Sökning: "Diebold-Mariano Test"

Visar resultat 1 - 5 av 13 uppsatser innehållade orden Diebold-Mariano Test.

1. 1. Volatility Forecasting - A comparative study of different forecasting models.

   Kandidat-uppsats,

   Författare :Emil Sturesson; Anton Wennström; [2023-06-29]
   Nyckelord :Volatility; GARCH; EGARCH; t-GAS; HAR-RV; Realized GARCH; Volatility Forecasting; Volatility Modelling;

   Sammanfattning : This study evaluates the out-of-sample forecasting performance of different volatility mod- els. When applied to XACT OMXS30, we use GARCH(1,1), EGARCH(1,1), and t- GAS(1,1) to forecast squared daily returns while Realized GARCH(1,1) and HAR-RV are used to forecast Realized Variance. LÄS MER

3. 2. Forecasting Volatility of Ether- An empirical evaluation of volatility models and their capacity to forecast one-day-ahead volatility of Ether

   Master-uppsats, Göteborgs universitet/Graduate School

   Författare :Johannes Marmdal; Adam Törnqvist; [2023-06-29]
   Nyckelord :Forecast; Volatility; Ether; GARCH; EWMA; SMA;

   Sammanfattning : This study evaluates the performance of volatility models in forecasting one-day-ahead volatility of the cryptocurrency Ether. The selected models are: GARCH, EGARCH, GJR-GARCH, SMA9, SMA20, and EWMA. We investigate both in-sample performance and out-of-sample performance. LÄS MER

4. 3. A comparison of forecasting techniques: Predicting the S&P500

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Axel Neikter; Nils Sjöberg; [2023]
   Nyckelord :Forecasting; machine learning; random forest; arima;

   Sammanfattning : Accurately predicting the S\&P 500 index means knowing where the US economy is heading. If there was a model that could predict the S\&P 500 with even some accuracy, this would be extremely valuable. Machine learning techniques such as neural network and Random forest have become more popular in forecasting. LÄS MER

5. 4. Using Machine Learning to Predict Aggregate Excess Returns

   D-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

   Författare :Erik Jonsson; Sebastian Gierlowski Carling; [2021]
   Nyckelord :Equity premium; Machine learning; Non-linear models; Penalized linear models; Prediction;

   Sammanfattning : In this paper we examine whether standard linear regression and machine learning tools can be used to predict the time series of total returns in excess of the risk-free rate on the S&P500 and FTSE100 indices. We have virtually no success in predicting monthly returns. However, we do have some success in predicting annual returns. LÄS MER

7. 5. Forcasting the Daily Air Temperature in Uppsala Using Univariate Time Series

   Magister-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Noah Aggeborn Leander; [2020]
   Nyckelord :ARIMA; naïve; neural network; SMHI; walk forward validation; Diebold-Mariano test.;

   Sammanfattning : This study is a comparison of forecasting methods for predicting the daily maximum air temperatures in Uppsala using real data from the Swedish Meteorological and Hydrological Institute. The methods for comparison are univariate time series approaches suitable for the data and represent both standard and more recently developed methods. LÄS MER