Predictability is time and frequency dependent. We propose a new forecasting method - forecast combination in the frequency domain - that takes this fact into account. With this method we forecast the equity premium and real GDP growth rate. Combining forecasts in the frequency domain produces...

In this work we focus on the application of wavelet-based methods in volatility modeling. We introduce a new, wavelet-based estimator (wavelet Whittle estimator) of a FIEGARCH model, ARCH-family model capturing long-memory and asymmetry in volatility, and study its properties. Based on an...

We study the relationship between conditional quantiles of returns and the long-, medium- and short-term volatility in a portfolio of financial assets. We argue that the combination of quantile panel regression and wavelet decomposition of the volatility time series provides us with new insights...

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric...

This research investigates the effect of the US economic policy uncertainty (EPU) on the price dynamics of agricultural, energy and metal over the period 1985:01–2017:12 using wavelet coherence analysis. The results demonstrate that the price movement among the commodities and EPU indices...

We propose an empirical test to depict possible endogenous cycles within Heterogeneous Agent Models (HAMs). We consider a 2-type HAM into a standard small-scale dynamic asset pricing framework. On the one hand, fundamentalists base their expectations on the deviation of fundamental value from...

Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long-memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, as well as...

Cryptocurrencies provide the ideal and natural experimental setting to test the local martingale theory of bubbles, because they have no cash flows. Using this theory, we test for the existence of price bubbles in eight cryptocurrencies from January 1, 2019 to July 17, 2019. The cryptocurrencies...