A Self-Exciting Threshold AutoRegressive (SETAR) model is applied to the Italian stock market volatility, to obtain volatility forecasts and Value-at-Risk (VaR) estimates. There is almost nothing dealing with Italian markets in the literature of Threshold models, which have never been used for VaR purposes up to now. The SETAR model's performance is compared to competitive linear and GARCH specifications and to the JP Morgan's RiskMetrics™ method. Here, the SETAR model shows the best performance in predicting volatility and VaR values, thanks to its ability in capturing some major volatility's dynamics. Only the Threshold model is able to distinguish an extraordinary from a persistent market shock. Its superiority is more evident during cri...
The variability of financial markets has become the focus of considerable interest, especially over ...
ABSTRACT: This paper explores three models to estimate volatility: exponential weighted moving avera...
The aim of this paper is to justify the use of threshold autoregressive models in financial time ser...
The aim of this paper is to compare the forecasting performance of competing threshold models, in or...
The use of bivariate cointegrated vector autoregressive models (CVAR) and Baba-Engle-Kraft-Koroner (...
The paper describes alternative methods of estimating Value-at-Risk (VaR) thresholds based on two ca...
This paper develops a valuation model for options under the class of self-exciting threshold autoreg...
The paper presents methods of estimating Value-at-Risk (VaR) thresholds utilising two calibrated mod...
The need of proper investment decisions and capital adeguacy led practictioners and researchers in d...
Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even mor...
textabstractValue-at-Risk (VaR) is commonly used for financial risk measurement. It has recently bec...
The aim of this paper is to compare the forecasting performance of competing volatility models, in ...
Value-at-Risk has widely been accepted as the standard measure of market risk in the past twenty yea...
In the present paper the predictor distribution of a SETAR (Self Exciting Threshold Autoregressive) ...
The aim of the presented study was to assess the quality of VaR forecasts in various states of the e...
The variability of financial markets has become the focus of considerable interest, especially over ...
ABSTRACT: This paper explores three models to estimate volatility: exponential weighted moving avera...
The aim of this paper is to justify the use of threshold autoregressive models in financial time ser...
The aim of this paper is to compare the forecasting performance of competing threshold models, in or...
The use of bivariate cointegrated vector autoregressive models (CVAR) and Baba-Engle-Kraft-Koroner (...
The paper describes alternative methods of estimating Value-at-Risk (VaR) thresholds based on two ca...
This paper develops a valuation model for options under the class of self-exciting threshold autoreg...
The paper presents methods of estimating Value-at-Risk (VaR) thresholds utilising two calibrated mod...
The need of proper investment decisions and capital adeguacy led practictioners and researchers in d...
Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even mor...
textabstractValue-at-Risk (VaR) is commonly used for financial risk measurement. It has recently bec...
The aim of this paper is to compare the forecasting performance of competing volatility models, in ...
Value-at-Risk has widely been accepted as the standard measure of market risk in the past twenty yea...
In the present paper the predictor distribution of a SETAR (Self Exciting Threshold Autoregressive) ...
The aim of the presented study was to assess the quality of VaR forecasts in various states of the e...
The variability of financial markets has become the focus of considerable interest, especially over ...
ABSTRACT: This paper explores three models to estimate volatility: exponential weighted moving avera...
The aim of this paper is to justify the use of threshold autoregressive models in financial time ser...