ivan; Thimme, Julian
  3. A Machine Learning Framework for Stock Selection By XingYu Fu; JinHong Du; YiFeng Guo; MingWen Liu; Tao Dong; XiuWen Duan
  4. Weak Correlations of Stocks Future Returns By Ludovico Latmiral
  5. Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors? By Fortin, Alain-Philippe; Simonato, Jean-Guy; Dionne, Georges
  6. The Stock Market Has Grown Unstable Since February 2018 By Blake C. Stacey; Yaneer Bar-Yam
  7. Bank liquidity provision and Basel liquidity regulations By Roberts, Daniel; Sarkar, Asani; Shachar, Or
  8. How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market By Cristina Sattarhoff; Marc Gronwald
  9. Strategic behaviour and indicative price diffusion in Paris Stock Exchange auctions By Damien Challet

  1. By: Crump, Richard K. (Federal Reserve Bank of New York); Giannone, Domenico (Federal Reserve Bank of New York); Hundtofte , Sean (Federal Reserve Bank of New York)
    Abstract: We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time. Current realized volatility has the most information content on the uncertainty of future returns, whereas it has only limited content about the location of the future return distribution. When volatility is low, the predicted distribution of returns is less dispersed and probabilistic forecasts are sharper. Given this finding on the importance of financial sector volatility not just to financial equity return uncertainty but to the broader market, we test for changes in the realized volatility of banks over a $50 billion threshold associated with more stringent Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank) requirements. We find that the equity volatility of these large banks is differentially lower by 9 percentage points after Dodd-Frank compared to pre-crisis levels, controlling for changes over the same period for all banks and all large firms.
    Keywords: stock returns; realized volatility; density forecasts; optimal pools; Dodd-Frank; financial intermediation; financial conditions
    JEL: C22 G17 G18
    Date: 2018–06–01
    URL: http://d.repec.org/n?u=RePEc:fip:fednsr:850&r=fmk
  2. By: Huang, Darien; Schlag, Christian; Shaliastovich, Ivan; Thimme, Julian
    Abstract: We show that time-varying volatility of volatility is a significant risk factor which affects the cross-section and the time-series of index and VIX option returns, beyond volatility risk itself. Volatility and volatility-of-volatility measures, identified modelfree from the option price data as the VIX and VVIX indices, respectively, are only weakly related to each other. Delta-hedged index and VIX option returns are negative on average, and are more negative for strategies which are more exposed to volatility and volatility-of-volatility risks. Volatility and volatility of volatility significantly and negatively predict future delta-hedged option payoffs. The evidence is consistent with a no-arbitrage model featuring time-varying market volatility and volatility-of-volatility factors, both of which have negative market price of risk.
    Keywords: volatility of volatility,hedging errors,risk premiums
    JEL: G12 G13
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:zbw:safewp:210&r=fmk
  3. By: XingYu Fu; JinHong Du; YiFeng Guo; MingWen Liu; Tao Dong; XiuWen Duan
    Abstract: This paper demonstrates how to apply machine learning algorithms to distinguish good stocks from the bad stocks. To this end, we construct 244 technical and fundamental features to characterize each stock, and label stocks according to their ranking with respect to the return-to-volatility ratio. Algorithms ranging from traditional statistical learning methods to recently popular deep learning method, e.g. Logistic Regression (LR), Random Forest (RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the classification task. Genetic Algorithm (GA) is also used to implement feature selection. The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios constructed by our models outperform market average in back tests.
    Date: 2018–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1806.01743&r=fmk
  4. By: Ludovico Latmiral
    Abstract: We analyze correlations of stock returns via a series of widely adopted market and stock parameters which we refer to as \textit{explanatory variables}. We subsequently exploit the results to propose a quantitative adaptive technique to infer predictions on expected relations among future stock returns.
    Date: 2018–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1806.05160&r=fmk
  5. By: Fortin, Alain-Philippe (HEC Montreal, Canada Research Chair in Risk Management); Simonato, Jean-Guy (HEC Montreal, Department of Finance); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: When forecasting the market risk of stock portfolios, is a univariate or a multivariate modeling approach more effective? This question is examined in the context of forecasting the one-week-ahead Expected Shortfall for a portfolio equally invested in the Fama-French and momentum factors. Applying extensive tests and comparisons, we find that in most cases there are no statistically significant differences between the forecasting accuracy of the two approaches. This suggests that univariate models, which are more parsimonious and simpler to implement than multivariate models, can be used to forecast the downsize risk of equity portfolios without losses in precision.
