nep-fmk New Economics Papers
on Financial Markets
Issue of 2013‒06‒09
five papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Multifractality and long memory of a financial index By Pablo Su\'arez-Garc\'ia; David G\'omez-Ullate
  2. Price jump prediction in a limit order book By Ban Zheng; Eric Moulines; Frédéric Abergel
  3. DOW effects in returns and in volatility of stock markets during quiet and turbulent times By Dumitriu, Ramona; Stefanescu, Razvan
  4. Bond returns and market expectations By Carlo Altavilla; Raffaella Giacomini; Riccardo Costantini
  5. What drives option prices ? By Frédéric Abergel; Riadh Zaatour

  1. By: Pablo Su\'arez-Garc\'ia; David G\'omez-Ullate
    Abstract: In this paper we will try to assess the multifractality displayed by the high-frequency returns of Madrid's Stock Exchange IBEX35 index. A Multifractal Detrended Fluctuation Analysis shows that this index has a wide singularity spectrum which is most likely caused by its long memory. Our findings also show that this long-memory can be considered as the superposition of a high-frequency component (related to the daily cycles of arrival of information to the market), over a slowly-varying component that reverberates for long periods of time and which shows no apparent relation with human economic cycles. This later component is therefore postulated to be endogenous to market's dynamics and to be also the most probable source of some of the stylized facts commonly associated with financial time series.
    Date: 2013–06
  2. By: Ban Zheng (LTCI - Laboratoire Traitement et Communication de l'Information [Paris] - Télécom ParisTech - CNRS : UMR5141, FiQuant - Chaire de finance quantitative - Ecole Centrale Paris); Eric Moulines (LTCI - Laboratoire Traitement et Communication de l'Information [Paris] - Télécom ParisTech - CNRS : UMR5141); Frédéric Abergel (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)
    Abstract: A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we de ne price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of di erent features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we nd that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.
    Keywords: limit order book, price jumps, predictibility, LASSO,
    Date: 2013–05
  3. By: Dumitriu, Ramona; Stefanescu, Razvan
    Abstract: The persistence in time of the calendar anomalies is one of the most disputed subjects from the financial literature. Quite often, the passing from quiet to turbulent periods of time provokes radical changes in the investors’ behaviors which affect the stock markets seasonality. In this paper we investigate the presence of the day of the week effects in returns and volatility for 32 indexes from advanced and emerging markets. We analyze this seasonality for two periods of time: a relative quiet period, from January 2000 to December 2006, and a more turbulent period, from January 2007 to September 2012. A GJR-GARCH model allows us to identify, for the two periods, various forms of day of the week effects in returns and volatility. However, only for few indexes we find the stability in time of the daily seasonality. For many of the advanced markets indexes, the day of the week effects in returns identified for the quiet period disappeared during the turbulent period. A less radical decline occurred for the day of the week effects in volatility. In the case of indexes from the emerging markets, the persistence in time of the daily seasonality in returns was more consistent in comparison with advanced markets indexes. Regarding the volatility of emerging markets, we find that during the turbulent period many day of the week effects in volatility disappeared, while new others appeared.
    Keywords: Calendar Anomalies, GJR - GARCH, Volatility, Day of the Week Effects, Stock Markets
    JEL: C58 G14 G15
    Date: 2013–02–28
  4. By: Carlo Altavilla; Raffaella Giacomini (Institute for Fiscal Studies and UCL); Riccardo Costantini
    Abstract: A well-documented empirical result is that market expectations extracted from futures contracts on the federal funds rate are among the best predictors for the future course of monetary policy. We show how this information can be exploited to produce accurate forecasts of bond excess returns and to construct profitable investment strategies in bond markets. We use a tilting method for incorporating market expectations into forecasts from a standard term-structure model and then derive the implied forecasts for bond excess returns. We find that the method delivers substantial improvements in out-of-sample accuracy relative to a number of benchmarks. The accuracy improvements are both statistically and economically significant and robust across a number of maturities and forecast horizons. The method would have allowed an investor to obtain positive cumulative excess returns from simple "riding the yield curve" investment strategies over the past ten years, and in this respect it would have outperformed its competitors even after accounting for a risk-return tradeoff.
    Keywords: Yield curve modelling, futures, market timing, exponential tilting, Kullback-Leibler
    Date: 2013–05
  5. By: Frédéric Abergel (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris); Riadh Zaatour (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)
    Abstract: We rely on high frequency data to explore the joint dynamics of underlying and option markets. In particular, high frequency data make observable the realized variance process of the underlying, so its effects on option price dynamics are tested. Empirical results are confronted with the predictions of stochastic volatility models. The study reveals that while the modeling of stochastic volatility gives more robust models, the market does not process information on the realized variance to update option prices.
    Keywords: options, microstructure, smile, stochastic volatility
    Date: 2012–06

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