Factor Analysed Hidden Markov Models for Conditionally Heteroscedastic Financial Time Series

In this article we develop a new approach within the framework of asset pricing models that incorporates two key features of the latent volatility: co-movement among conditionally heteroscedastic financial returns and switching between different unobservable regimes. By combining latent factor models with hidden Markov chain models (HMM) we derive a dynamical local model for segmentation and prediction of multivariate conditionally heteroscedastic financial time series. We concentrate, more precisely on situations where the factor variances are modeled by univariate GQARCH processes. The intuition behind our approach is the use a piece-wise multivariate and linear process - which we can also regard as a mixed-state dynamic linear system - for modeling the regime switches. In particular, we supposed that the observed series can be modeled using a time varying parameter model with the assumption that the evolution of these parameters is governed by a first-order hidden Markov process. The EM algorithm that we have developed for the maximum likelihood estimation, is based on a quasi-optimal switching Kalman filter approach combined with a Viterbi approximation which yield inferences about the unobservable path of the common factors, their variances and the latent variable of the state process. Extensive Monte Carlo simulations and preliminary experiments obtained with daily foreign exchange rate returns of eight currencies show promising results

Statistical Inference for Complex Time Series Data

During recent years the focus of scientific interest has turned from low dimensional stationary time series to nonstationary time series and high dimensional time series. In addition new methodological challenges are coming from high frequency finance where data are recorded and analyzed on a millisecond basis. The three topics “nonstationarity”, “high dimensionality” and “high frequency” are on the forefront of present research in time series analysis. The topics also have some overlap in that there already exists work on the intersection of these three topics, e.g. on locally stationary diffusion models, on high dimensional covariance matrices for high frequency data, or on multivariate dynamic factor models for nonstationary processes. The aim of the workshop was to bring together researchers from time series analysis, nonparametric statistics, econometrics and empirical finance to work on these topics. This aim was successfully achieved and the workshops was very well attended

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Estimation of Hidden Markov Models and Their Applications in Finance

Movements of financial variables exhibit extreme fluctuations during periods of economic crisis and times of market uncertainty. They are also affected by institutional policies and intervention of regulatory authorities. These structural changes driving prices and other economic indicators can be captured reasonably by models featuring regime-switching capabilities. Hidden Markov models (HMM) modulating the model parameters to incorporate such regime-switching dynamics have been put forward in recent years, but many of them could still be further improved. In this research, we aim to address some of the inadequacies of previous regime-switching models in terms of their capacity to provide better forecasts and efficiency in estimating parameters. New models are developed, and their corresponding filtering results are obtained and tested on financial data sets. The contributions of this research work include the following: (i) Recursive filtering algorithms are constructed for a regime-switching financial model consistent with no-arbitrage pricing. An application to the filtering and forecasting of futures prices under a multivariate set-up is presented. (ii) The modelling of risk due to market and funding liquidity is considered by capturing the joint dynamics of three time series (Treasury-Eurodollar spread, VIX and S\&P 500 spread-derived metric), which mirror liquidity levels in the financial markets. HMM filters under a multi-regime mean- reverting model are established. (iii) Kalman filtering techniques and the change of reference probability-based filtering methods are integrated to obtain hybrid algorithms. A pairs trading investment strategy is supported by the combined power of both HMM and Kalman filters. It is shown that an investor is able to benefit from the proposed interplay of the two filtering methods. (iv) A zero-delay HMM is devised for the evolution of multivariate foreign exchange rate data under a high-frequency trading environment. Recursive filters for quantities that are functions of a Markov chain are derived, which in turn provide optimal parameter estimates. (v) An algorithm is designed for the efficient calculation of the joint probability function for the occupation time in a Markov-modulated model for asset returns under a general number of economic regimes. The algorithm is constructed with accessible implementation and practical considerations in mind

Investment in capital markets

Investment in Capital Markets creates a strategic vision on the financial capital investment in the capital markets with the aim to get an increased return premium in the short and long time periods. The book is written with a main goal to explain the pros and cons of the financial capital investment in the capital markets, discussing the sophisticated investment concepts and techniques in the simple understandable readable general format language. We would like to highlight the three interesting facts about the book: 1. It is centered on the consideration of the modern investment products, the investment vehicles and the investment mediums for the financial capital investment in the capital markets; 2. It is focused on the financial risk calculation and mitigation techniques for the financial capital investment in the financial capital markets. 3. It is aimed to describe the quantum winning virtuous investment strategies creation and execution techniques during the financial capital investment in the capital markets. The investors, financiers, economists, financial analysts, financial traders, financial advisers, lawmakers, policy analysts, subject experts, professors, and students will certainly enjoy a breathtaking splendid learning journey with the explained new ideas, established concepts and outlined future prospects toward the financial capital investment in the capital markets with the aim to get an increased return premium in the short and long time periods

Business Cycles in Economics

The business cycles are generated by the oscillating macro-/micro-/nano- economic output variables in the economy of the scale and the scope in the amplitude/frequency/phase/time domains in the economics. The accurate forward looking assumptions on the business cycles oscillation dynamics can optimize the financial capital investing and/or borrowing by the economic agents in the capital markets. The book's main objective is to study the business cycles in the economy of the scale and the scope, formulating the Ledenyov unified business cycles theory in the Ledenyov classic and quantum econodynamics