Sökning: "volatility prediction model"
Visar resultat 1 - 5 av 19 uppsatser innehållade orden volatility prediction model.
- Master-uppsats, KTH/Matematisk statistik
Sammanfattning : In this thesis, we apply unsupervised and supervised statistical learning methods on the high-yield corporate bond market with the goal of predicting its future excess return. We analyse the excess return of industry based indices of high-yield corporate bonds belonging to the Chemical, Metals, Paper, Building Materials, Packaging, Telecom, and Electric Utility industry. LÄS MER
- Master-uppsats, Stockholms universitet/Nationalekonomiska institutionen
Sammanfattning : Predicting volatility of financial assets based on realized volatility has grown popular in the literature due to its strong prediction power. Theoretically, realized volatility has the advantage of being free from measurement error since it accounts for intraday variation that occurs on high frequencies in financial assets. LÄS MER
- Master-uppsats, Lunds universitet/Matematisk statistik
Sammanfattning : This thesis aims at developing and evaluating a model for high frequency foreign exchange data, that beats the TWAP benchmark the majority of the time. This is done by dividing the total order time into smaller time buckets and trading a smaller quantity of the total order volume in each bucket. LÄS MER
4. Capital structure's influence on volatility on in times of financial distress : An investigation on capital structure as a volatility influencer before, during and after the European debt crisis on the Stockholm Stock ExchangeUppsats för yrkesexamina på avancerad nivå, Umeå universitet/Företagsekonomi; Umeå universitet/Företagsekonomi
Sammanfattning : The financial crisisand the European debt crisis wreaked havoc on many European economies and stock markets. Previous studies have shown that crises are associated with high debt and linked with lower growth. LÄS MER
5. Predicting Corporate Credit Ratings: A Comparative Study Between Ordered Probit, Neural Network and Random ForestC-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi
Sammanfattning : This thesis compares the prediction accuracy for corporate credit ratings between three different models. The two first models, a traditional statistical model called ordered probit and a machine learning model called artificial neural network has been used with success before. LÄS MER
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