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Frank M Song - One of the best experts on this subject based on the ideXlab platform.

  • a two factor ARCH Model for deposit institution stock returns
    1999
    Co-Authors: Frank M Song
    Abstract:

    The economic environment facing both banks and savings and loan associations (S&Ls) changed dramatically during the later 1970s and into the decade of the 1980s. A drastic change in the Federal Reserve Bank's monetary policy regime, coupled with the deregulation of banking, seemed to expose banks and S&Ls to more risks. This paper applied a two- factor Model for a sample of banks and S&Ls in order to identify their changing market risks and interest rate risks. The two factors considered are the market return and the interest rate. This two factor Model is specified by the Autoregressive Conditional Heteroskedacity (ARCH) Modelling strategy and is estimated by the Generalized Method of Moments (GMM). The market and interest rate risks are measured by their time-varying betas. The results suggest that the market risks were volatile over the sample period, 1977-87, and that they increased and became especially volatile after 1982. The interest rate risks, on the other hand, were more stable and did not respond to the changes in the Federal Reserve Bank's monetary policy regime in either 1979 or 1982. Specification tests suggest the usefulness of my two-factor ARCH Model in the study of deposit-institution stock returns.

  • a two factor ARCH Model for deposit institution stock returns
    1994
    Co-Authors: Frank M Song
    Abstract:

    This paper specifies a two-factor Model for a sample of deposit institutions. The factors are the market return and an interest rate factor. The two-factor Model is specified with Autoregressive Conditional Heteroskedacity (ARCH) Modeling strategy and is estimated by Generalized Method of Moments (GMM). The market and interest rate risks are measured by their time-varying betas. The results suggest that the market risks have been volatile over the sample period 1977-87 and they increased and became more volatile after 1982. The interest rate risks were more stable and they did not respond to the Fed's regime change in monetary policy in 1979 and 1982. Specification tests suggest the usefulness of my two-factor ARCH Model in the study of deposit-institution stock returns. Copyright 1994 by Ohio State University Press.

Daniel B Nelson - One of the best experts on this subject based on the ideXlab platform.

  • asymptotic filtering theory for multivariate ARCH Models
    1996
    Co-Authors: Daniel B Nelson
    Abstract:

    Abstract ARCH Models are widely used to estimate conditional variances and covariances in financial time series Models. How successfully can ARCH Models carry out this estimation when they are misspecified? How can ARCH Models be made robust to misspecification? Nelson and Foster (1994a) employed continuous record asymptotics to answer these questions in the univariate case. This paper considers the general multivariate case. Our results allow us, for example, to construct an asymptotically optimal ARCH Model for estimating the conditional variance or conditional beta of a stock return given lagged returns on the stock, volume, market returns, implicit volatility from options contracts, and other relevant data. We also allow for time-varying shapes of conditional densities (e.g., ‘heteroskewticity’ and ‘heterokurticity’). Examples are provided.

  • filtering and forecasting with misspecified ARCH Models ii making the right forecast with the wrong Model
    1995
    Co-Authors: Daniel B Nelson, Dean P Foster
    Abstract:

    Abstract A companion paper (Nelson, 1992) showed that in data observed at high frequencies, an ARCH Model may perform well in estimating the conditional variance of a process, even when the ARCH Model is severely misspecified. While such Models may perform reasonably well at filtering (i.e., at estimating unobserved instantaneous conditional variances), they may perform disastrously at medium- and long-term forecasting of the process and its volatility. In this paper, we develop conditions under which a misspecified ARCH Model successfully performs both tasks, filtering and forecasting. The key requirement (in addition to the conditions for consistent filtering) is that the ARCH Model correctly specifies the functional form of the first two conditional moments of all state variables. We apply these results to a diffusion Model employed in the options pricing literature, the stochastic volatility Model of Hull and White (1987), Scott (1987), and Wiggins (1987).

  • asymptotic filtering theory for multivariate ARCH Models
    1994
    Co-Authors: Daniel B Nelson
    Abstract:

    ARCH Models are widely used to estimate conditional variances and covariances in financial time series Models. How successfully can ARCH Models carry out this estimation when they are misspecified? How can ARCH Models be optimally constructed? Nelson and Foster (1994) employed continuous record asymptotics to answer these questions in the univariate case. This paper considers the general multivariate case. Our results allow us, for example, to construct an asymptotically optimal ARCH Model for estimating the conditional variance or conditional beta of a stock return given lagged returns on the stock, volume, market returns, implicit volatility from options contracts, and other relevant data. We also allow for time-varying shapes of conditional densities (e.g., `heteroskewticity` and `heterokurticity'). Examples are provided.

  • filtering and forecasting with misspecified ARCH Models ii making the right forecast with the wrong Model
    1992
    Co-Authors: Daniel B Nelson, Dean P Foster
    Abstract:

    A companion paper (Nelson (1992)) showed that in data observed at high frequencies, an ARCH Model may do a good job at estimating conditional variances, even when the ARCH Model is severely misspecified. While such Models may perform reasonably well at filtering (i.e., at estimating unobserved instantaneous conditional variances) they may perform disastrously at medium and long term forecasting. In this paper, we develop conditions under which a misspecified ARCH Model successfully performs both tasks, filtering and forecasting. The key requirement (in addition to the conditions for consistent filtering) is that the ARCH Model correctly specifies the functional form of the first two conditional moments of all state variables. We apply these results to a diffusion Model employed in the options pricing literature, the stochastic volatility Model of Hull and White (1987), Scott (1987), and Wiggins (1987).

