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asset pricing --- derivative pricing and hedging --- disruptive financial models --- extreme risks and insurance --- high frequency and algorithmic trading --- taxation --- Finance --- Financial engineering --- Finance. --- Financial engineering. --- Research --- Research. --- Computational finance --- Engineering, Financial --- Funding --- Funds --- Economics --- Currency question --- Public finance
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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.
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& --- 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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Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.
algorithmic trading --- Stop Loss --- Turtle --- ATR --- community finances --- fiscal flexibility --- individualized financial arrangements --- sustainable financial services --- price momentum --- hidden markov model --- asset allocation --- blockchain --- BlockCloud --- Artificial Intelligence --- consensus algorithms --- exchange rates --- fundamentals --- prediction --- random forest --- support vector machine --- neural network --- deep reinforcement learning --- financial market simulation --- agent based simulation --- artificial market --- simulation --- CAR regulation --- portfolio --- contract for difference --- CfD --- reinforcement learning --- RL --- neural networks --- long short-term memory --- LSTM --- Q-learning --- deep learning --- uncertainty --- economic policy --- text mining --- topic model --- yield curve --- term structure of interest rates --- machine learning --- autoencoder --- interpretability
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Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.
Economics, finance, business & management --- algorithmic trading --- Stop Loss --- Turtle --- ATR --- community finances --- fiscal flexibility --- individualized financial arrangements --- sustainable financial services --- price momentum --- hidden markov model --- asset allocation --- blockchain --- BlockCloud --- Artificial Intelligence --- consensus algorithms --- exchange rates --- fundamentals --- prediction --- random forest --- support vector machine --- neural network --- deep reinforcement learning --- financial market simulation --- agent based simulation --- artificial market --- simulation --- CAR regulation --- portfolio --- contract for difference --- CfD --- reinforcement learning --- RL --- neural networks --- long short-term memory --- LSTM --- Q-learning --- deep learning --- uncertainty --- economic policy --- text mining --- topic model --- yield curve --- term structure of interest rates --- machine learning --- autoencoder --- interpretability
Choose an application
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& --- 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
Choose an application
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.
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
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Free trade is always under attack, more than ever in recent years. The imposition of numerous U.S. tariffs in 2018, and the retaliation those tariffs have drawn, has thrust trade issues to the top of the policy agenda. Critics contend that free trade brings economic pain, including plant closings and worker layoffs, and that trade agreements serve corporate interests, undercut domestic environmental regulations, and erode national sovereignty. Why are global trade and agreements such as the Trans-Pacific Partnership so controversial? Does free trade deserve its bad reputation? This book aside the misconceptions that run rampant in the debate over trade and gives readers a clear understanding of the issues involved. In its fifth edition, the book has been updated to address the sweeping new policy developments under the Trump administration and the latest research on the impact of trade.
Free trade --- Globalization. --- United States --- Commercial policy. --- Adobe. --- Algorithmic trading. --- Anti-globalization movement. --- Balance of trade. --- Brookings Institution. --- Bureaucrat. --- Calculation. --- Commercial policy. --- Comparative advantage. --- Competition. --- Competitiveness. --- Consumer. --- Consumption (economics). --- Currency. --- Developed country. --- Donald Trump. --- Dumping (pricing policy). --- Economic development. --- Economic growth. --- Economic integration. --- Economic interventionism. --- Economic policy. --- Economics. --- Economist. --- Economy of the United States. --- Economy. --- Employment. --- Exchange rate. --- Expense. --- Export subsidy. --- Export. --- Financial crisis of 2007–08. --- Financial crisis. --- Foreign direct investment. --- Foreign trade of the United States. --- Free trade. --- General Agreement on Tariffs and Trade. --- Globalization. --- Great Recession. --- Gross domestic product. --- Import "a. --- Import Duty. --- Import. --- Income. --- Industrial policy. --- Industry. --- Inflation. --- International Monetary Fund. --- International trade. --- Investment. --- Journal of International Economics. --- Legislation. --- Liberalization. --- Lobbying. --- Macroeconomics. --- Manufacturing. --- Market economy. --- Market price. --- National security. --- Net Exporter. --- Net Importer. --- North American Free Trade Agreement. --- Oxford University Press. --- Paul Krugman. --- Payment. --- Percentage point. --- Peterson Institute for International Economics. --- Policy. --- Politician. --- Price support. --- Princeton University Press. --- Product (business). --- Productivity. --- Protectionism. --- Ralph Nader. --- Real income. --- Recession. --- Regulation. --- Smoot–Hawley Tariff Act. --- Subsidy. --- Supply (economics). --- Tariff. --- Tax. --- Technology. --- The Wealth of Nations. --- Trade agreement. --- Trade barrier. --- Trade diversion. --- Trade restriction. --- Trade union. --- Trade war. --- Trans-Pacific Partnership. --- Unemployment. --- United States Department of Commerce. --- Wage. --- Welfare. --- World Bank. --- World Trade Organization. --- World War II. --- World economy.
