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Static hedge portfolios for barrier options are very sensitive with respect to changes of the volatility surface. To prevent potentially significant hedging losses this book develops a static super-replication strategy with market-typical robustness against volatility, skew and liquidity risk as well as model errors. Empirical results and various numerical examples confirm that the static superhedge successfully eliminates the risk of a changing volatility surface. Combined with associated sub-replication strategies this leads to robust price bounds for barrier options which are also relevant in the context of dynamic hedging. The mathematical techniques used to prove appropriate existence, duality and convergence results range from financial mathematics, stochastic and semi-infinite optimization, convex analysis and partial differential equations to semidefinite programming.
Options (Finance) --- Hedging (Finance) --- Speculation --- Financial futures --- Mathematical models. --- Mathematical models --- Barrier Options. --- Robust Optimization. --- Semi-infinite Optimization. --- Semidefinite Programming. --- Static Hedging. --- Stochastic Volatility.
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With the COVID-19 pandemic, the intense debate about secular stagnation will become even more important. Empirical estimates of equilibrium real interest rates are so far mostly limited to advanced economies, since no statistical procedure suitable for a large set of countries is available. This is surprising, as equilibrium rates have strong policy implications in emerging markets and developing economies as well; current estimates of the global equilibrium rate rely on only a few countries; and estimates for a more diverse set of countries can improve understanding of the drivers. This paper proposes a model and estimation strategy that decompose ex ante real interest rates into a permanent and transitory component even with short samples and high volatility. This is done with an unobserved component local level stochastic volatility model, which is used to estimate equilibrium rates for 50 countries with Bayesian methods. Equilibrium rates were lower in emerging markets and developing economies than in advanced economies in the 1980s, similar in the 1990s, and have been higher since 2000. In line with economic integration and rising global capital markets, synchronization has been rising over time and is higher among advanced economies. Equilibrium rates of countries with stronger trade linkages and similar demographic and economic trends are more synchronized.
Bayesian Inference --- Business Cycle Transmission --- Business Cycles and Stabilization Policies --- Capital Markets and Capital Flows --- Currencies and Exchange Rates --- Economic Investment and Savings --- Equilibrium --- Finance and Financial Sector Development --- Global Financial Crisis --- Interest Rate --- Macroeconomics and Economic Growth --- Stochastic Volatility --- Synchronization
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Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.
Bonds --- Bond issues --- Debentures --- Negotiable instruments --- Securities --- Debts, Public --- Stocks --- Mathematical models. --- Mathematical models --- E-books --- Finanzas. --- Bonos --- Especulación --- Modelos matemáticos. --- Bonds - Mathematical models --- AFNS. --- Bayesian analysis. --- DNS. --- NelsonГiegel curve fitting. --- RudebuschЗu model. --- affine arbitrage-free models. --- arbitrage-free NelsonГiegel models. --- arbitrage-free dynamic NelsonГiegel. --- arbitrage-free models. --- credit spreads. --- dynamic NelsonГiegel model. --- dynamic NelsonГiegel modeling. --- dynamic yield curve forecasting. --- dynamic yield curve modeling. --- factor loadings. --- forecasting. --- macro-finance yield curve modeling. --- multicountry modeling. --- risk management. --- stateгpace structure. --- stochastic volatility. --- yield curve fitting. --- yield curve models. --- yield curve.
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This book is a collection of feature articles published in Risks in 2020. They were all written by experts in their respective fields. In these articles, they all develop and present new aspects and insights that can help us to understand and cope with the different and ever-changing aspects of risks. In some of the feature articles the probabilistic risk modeling is the central focus, whereas impact and innovation, in the context of financial economics and actuarial science, is somewhat retained and left for future research. In other articles it is the other way around. Ideas and perceptions in financial markets are the driving force of the research but they do not necessarily rely on innovation in the underlying risk models. Together, they are state-of-the-art, expert-led, up-to-date contributions, demonstrating what Risks is and what Risks has to offer: articles that focus on the central aspects of insurance and financial risk management, that detail progress and paths of further development in understanding and dealing with...risks. Asking the same type of questions (which risk allocation and mitigation should be provided, and why?) creates value from three different perspectives: the normative perspective of market regulator; the existential perspective of the financial institution; the phenomenological perspective of the individual consumer or policy holder.
