Showing 51 - 60 of 84
Persistent link: https://www.econbiz.de/10005015117
In this paper, we use the generalized Hurst exponent approach to study the multi- scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multiscaling. We observe a puzzling phenomenon where an apparent increase in...
Persistent link: https://www.econbiz.de/10009399139
In this paper, we contribute to the literature on energy market co-movement by studying its dynamics in the time-frequency domain. The novelty of our approach lies in the application of wavelet tools to commodity market data. A major part of economic time series analysis is done in the time or...
Persistent link: https://www.econbiz.de/10009422067
In this paper we propose a new approach to estimation of the tail exponent in financial stock markets. We begin the study with the finite sample behavior of the Hill estimator under {\alpha}-stable distributions. Using large Monte Carlo simulations, we show that the Hill estimator overestimates...
Persistent link: https://www.econbiz.de/10009422068
In this paper, we show how the sampling properties of the Hurst exponent methods of estimation change with the presence of heavy tails. We run extensive Monte Carlo simulations to find out how rescaled range analysis (R/S), multifractal detrended fluctuation analysis (MF-DFA), detrending moving...
Persistent link: https://www.econbiz.de/10009422072
We introduce wavelet-based methodology for estimation of realized variance allowing its measurement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Discrete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and...
Persistent link: https://www.econbiz.de/10009492881
We study the ability of artificial neural networks to price the European style call and put options on the S&P 500 index covering the daily data for the period from June 2004 to June 2007. The greatest advantage of option pricing with neural networks is that we do not need to make any...
Persistent link: https://www.econbiz.de/10009643445
This paper shows how fan charts generated from Bayesian vector autoregression (BVAR) models can be useful for assessing 1) the forecasting accuracy of central banks’ prediction models and 2) the credibility of stress tests carried out to evaluate financial stability. Using unique data...
Persistent link: https://www.econbiz.de/10009645624
Persistent link: https://www.econbiz.de/10010548301
This paper revisits the fractional cointegrating relationship between ex-ante implied volatility and ex-post realized volatility. We argue that the concept of corridor implied volatility (CIV) should be used instead of the popular model-free option-implied volatility (MFIV) when assessing the...
Persistent link: https://www.econbiz.de/10010610157