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Showing 1 to 10 of 10 for “"Volatility Estimation"”.

  1. Point process based high frequency volatility estimation : theory and applications

    … common theme: point process based high-frequency volatility estimation. The first chapter introduces a new class of high-frequency volatility estimators and examines its asymptotic properties. The second chapter studies the relative importance of market microstructure (MMS) variables on …

    lancaster Repository record for Point process based high frequency volatility estimation : theory and applications (opens in a new tab)

  2. Business cycles, interest rates and market volatility : estimation and forecasting using DSGE macroeconomic models under partial information

    … can be extended and suited to both, fitting and estimation of long-term yield curve, and to estimating with rich data sets by extending further its inner-mechanism. In the aftermath of the 2008 crises, which struck at the beginning of this research project, and the subsequent, extensive criticism …

    london-metro Repository record for Business cycles, interest rates and market volatility : estimation and forecasting using DSGE macroeconomic models under partial information (opens in a new tab)

  3. Empirical essays in financial economics

    Paper 1 focuses on implied volatility estimation and investigates the volatility smile in a South African context with fourteen stocks listed on JSE Limited and fifty-nine options on these underlying stocks for the period April 4, 2002 to November 8, 2008. Paper 2 uses an empirical approach, based …

    cape-town Repository record for Empirical essays in financial economics (opens in a new tab)

  4. Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks

    … & Poor's (S&P) 500 Index using five different volatility estimation methods. Moreover, it then compares the forecasts of the two parametrised models to a deep feed-forward artificial neural network which is also used to price such options. Overall, the artificial neural network is statistically …

    cape-town Repository record for Option pricing and machine learning: a comparison of black-scholes, bachelier, and artificial neural networks (opens in a new tab)

  5. Estrategias de trading con Time Series Momentum

    … a time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the e ciency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 18 assets from May 2017 to …

    rosario Repository record for Estrategias de trading con Time Series Momentum (opens in a new tab)

  6. Parameter learning with particle filters

    … been applied to regime-shifting and stochastic volatility models. Numerical and graphical evidence of parameter and volatility estimation will be provided under regime-shifting parameters for the Heston (1993) stochastic volatility model. The filter demonstrates rapid adaptation in estimating …

    cape-town Repository record for Parameter learning with particle filters (opens in a new tab)

  7. A Comparison Between Break-Even Volatility and Deep Hedging For Option Pricing

    … paid in particular to the assumption of constant volatility, which does not hold in practice (Yalincak, 2012). The standard in industry is to use various volatility estimation and parameterisation techniques when pricing to more closely recover the market-implied volatility skew. One such …

    cape-town Repository record for A Comparison Between Break-Even Volatility and Deep Hedging For Option Pricing (opens in a new tab)

  8. Nonparametric Methods in Financial Time Series Analysis

    … considered. Chapter 2 is about nonparametric estimation of conditional moments. We propose a local constant type estimator that operates with an infinite number of conditioning variables; this enables a direct estimation of many objects of econometric interest that have dependence upon the …

    cambridge Repository record for Nonparametric Methods in Financial Time Series Analysis (opens in a new tab)

  9. Volatility Modeling Using the Student's t Distribution

    Over the last twenty years or so the Dynamic Volatility literature has produced a wealth of univariate and multivariate GARCH type models. While the univariate models have been relatively successful in empirical studies, they suffer from a number ofweaknesses, such as unverifiable parameter …

    vt Repository record for Volatility Modeling Using the Student's t Distribution (opens in a new tab)