Medicine --- medical services’ consumption --- lifestyle factors --- insurance plan --- structural equation model --- stock–bond correlation --- VIX --- economic policy uncertainty --- monetary policy uncertainty --- fiscal policy uncertainty --- agricultural commodity futures --- price discovery --- market reflexivity --- Hawkes process --- poisson autoregressive models --- contagion --- predictive monitoring --- information-based asset pricing --- Lévy processes --- gamma processes --- variance gamma processes --- Brownian bridges --- gamma bridges --- nonlinear filtering --- house price prediction --- real estate --- machine learning --- random forest --- Lévy process --- subordination --- option pricing --- risk sensitivity --- stochastic volatility --- Greeks --- time-change --- time series --- volatility --- probability-integral transform --- ARMA model --- copula
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This book is a collection of feature articles published in Risks in 2020. They were all written by experts in their respective fields. In these articles, they all develop and present new aspects and insights that can help us to understand and cope with the different and ever-changing aspects of risks. In some of the feature articles the probabilistic risk modeling is the central focus, whereas impact and innovation, in the context of financial economics and actuarial science, is somewhat retained and left for future research. In other articles it is the other way around. Ideas and perceptions in financial markets are the driving force of the research but they do not necessarily rely on innovation in the underlying risk models. Together, they are state-of-the-art, expert-led, up-to-date contributions, demonstrating what Risks is and what Risks has to offer: articles that focus on the central aspects of insurance and financial risk management, that detail progress and paths of further development in understanding and dealing with...risks. Asking the same type of questions (which risk allocation and mitigation should be provided, and why?) creates value from three different perspectives: the normative perspective of market regulator; the existential perspective of the financial institution; the phenomenological perspective of the individual consumer or policy holder.
Medicine --- medical services’ consumption --- lifestyle factors --- insurance plan --- structural equation model --- stock–bond correlation --- VIX --- economic policy uncertainty --- monetary policy uncertainty --- fiscal policy uncertainty --- agricultural commodity futures --- price discovery --- market reflexivity --- Hawkes process --- poisson autoregressive models --- contagion --- predictive monitoring --- information-based asset pricing --- Lévy processes --- gamma processes --- variance gamma processes --- Brownian bridges --- gamma bridges --- nonlinear filtering --- house price prediction --- real estate --- machine learning --- random forest --- Lévy process --- subordination --- option pricing --- risk sensitivity --- stochastic volatility --- Greeks --- time-change --- time series --- volatility --- probability-integral transform --- ARMA model --- copula
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High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis.Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes.Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.
Finance --- Econometrics. --- Econometric models. --- BlumenthalЇetoor indices. --- Brownian motion. --- Fisher efficiency. --- It semimartingale. --- It semimartingales. --- Lvy processes. --- algorithm. --- certain moment functions. --- co-jumps. --- common jumps. --- computerized trading. --- diffusion. --- disjoint jumps. --- financial data. --- finite activity. --- high-frequency econometrics. --- high-frequency financial data. --- high-frequency trading. --- infinite activity. --- integrated volatility estimation. --- integrated volatility. --- irregular observation times. --- jump process. --- jumps. --- local volatility. --- market microstructure noise. --- microstructure noise. --- one-dimensional continuous martingale. --- parameter identifiability. --- power variations. --- price determination. --- risk management. --- semimartingales. --- spot volatility. --- stable convergence. --- stochastic processes. --- stochastic volatility. --- volatility estimation. --- volatility.
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This book is a collection of feature articles published in Risks in 2020. They were all written by experts in their respective fields. In these articles, they all develop and present new aspects and insights that can help us to understand and cope with the different and ever-changing aspects of risks. In some of the feature articles the probabilistic risk modeling is the central focus, whereas impact and innovation, in the context of financial economics and actuarial science, is somewhat retained and left for future research. In other articles it is the other way around. Ideas and perceptions in financial markets are the driving force of the research but they do not necessarily rely on innovation in the underlying risk models. Together, they are state-of-the-art, expert-led, up-to-date contributions, demonstrating what Risks is and what Risks has to offer: articles that focus on the central aspects of insurance and financial risk management, that detail progress and paths of further development in understanding and dealing with...risks. Asking the same type of questions (which risk allocation and mitigation should be provided, and why?) creates value from three different perspectives: the normative perspective of market regulator; the existential perspective of the financial institution; the phenomenological perspective of the individual consumer or policy holder.