  • filtering and forecasting with misspecified ARCH Models i getting the right variance with the wrong Model
    1992
    Co-Authors: Daniel B Nelson
    Abstract:

    This paper investigates the properties of the conditional covariance estimates generated by a misspecified ARCH Model. For example, suppose that we observe a diffusion process at discrete time intervals of length h. For each h, we use a GARCH(1,1) Model to estimate the instantaneous conditional covariance matrix of the diffusion. Under mild regularity conditions, the difference between these conditional covariance estimates and the true conditional covariance converges to zero in probability as h↓0. Many other ARCH Models (for example, Exponential ARCH) have similar consistency properties. This may well account for the success of ARCH Models in short-term forecasting using high-frequency data, since even misspecified ARCH Models can produce ‘good’ estimates of volatility.

Hiroyuki Kasahara - One of the best experts on this subject based on the ideXlab platform.

  • asymptotic properties of the maximum likelihood estimator in regime switching econometric Models
    2019
    Co-Authors: Hiroyuki Kasahara, Katsumi Shimotsu
    Abstract:

    Abstract Markov regime switching Models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching Models. In particular, the asymptotic distribution of the MLE has been unknown for Models in which some elements of the transition probability matrix have the value of zero, as is commonly assumed in empirical applications with Models with more than two regimes. This also includes Models in which the regime-specific density depends on both the current and the lagged regimes such as the seminal Model of Hamilton (1989) and switching ARCH Model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and consistency of the asymptotic covariance matrix estimate of these Models.

  • asymptotic properties of the maximum likelihood estimator in regime switching econometric Models
    2019
    Co-Authors: Hiroyuki Kasahara, Katsumi Shimotsu
    Abstract:

    Abstract Markov regime switching Models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching Models. In particular, the asymptotic distribution of the MLE has been unknown for Models in which some elements of the transition probability matrix have the value of zero, as is commonly assumed in empirical applications with Models with more than two regimes. This also includes Models in which the regime-specific density depends on both the current and the lagged regimes such as the seminal Model of Hamilton (1989) and switching ARCH Model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and consistency of the asymptotic covariance matrix estimate of these Models.

  • asymptotic properties of the maximum likelihood estimator in regime switching econometric Models
    2017
    Co-Authors: Hiroyuki Kasahara, Katsumi Shimotsu
    Abstract:

    Markov regime switching Models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching Models. In particular, the asymptotic distribution of the MLE has been unknown for Models in which the regime-specific density depends on both the current and the lagged regimes, which include the seminal Model of Hamilton (1989) and the switching ARCH Model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and the consistency of the asymptotic covariance matrix estimate of these Models.

  • asymptotic properties of the maximum likelihood estimator in regime switching econometric Models
    2017
    Co-Authors: Hiroyuki Kasahara, Katsumi Shimotsu
    Abstract:

    Markov regime switching Models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching Models. In particular, the asymptotic distribution of the MLE has been unknown for Models in which the regime-specific density depends on both the current and the lagged regimes, which include the seminal Model of Hamilton (1989) and the switching ARCH Model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and the consistency of the asymptotic covariance matrix estimate of these Models.

Xiaoqiang Liu - One of the best experts on this subject based on the ideXlab platform.

  • truss ARCH Model for shear strength of seismic damaged src frame columns strengthened with cfrp sheets
    2019
    Co-Authors: Sheng Peng, X U Chengxiang, Xiaoqiang Liu
    Abstract:

    Carbon fiber reinforced polymer (CFRP) materials are important reinforcing substances which are widely used in the shear strengthening of seismic-damage steel reinforced concrete (SRC) frame structures. To investigate the shear strength of SRC frame columns strengthened with CFRP sheets, experimental observations on eight seismic-damaged SRC frame columns strengthened with CFRP sheets were conducted at Yangtze University and existing experimental data of 49 SRC columns are presented. Based on the existing experiments, the theories of damage degree, zoning analysis of concrete, and strengthening material of the column are adopted. To present the expression formula of the shear strength of SRC frame columns strengthened with CFRP sheets, the contributions of strengthening material and transverse reinforcement to shear strength in the truss Model are considered, based on the truss-ARCH Model. The contribution of ARCH action is also considered through the analysis of the whole concrete and that of the three zones of the concrete are also considered. The formula is verified, and the calculated results are found to match well with the experimental results. Results indicate that the proposed whole analysis Model can improve the accuracy of shear strength predictions of shear seismic-damaged SRC frame columns reinforced with CFRP sheets.

Fang Han - One of the best experts on this subject based on the ideXlab platform.

  • moment bounds for large autocovariance matrices under dependence
    2020
    Co-Authors: Fang Han
    Abstract:

    The goal of this paper is to obtain expectation bounds for the deviation of large sample autocovariance matrices from their means under weak data dependence. While the accuracy of covariance matrix estimation corresponding to independent data has been well understood, much less is known in the case of dependent data. We make a step toward filling this gap and establish deviation bounds that depend only on the parameters controlling the “intrinsic dimension” of the data up to some logarithmic terms. Our results have immediate impacts on high-dimensional time-series analysis, and we apply them to high-dimensional linear VAR(d) Model, vector-valued ARCH Model, and a Model used in Banna et al. (Random Matrices Theory Appl 5(2):1650006, 2016).

  • moment bounds for large autocovariance matrices under dependence
    2018
    Co-Authors: Fang Han
    Abstract:

    The goal of this paper is to obtain expectation bounds for the deviation of large sample autocovariance matrices from their means under weak data dependence. While the accuracy of covariance matrix estimation corresponding to independent data has been well understood, much less is known in the case of dependent data. We make a step towards filling this gap, and establish deviation bounds that depend only on the parameters controlling the "intrinsic dimension" of the data up to some logarithmic terms. Our results have immediate impacts on high dimensional time series analysis, and we apply them to high dimensional linear VAR($d$) Model, vector-valued ARCH Model, and a Model used in Banna et al. (2016).