Choose an application
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
Choose an application
Free trade is always under attack, more than ever in recent years. The imposition of numerous U.S. tariffs in 2018, and the retaliation those tariffs have drawn, has thrust trade issues to the top of the policy agenda. Critics contend that free trade brings economic pain, including plant closings and worker layoffs, and that trade agreements serve corporate interests, undercut domestic environmental regulations, and erode national sovereignty. Why are global trade and agreements such as the Trans-Pacific Partnership so controversial? Does free trade deserve its bad reputation? This book aside the misconceptions that run rampant in the debate over trade and gives readers a clear understanding of the issues involved. In its fifth edition, the book has been updated to address the sweeping new policy developments under the Trump administration and the latest research on the impact of trade.
Free trade. --- Globalization. --- Protectionism. --- United States --- United States. --- Commercial policy. --- Adobe. --- Algorithmic trading. --- Anti-globalization movement. --- Balance of trade. --- Brookings Institution. --- Bureaucrat. --- Calculation. --- Comparative advantage. --- Competition. --- Competitiveness. --- Consumer. --- Consumption (economics). --- Currency. --- Developed country. --- Donald Trump. --- Dumping (pricing policy). --- Economic development. --- Economic growth. --- Economic integration. --- Economic interventionism. --- Economic policy. --- Economics. --- Economist. --- Economy of the United States. --- Economy. --- Employment. --- Exchange rate. --- Expense. --- Export subsidy. --- Export. --- Financial crisis of 2007–08. --- Financial crisis. --- Foreign direct investment. --- Foreign trade of the United States. --- General Agreement on Tariffs and Trade. --- Great Recession. --- Gross domestic product. --- Import "a. --- Import Duty. --- Import. --- Income. --- Industrial policy. --- Industry. --- Inflation. --- International Monetary Fund. --- International trade. --- Investment. --- Journal of International Economics. --- Legislation. --- Liberalization. --- Lobbying. --- Macroeconomics. --- Manufacturing. --- Market economy. --- Market price. --- National security. --- Net Exporter. --- Net Importer. --- North American Free Trade Agreement. --- Oxford University Press. --- Paul Krugman. --- Payment. --- Percentage point. --- Peterson Institute for International Economics. --- Policy. --- Politician. --- Price support. --- Princeton University Press. --- Product (business). --- Productivity. --- Ralph Nader. --- Real income. --- Recession. --- Regulation. --- Smoot–Hawley Tariff Act. --- Subsidy. --- Supply (economics). --- Tariff. --- Tax. --- Technology. --- The Wealth of Nations. --- Trade agreement. --- Trade barrier. --- Trade diversion. --- Trade restriction. --- Trade union. --- Trade war. --- Trans-Pacific Partnership. --- Unemployment. --- United States Department of Commerce. --- Wage. --- Welfare. --- World Bank. --- World Trade Organization. --- World War II. --- World economy. --- Free trade
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