medical services’ consumption --- lifestyle factors --- insurance plan --- structural equation model --- stock–bond correlation --- VIX --- economic policy uncertainty --- monetary policy uncertainty --- fiscal policy uncertainty --- agricultural commodity futures --- price discovery --- market reflexivity --- Hawkes process --- poisson autoregressive models --- contagion --- predictive monitoring --- information-based asset pricing --- Lévy processes --- gamma processes --- variance gamma processes --- Brownian bridges --- gamma bridges --- nonlinear filtering --- house price prediction --- real estate --- machine learning --- random forest --- Lévy process --- subordination --- option pricing --- risk sensitivity --- stochastic volatility --- Greeks --- time-change --- time series --- volatility --- probability-integral transform --- ARMA model --- copula
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Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.
Coins, banknotes, medals, seals (numismatics) --- cluster analysis --- equity index networks --- machine learning --- copulas --- dependence structures --- quotient of random variables --- density functions --- distribution functions --- multi-factor model --- risk factors --- OLS and ridge regression model --- python --- chi-square test --- quantile --- VaR --- quadrangle --- CVaR --- conditional value-at-risk --- expected shortfall --- ES --- superquantile --- deviation --- risk --- error --- regret --- minimization --- CVaR estimation --- regression --- linear regression --- linear programming --- portfolio safeguard --- PSG --- equity option pricing --- factor models --- stochastic volatility --- jumps --- mathematics --- probability --- statistics --- finance --- applications --- investment home bias (IHB) --- bivariate first-degree stochastic dominance (BFSD) --- keeping up with the Joneses (KUJ) --- correlation loving (CL) --- return spillover --- volatility spillover --- optimal weights --- hedge ratios --- US financial crisis --- Chinese stock market crash --- stock price prediction --- auto-regressive integrated moving average --- artificial neural network --- stochastic process-geometric Brownian motion --- financial models --- firm performance --- causality tests --- leverage --- long-term debt --- capital structure --- shock spillover
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Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.
Coins, banknotes, medals, seals (numismatics) --- cluster analysis --- equity index networks --- machine learning --- copulas --- dependence structures --- quotient of random variables --- density functions --- distribution functions --- multi-factor model --- risk factors --- OLS and ridge regression model --- python --- chi-square test --- quantile --- VaR --- quadrangle --- CVaR --- conditional value-at-risk --- expected shortfall --- ES --- superquantile --- deviation --- risk --- error --- regret --- minimization --- CVaR estimation --- regression --- linear regression --- linear programming --- portfolio safeguard --- PSG --- equity option pricing --- factor models --- stochastic volatility --- jumps --- mathematics --- probability --- statistics --- finance --- applications --- investment home bias (IHB) --- bivariate first-degree stochastic dominance (BFSD) --- keeping up with the Joneses (KUJ) --- correlation loving (CL) --- return spillover --- volatility spillover --- optimal weights --- hedge ratios --- US financial crisis --- Chinese stock market crash --- stock price prediction --- auto-regressive integrated moving average --- artificial neural network --- stochastic process-geometric Brownian motion --- financial models --- firm performance --- causality tests --- leverage --- long-term debt --- capital structure --- shock spillover
Choose an application
Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.
cluster analysis --- equity index networks --- machine learning --- copulas --- dependence structures --- quotient of random variables --- density functions --- distribution functions --- multi-factor model --- risk factors --- OLS and ridge regression model --- python --- chi-square test --- quantile --- VaR --- quadrangle --- CVaR --- conditional value-at-risk --- expected shortfall --- ES --- superquantile --- deviation --- risk --- error --- regret --- minimization --- CVaR estimation --- regression --- linear regression --- linear programming --- portfolio safeguard --- PSG --- equity option pricing --- factor models --- stochastic volatility --- jumps --- mathematics --- probability --- statistics --- finance --- applications --- investment home bias (IHB) --- bivariate first-degree stochastic dominance (BFSD) --- keeping up with the Joneses (KUJ) --- correlation loving (CL) --- return spillover --- volatility spillover --- optimal weights --- hedge ratios --- US financial crisis --- Chinese stock market crash --- stock price prediction --- auto-regressive integrated moving average --- artificial neural network --- stochastic process-geometric Brownian motion --- financial models --- firm performance --- causality tests --- leverage --- long-term debt --- capital structure --- shock spillover
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