Sökning: "Volatility forecasting"

Visar resultat 1 - 5 av 96 uppsatser innehållade orden Volatility forecasting.

  1. 1. Portfolio Performance Optimization Using Multivariate Time Series Volatilities Processed With Deep Layering LSTM Neurons and Markowitz

    Master-uppsats, KTH/Matematisk statistik; KTH/Matematisk statistik

    Författare :Aron Andersson; Shabnam Mirkhani; [2020]
    Nyckelord :Recurrent Neural network RNN ; long short-term memory LSTM ; portfolio optimization; markowitz; exponential moving average; sharpe ratio; heteroskedasticity; Markowitz;

    Sammanfattning : The stock market is a non-linear field, but many of the best-known portfolio optimization algorithms are based on linear models. In recent years, the rapid development of machine learning has produced flexible models capable of complex pattern recognition. LÄS MER

  2. 2. Volatility forecasting using the GARCH framework on the OMXS30 and MIB30 stock indices

    Kandidat-uppsats, Göteborgs universitet/Institutionen för nationalekonomi med statistik

    Författare :Peter Johansson; [2019-01-22]
    Nyckelord :Volatility forecasting; Random Walk; Moving Average; Exponentially Weighted Moving Average; GARCH; EGARCH; GJR-GARCH; APGARCH; volatility model valuation; regression; information criterion;

    Sammanfattning : There are many models on the market that claim to predict changes in financial assets as stocks on the Stockholm stock exchange (OMXS30) and the Milano stock exchange index (MIB30). Which of these models gives the best forecasts for further risk management purposes for the period 31st of October 2003 to 30th of December 2008? Is the GARCH framework more successful in forecasting volatility than more simple models as the Random Walk, Moving Average or the Exponentially Weighted Moving Average?The purpose of this study is to find and investigate different volatility forecasting models and especially GARCH models that have been developed during the years. LÄS MER

  3. 3. Portfolio Optimization : A DCC-GARCH forecast with implied volatility

    Magister-uppsats, Linnéuniversitetet/Institutionen för ekonomistyrning och logistik (ELO); Linnéuniversitetet/Institutionen för ekonomistyrning och logistik (ELO)

    Författare :Sam Bigdeli; Filip Bengtsson; [2019]
    Nyckelord :DCC-GARCH; Portfolio Optimization; Certainty Equivalence Tangency; CET; Global Minimum Variance; GMV; Minimum Conditional Value-at-Risk; MinCVaR; Implied volatility index; VIX;

    Sammanfattning : This thesis performs portfolio optimization using three allocation methods, Certainty Equivalence Tangency (CET), Global Minimum Variance (GMV) and Minimum Conditional Value-at-Risk (MinCVaR). We estimate expected returns and covariance matrices based on 7 stock market indices with a DCC-GARCH model including an ARMA (1. LÄS MER

  4. 4. Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model

    Kandidat-uppsats, Mälardalens högskola/Akademin för utbildning, kultur och kommunikation

    Författare :Renato Rios Benavides; [2019]
    Nyckelord :;

    Sammanfattning : Stochastic volatility models have become essential for financial modelling and forecasting. The present thesis works with a two-factor stochastic volatility model that is reduced to four parameters. LÄS MER

  5. 5. Predicting Exchange Rate Value-at-Risk and Expected Shortfall: A Neural Network Approach

    Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen

    Författare :Anna Bijelic; Tilila Ouijjane; [2019]
    Nyckelord :Value-at-Risk; Expected Shortfall; Recurrent Neural Networks; GRU; GARCH 1; 1 ; Exchange Rate Volatility; Intra-day Data; Business and Economics;

    Sammanfattning : On the basis of the recommendation of the Basel Committee on Banking Supervision to transition from Value-at-Risk (VaR) to Expected Shortfall (ES) in determining market risk capital, this paper attempts to investigate whether a Recurrent Neural Network provides more accurate VaR and ES predictions of the EUR/USD exchange rate compared to the conventional GARCH(1,1) model. A number of previous studies has confirmed the forecasting ability of a plain vanilla Feedforward Neural Network over traditional statistical models. LÄS MER