State space models play a key role in the estimation of time-varying sensitivities in financial markets. The objective of this book is to analyze the relative merits of modern time series techniques, such as Markov regime switching and the Kalman filter, to model structural changes in the...

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...

Do more active hedge fund managing strategies generate higher returns than the less active ones? We develop a novel approach to measuring activeness for hedge funds by estimating the dynamics of risk exposure of a large sample of live and dead equity long-short funds. We find that higher...

In this article, we have tested a linear Gaussian state space model and the kalman filter in testing ARMA(2,3) models of the natural logarithmic monthly market returns of the US 1838 bond debenture closed-end fund. The aim is to estimate expectations that arises from the interaction of...

This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min-data. Empirical RWCC results indicate...