    Keywords: Value-at-Risk; Expected Shortfall; Conditional Value-at-Risk; Elicitability; model comparison; backtesting; Fama-French and momentum factors
    JEL: C22 C32 C52 C53 G17
    Date: 2018–06–18
    URL: http://d.repec.org/n?u=RePEc:ris:crcrmw:2018_004&r=fmk
  6. By: Blake C. Stacey; Yaneer Bar-Yam
    Abstract: On the fifth of February, 2018, the Dow Jones Industrial Average dropped 1,175.21 points, the largest single-day fall in history in raw point terms. This followed a 666-point loss on the second, and another drop of over a thousand points occurred three days later. It is natural to ask whether these events indicate a transition to a new regime of market behavior, particularly given the dramatic fluctuations --- both gains and losses --- in the weeks since. To illuminate this matter, we can apply a model grounded in the science of complex systems, a model that demonstrated considerable success at unraveling the stock-market dynamics from the 1980s through the 2000s. By using large-scale comovement of stock prices as an early indicator of unhealthy market dynamics, this work found that abrupt drops in a certain parameter $U$ provide an early warning of single-day panics and economic crises. Decreases in $U$ indicate regimes of "high co-movement", a market behavior that is not the same as volatility, though market volatility can be a component of co-movement. Applying the same analysis to stock-price data from the beginning of 2016 until now, we find that the $U$ value for the period since 5 February is significantly lower than for the period before. This decrease entered the "danger zone" in the last week of May, 2018.
    Date: 2018–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1806.00529&r=fmk
  7. By: Roberts, Daniel (Federal Reserve Bank of New York); Sarkar, Asani (Federal Reserve Bank of New York); Shachar, Or (Federal Reserve Bank of New York)
    Abstract: We examine liquidity creation per unit of assets by banks subject to the Liquidity Coverage Ratio (LCR) using the liquidity measures Liquidity Mismatch Index (LMI) (Bai et al., 2018) and BB (Berger and Bouwman, 2009). We identify the LCR effects through time and cross-section effects, specific LCR-constrained balance sheet categories, an economically similar asset pair with different LCR weights, and the differential implementation of LCR by the very large and less-large LCR banks. We find that, since 2013, there has been reduced liquidity creation by LCR banks compared to non-LCR banks, occurring mostly through greater holdings of liquid assets and lower holdings of illiquid assets. Trends in liquid asset holdings are driven by High Quality Liquid Assets (HQLA), an LCR-defined category, particularly for assets where market and LCR liquidity weights are most similar. Of particular interest is a post-LCR shift in LCR bank portfolios to GNMA MBS rather than GSE MBS, economically similar assets with different LCR weights, that is not attributable to relatively greater issuances or relative price effects. We also find sharper declines of commercial and residential real estate loans by LCR banks relative to non-LCR banks post-2013. Finally, we find a decline in the high run-off category of LCR liabilities for LCR banks relative to non-LCR banks post-2013 for the largest LCR banks with greater than $250 billion in assets. Our results highlight the trade-off between lower liquidity creation and lower run risk from reduced liquidity mismatch of the largest banks.
    Keywords: LCR; banks; liquidity creation
    JEL: G01 G21 G28
    Date: 2018–06–01
    URL: http://d.repec.org/n?u=RePEc:fip:fednsr:852&r=fmk
  8. By: Cristina Sattarhoff; Marc Gronwald
    Abstract: This paper proposes a new measure for the evaluation of financial market efficiency, the so-called intermittency coefficient. This is a multifractality measure that can quantify the deviation from a random walk within the framework of the multifractal random walk model by Bacry et al. (2001b). While the random walk corresponds to the most genuine form of market efficiency, the larger the value of the intermittency coefficient is, the more inefficient a market would be. In contrast to commonly used methods based on Hurst exponents, the intermittency coefficient is a more powerful tool due to its well-established inference apparatus based on the generalised method of moments estimation technique. In an empirical application using data from the largest currently existing market for tradable pollution permits, the European Union Emissions Trading Scheme, we show that this market becomes more efficient over time. In addition, the degree of market efficiency is overall similar to that for the US stock market; for one sub-period, the market efficiency is found to be higher. While the first finding is anticipated, the second finding is noteworthy, as various observers expressed concerns with regard to the information efficiency of this newly established artificial market.
    Keywords: market efficiency, multifractality, multifractal random walk, European Union Emissions Trading Scheme
    JEL: C58 C53 G14 Q02 Q54
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_7102&r=fmk
  9. By: Damien Challet
    Abstract: We report statistical regularities of the opening and closing auctions of French equities, focusing on the diffusive properties of the indicative auction price. Two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events increases, potentially leading to overdiffusion. A third mechanism, caused by the strategic behavior of traders, is needed to produce nearly diffusive prices: waiting to submit buy orders until sell orders have decreased the indicative price and vice-versa.
    Date: 2018–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1807.00573&r=fmk

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