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This paper examines the adaptive market hypothesis of Lo (2004, 2005) using the Ito and Noda’s (2012) non-Bayesian time-varying AR model in Japan. As shown in Ito and Noda (2012), their degree of mar...
Efficient simulation of rare events involving sums of heavy-tailed random vari-ables has been an active research area in applied probability in the lastfifteen years.These rare events arise in many ap...
This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that joint...
This paper focuses on an extension of the Limit Order Book (LOB) model with general shape introduced by Alfonsi, Fruth and Schied. Here, the additional feature allows a time-varying LOB depth. We solv...
We suggest using a class of semiparametric dynamic panel data models to capture individual variations in panel data. The model assumes linearity in some continuous/discrete variables that can be exo...
Bollerslev’s (1990) constant conditional correlation (CCC) and Engle’s (2002) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models ...
In this paper the U.S. gasoline demand from 1976 to 2008 is estimated using a time-varying cointegrating regression.
We study quantile regression estimation for dynamic models with partially varying coefficients so that the values of some coefficients may be functions of informative covariates. Estimation of both pa...
Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from twelve European bourses, this paper presents VaR measures based on their un...
Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M...
In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation structure where the ...
We introduce a multivariate components model for returns and net relative inflows into hedge funds, accounting for time-varying market premia. We estimate alpha as an unobserved variable of the econom...
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible ...
We suggest using a class of semiparametric dynamic panel data models to capture individual variations in panel data. The model assumes linearity in some continu ous/discrete variables which can be exo...
We study a new class of semiparametric instrumental variables models with the structural function represented by a partially varying coefficient functional form. Under this representation, the models ...

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