New Economics Papers
on Market Microstructure
Issue of 2009‒03‒07
seven papers chosen by
Thanos Verousis

  1. An Optimal Weight for Realized Variance Based on Intermittent High-Frequency Data By Hiroki Masuda; Takayuki Morimoto
  2. Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model By Isao Ishida; Toshiaki Watanabe
  3. Nonparametric Stochastic Volatility By Federico M. Bandi; Roberto Reno
  4. Private information, stock markets, and exchange rates By Jacob Gyntelberg; Mico Loretan; Tientip Subhanij; Eric Chan
  5. Duration-Based Volatility Estimation By Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg
  6. Semiparametric vector MEM By Fabrizio Cipollini; Robert F. Engle; Giampiero M. Gallo
  7. Asset prices and exchange rates: a time dependent approach By Giulia PICCILLO

  1. By: Hiroki Masuda; Takayuki Morimoto
    Abstract: In Japanese stock markets, there are two kinds of breaks, i.e., nighttime and lunch break, where we have no trading, entailing inevitable increase of variance in estimating daily volatility via naive realized variance (RV). In order to perform a much more stabilized estimation, we are concerned here with a modification of the weighting technique of Hansen and Lunde (2005). As an empirical study, we estimate optimal weights in a certain sense for Japanese stock data listed on the Tokyo Stock Exchange. We found that, in most stocks appropriate use of the optimally weighted RV can lead to remarkably smaller estimation variance compared with naive RV, hence substantially to more accurate forecasting of daily volatility.
    Keywords: high-frequency data, market microstructure noise, realized volatility, Japanese stock markets, variance of realized variance
    JEL: C19 C22 C51
    Date: 2009–02
  2. By: Isao Ishida; Toshiaki Watanabe
    Abstract: In this paper, we apply the ARFIMA-GARCH model to the realized volatility and the continuous sample path variations constructed from high-frequency Nikkei 225 data. While the homoskedastic ARFIMA model performs excellently in predicting the Nikkei 225 realized volatility time series and their square-root and log transformations, the residuals of the model suggest presence of strong conditional heteroskedasticity similar to the finding of Corsi et al. (2007) for the realized S&P 500 futures volatility. An ARFIMA model augmented by a GARCH(1,1) specification for the error term largely captures this and substantially improves the fit to the data. In a multi-day forecasting setting, we also find some evidence of predictable time variation in the volatility of the Nikkei 225 volatility captured by the ARFIMA-GARCH model.
    Keywords: ARFIMA-GARCH, Volatility of realized volatility, Realized bipower variation, Jump detection, BDS test, Hong-Li test, High-frequency Nikkei 225 data
    JEL: C22 C53 G15
    Date: 2009–02
  3. By: Federico M. Bandi; Roberto Reno
    Abstract: Using recent advances in the nonparametric estimation of continuous-time processes under mild statistical assumptions as well as recent developments on nonparametric volatility estimation by virtue of market microstructure noise-contaminated high-frequency asset price data, we provide (i) a theory of spot variance estimation and (ii) functional methods for stochastic volatility modelling. Our methods allow for the joint evaluation of return and volatility dynamics with nonlinear drift and diffusion functions, nonlinear leverage effects, jumps in returns and volatility with possibly state-dependent jump intensities, as well as nonlinear risk-return trade-offs. Our identification approach and asymptotic results apply under weak recurrence assumptions and, hence, accommodate the persistence properties of variance in finite samples. Functional estimation of a generalized (i.e., nonlinear) version of the square-root stochastic variance model with jumps in both volatility and returns for the S&P500 index suggests the need for richer variance dynamics than in existing work. We find a linear specification for the variance's diffusive variance to be misspecified (and inferior to a more flexible CEV specification) even when allowing for jumps in the variance dynamics.
    Keywords: Spot variance, stochastic volatility, jumps in returns, jumps in volatility, leverage effects, risk-return trade-offs, kernel methods, recurrence, market microstructure noise.
    Date: 2009–03
  4. By: Jacob Gyntelberg; Mico Loretan; Tientip Subhanij; Eric Chan
    Abstract: Explaining exchange rates has long been an important but vexing issue in international economics and finance. In recent years, a number of studies have shown that investors' private information plays a central role in determining exchange rates. We demonstrate in this paper that the private information of investors relevant for exchange rates is largely connected to the stock market, and that this information is conveyed to foreign exchange (FX) markets by order flow that is induced by investors' transactions in the stock market. We establish these results by analyzing several novel unused datasets on nearly two years' worth of daily-frequency capital flows of nonresident investors in the foreign exchange, stock, and bond markets of Thailand. We present compelling evidence that FX order flow that is induced by nonresident investors transactions in the Stock Exchange of Thailand - which we show are driven largely by private information - has far greater explanatory power for the exchange rate than other order flow has, both in the short run and the long run. In contrast, FX order flow of nonresident investors that is related to their transactions in Thai government bonds - which we find are not driven appreciably by private information - does not have a statistically significant effect on the exchange rate.
    Keywords: Exchange rate models, market microstructure approach, asymmetric information, Thailand, generated regressors, impulse response functions, I(1) measurement error
    Date: 2009–02
  5. By: Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg
    Abstract: We develop a novel approach to estimating the integrated variance of a general jump-diffusion with stochastic volatility. Our approach exploits the relationship between the speed (distance traveled per fixed time unit) and passage time (time taken to travel a fixed distance) of the Brownian motion. The new class of duration-based IV estimators derived in this paper is shown to be robust to both jumps and market microstructure noise. Moreover, their asymptotic and finite sample properties compare favorably to those of commonly used robust IV estimators.
    Date: 2009–03
  6. By: Fabrizio Cipollini (Università di Firenze, Dipartimento di Statistica "G. Parenti"); Robert F. Engle (New York University - Leonard Stern School of Business); Giampiero M. Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti")
    Abstract: In financial time series analysis we encounter several instances of non–negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model). Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non–negative valued innovation process. A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks.
    Keywords: Multiplicative Error Model, GMM, Simultaneous Equations, Volatility, Market Activity
    JEL: C22 C52 C53
    Date: 2009–02
  7. By: Giulia PICCILLO
    Abstract: The paper studies the relationship between exchange rates and asset prices. It takes the approach of order ows to exchange rates. Specifically, it focuses on the effect of time-dependent risk aversion. The switch in the parameter causes the equilibrium of the system to alternate between two regimes: an optimistic and a pessimistic one. The paper is complete of a wide empirical section where the two equilibria are identified and specified for three of the main world markets. The regimes appear to be persistent and consistent with the existing literature on risk aversion. This also includes recent events of the financial crisis. The analysis uncovers a new development for exchange rate microstructure models. 3 of the 4 markets studied are consistent with both the order flow and the Markov switching models. The markets analyzed are the UK, Switzerland, Germany and Japan.
    Keywords: Exchange rates, Microstructure, Markov chains
    JEL: C2 F3 G1
    Date: 2008–12

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