New Economics Papers
on Financial Markets
Issue of 2009‒03‒07
seven papers chosen by

  1. Financial distress and banks' communication policy in crisis times By Besancenot, Damien; Vranceanu, Radu
  2. Volatility Forecasting: The Jumps Do Matter By Fulvio Corsi; Davide Pirino; Roberto Reno
  3. Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model By Isao Ishida; Toshiaki Watanabe
  4. Nonparametric Stochastic Volatility By Federico M. Bandi; Roberto Reno
  5. Duration-Based Volatility Estimation By Torben G. Andersen; Dobrislav Dobrev; Ernst Schaumburg
  6. An Optimal Weight for Realized Variance Based on Intermittent High-Frequency Data By Hiroki Masuda; Takayuki Morimoto
  7. Volatility Transmission: What Does Asia-Pacific Markets Expect? By Shamiri, Ahmed

  1. By: Besancenot, Damien (CEPN and University Paris 13); Vranceanu, Radu (ESSEC Business School)
    Abstract: This short paper analyzes banks' communication policies in crisis times and the role of imperfect information in enhancing banks' distress. If banks differ in their exposure to risky assets, fragile banks may claim to be solid only in order to manipulate investors' expectations. Then solid banks must pay a larger interest rate than in a perfect information set-up. A stronger sanction for false information would improve the situation of the low-risk banks but deteriorate the situation of the high-risk banks. The total effect on defaulting credit institutions is ambiguous. It is shown that, in some cases, the optimal sanction is lower than the sanction that rules out any manipulatory behaviour.
    Keywords: Banks; Disclosure; Financial Crisis; Transparency
    JEL: D82 E44 G21
    Date: 2008–11
  2. By: Fulvio Corsi; Davide Pirino; Roberto Reno
    Abstract: This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is correctly separated into its continuous and discontinuous component. To this purpose, we introduce the concept of threshold multipower variation (TMPV), which is based on the joint use of bipower variation and threshold estimation. With respect to alternative methods, our TMPV estimator provides less biased and robust estimates of the continuous quadratic variation and jumps. This technique also provides a new test for jump detection which has substantially more power than traditional tests. We use this separation to forecast volatility by employing an heterogeneous autoregressive (HAR) model which is suitable to parsimoniously model long memory in realized volatility time series. Empirical analysis shows that the proposed techniques improve significantly the accuracy of volatility forecasts for the S&P500 index, single stocks and US bond yields, especially in periods following the occurrence of a jump.
    Keywords: volatility forecasting, jumps, bipower variation, threshold estimation, stock, bond
    JEL: G1 C1 C22 C53
    Date: 2009–03
  3. 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
  4. 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
  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: 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
  7. By: Shamiri, Ahmed
    Abstract: The purpose of this paper is to investigate the international information transmission of return and volatility spillovers from the US and Japan and the rest of the Asia-Pacific markets using daily stock market return data covering the last 14 years. In the majority of the markets under scrutiny, we provide evidence of direct volatility spillovers, running mainly from the Japanese and US markets and pointing to more rapid information transmission during the recent years. First, the volatility of the Asia-Pacific markets is becoming influenced more by the US market for the recent years. Secondly, for international investors to get profits from the returns of Asia-Pacific securities, it is necessary to pay attention to the US market directly. Third, Korea, Singapore and Hong Kong are among the most Asia-Pacific markets vulnerable to shocks from US investors due to the large ratio of portfolio holding.
    Keywords: GARCH-BEKK; volatility spillovers; multivariate GARCH
    JEL: C5 C01
    Date: 2008

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