Sökning: "finansiella tidsserier"

Visar resultat 1 - 5 av 31 uppsatser innehållade orden finansiella tidsserier.

  1. 1. The Time-Varying Correlation between Regional Home Prices and The Impact of Central Bank Balance Sheet Policies on Home Prices : A Graphical Descriptive Statistics Approach on The US Housing Market

    Master-uppsats, KTH/Fastighetsföretagande och finansiella system

    Författare :Claudia Patricia Moros Martinez; [2023]
    Nyckelord :Quantitative easing; Quantitative tapering; Quantitative Research; Stock Prices; Real Estate; Central Banks; Federal Reserve; COVID-19; Kvantitativa lättnader; Kvantitativ nedtrappning; Kvantitativ forskning; Aktiekurser; Fastigheter; Centralbanker; Federal Reserve; COVID-19;

    Sammanfattning : There has been a growing interest in economic policies and their impact within a country among the real estate economics research community in recent years. After the economic crisis of 2008, an unconventional monetary policy was created, and it has been called quantitative easing (QE), an instrument of economic policy applied through central banks to boost the economy in periods when conventional monetary policy is not satisfactory. LÄS MER

  3. 2. Credit Index Forecasting: Stability of an Autoregressive Model

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Melker Wallén; Erik Grimlund; [2023]
    Nyckelord :Credit spreads; Time Series; Credit Risk; Index Modeling; Forecasting; Kreditspreadar; Tidsserier; Kreditrisk; Indexmodellering; Prognoser;

    Sammanfattning : This thesis investigates the robustness and stability of total return series for credit bond index investments. Dueto the challenges which arise for financial institutes and investors in achieving these objectives, we aim to createa forecasting model which matches the statistical properties of historical data, while remaining robust, stable andeasy to calibrate. LÄS MER

  4. 3. Portfolio Risk Modelling in Venture Debt

    Master-uppsats, KTH/Matematisk statistik

    Författare :John Eriksson; Jacob Holmberg; [2023]
    Nyckelord :Startup Default Probability; Venture Debt; Gaussian Copula; Value-at-Risk; Expected Shortfall; Exposure at Default; Loss Given Default; Forecast; Linear Dynamic System; ARIMA Time Series; Monte Carlo Simulation; Linear Regression; Central Limit Theorem;

    Sammanfattning : This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. LÄS MER

  5. 4. Evaluating clustering techniques in financial time series

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

    Författare :Johan Millberg; [2023]
    Nyckelord :clustering; machine learning; financial time series; time series; unsupervised learning; cluster validation; cluster evaluation; klustring; klusteranalys; finansiella tidsserier; maskininlärning; klustervalidering; evalueringsteknik;

    Sammanfattning : This degree project aims to investigate different evaluation strategies for clustering methodsused to cluster multivariate financial time series. Clustering is a type of data mining techniquewith the purpose of partitioning a data set based on similarity to data points in the same cluster,and dissimilarity to data points in other clusters. LÄS MER

  7. 5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Isabella Mustén Ross; [2023]
    Nyckelord :Deep Learning; Long-Short-Term-Memory LSTM ; ARIMA; Financial Time Series Forecasting; Algorithmic Trading; Intraday Trading; Stock Prediction; Djupinlärning; LSTM; ARIMA; finansiella tidsserier; algoritmisk aktiehandel; intradagshandel; aktieprediktion;

    Sammanfattning : Deep learning techniques have exhibited remarkable capabilities in capturing nonlinear patterns and dependencies in time series data. Therefore, this study investigates the application of the Long-Short-Term-Memory (LSTM) algorithm for stock price prediction in intraday quantitative trading using Swedish stocks in the OMXS30 index from February 28, 2013, to March 1, 2023. LÄS MER