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Alternative assets such as fine art, wine, or diamonds have become popular investment vehicles in the aftermath of the global financial crisis. Correlation with classical financial markets is typically low, such that diversification benefits arise for portfolio allocation and risk management. Cryptocurrencies share many alternative asset features, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This collection of papers addresses alternative assets and cryptocurrencies from economic, financial, statistical, and technical points of view. It gives an overview of their current state and explores their properties and prospects using innovative approaches and methodologies.

inflation propensity --- realized volatility --- portfolio modelling --- diamond stocks --- systemic risk --- cryptocurrencies --- initial coin offering --- smooth transition --- investment asset --- GARCH --- risk management --- transaction costs --- liquidity costs --- time series --- Baltic dry index --- statistical arbitrage --- volume --- cryptocurrency --- Hashrate --- blockchain --- diamond prices --- pro-cyclical volatility --- capital asset pricing model --- Bitcoin volatility --- trend prediction --- collatz conjecture --- high-frequency finance --- sentiment --- geometric distribution --- speculative bubbles --- gold --- classification framework --- limit order book --- venture capital --- proof-of-work --- high frequency --- Bitcoin --- machine learning --- metric learning --- stylized fact --- digital currency --- crowdfunding --- HAR --- GARCH-MIDAS --- bitcoin --- Finance. --- Funding --- Funds --- Economics --- Currency question

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With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets.

Economics, finance, business & management --- insurance --- Solvency II --- risk-neutral models --- computational finance --- asset pricing models --- overnight price gaps --- financial econometrics --- mean-reversion --- statistical arbitrage --- high-frequency data --- jump-diffusion model --- instantaneous volatility --- directional-change --- seasonality --- forex --- bitcoin --- S&amp --- P500 --- risk management --- drawdown --- safe assets --- securitisation --- dealer behaviour --- liquidity --- bid–ask spread --- least-squares Monte Carlo --- put-call symmetry --- regression --- simulation --- algorithmic trading --- market quality --- defined contribution plan --- probability of shortfall --- quadratic shortfall --- dynamic asset allocation --- resampled backtests --- stochastic covariance --- 4/2 model --- option pricing --- risk measures --- American options --- exercise boundary --- Monte Carlo --- multiple exercise options --- dynamic programming --- stochastic optimal control --- asset pricing --- calibration --- derivatives --- hedging --- multivariate models --- volatility

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There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.

n/a --- short-term forecasting --- wavelet transform --- IPO --- volatility --- US dollar --- institutional investors’ shareholdings --- neural network --- financial market stress --- market microstructure --- text similarity --- TVP-VAR model --- Japanese yen --- convolutional neural networks --- global financial crisis --- deep neural network --- cross-correlation function --- boosting --- causality-in-variance --- flight to quality --- bagging --- earnings quality --- algorithmic trading --- stop loss --- statistical arbitrage --- ensemble learning --- liquidity risk premium --- gold return --- futures market --- take profit --- currency crisis --- spark spread --- city banks --- piecewise regression model --- financial and non-financial variables --- exports --- data mining --- latency --- crude oil futures prices forecasting --- random forests --- wholesale electricity --- SVM --- random forest --- bank credit --- deep learning --- Vietnam --- inertia --- MACD --- initial public offering --- text mining --- bankruptcy prediction --- exchange rate --- asset pricing model --- LSTM --- panel data model --- structural break --- credit risk --- housing and stock markets --- copula --- ARDL --- earnings manipulation --- machine learning --- natural gas --- housing price --- asymmetric dependence --- real estate development loans --- earnings management --- cointegration --- predictive accuracy --- robust regression --- quantile regression --- dependence structure --- housing loans --- price discovery --- utility of international currency --- ATR