REZERV PORTFÖY YÖNETİMİNDE İPOTEĞE DAYALI MENKUL KIYMETLERİN YERİ: RİSK VE GETİRİ AÇISINDAN BİR İNCELEME (Use of Mortgage-Backed Securities in Reserve Portfolio Management: A Risk-Return Analysis Perspective)

2010 ◽  
Author(s):  
Omer Cayirli
Keyword(s):  
Portfolio Management ◽  
Risk Return ◽  
Mortgage Backed Securities

HFTViz: Visualization for the exploration of high frequency trading data

2021 ◽  
pp. 147387162110649
Author(s):  
Javad Yaali ◽  
Vincent Grégoire ◽  
Thomas Hurtut
Keyword(s):  
Portfolio Management ◽  
High Frequency ◽  
High Speed ◽  
High Frequency Trading ◽  
Domain Experts ◽  
Risk Return ◽  
Visualization Tools ◽  
Liquidity Measures ◽  
Trading Volumes

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

Securitization Process

2019 ◽  
pp. 365-382
Author(s):  
Mark Ferguson ◽  
Joseph Mcbride ◽  
Kevin Tripp
Keyword(s):  
Capital Structure ◽  
Credit Card ◽  
Student Loans ◽  
General Structure ◽  
Commercial Real Estate ◽  
Single Family ◽  
Risk Return ◽  
Mortgage Backed Securities ◽  
Asset Backed Securities ◽  
Opening Up

The securitization process has become an essential tool that provides liquidity to firms and borrowers while opening up the breadth and depth of the capital markets to previously underserved individuals and firms. Securitized products pool illiquid, idiosyncratic assets or contracts, turn those pools into claims (bonds) with a new capital structure with differing risk-return attributes, and sell those bonds to institutional investors. Securitization began in the housing market where single-family mortgages were pooled and sold to investors as mortgage-backed securities. The securitized market has increased in size and complexity to include many other asset classes such as commercial real estate loans in commercial mortgage-backed securities, student loans, credit card debt, auto leases, equipment leases, and aircraft leases in asset-backed securities. The purpose of this chapter is to describe the participants in and the general structure of securitizations.

ProSight: New Millennium Financial Technology Portfolio Management

2017 ◽  
pp. 1-33
Author(s):  
Mark Jeffery ◽  
Chuck Olson ◽  
Robin Barnes
Keyword(s):  
Portfolio Management ◽  
Management Decision ◽  
It Projects ◽  
Web Based ◽  
It Investments ◽  
Business Objectives ◽  
Risk Return ◽  
Strategic Context ◽  
Financial Technology ◽  
New Millennium

Mergers and acquisitions (M&A) are often very complex management endeavors. Analyzes the IT component of M&A for two financial institutions. Students are tasked with assisting Mike Farrell, the CIO of New Millennium Financial (NMF), a new company created through the merger of FinStar Financial and D&L Bank, in determining the optimal combined IT portfolio. To accomplish this task the strategic business objectives of the firm must be clearly understood and the IT projects in the pipelines of both institutions analyzed. Students must make an IT portfolio management decision and answer the question: What is the optimal IT strategy and project portfolio for NMF?To apply a framework to manage a company's IT portfolio, i.e., understand the company's strategic context, develop business objectives that align with its strategy, assess IT investments, and develop a portfolio of IT projects that support the objectives. The framework is iterative, i.e., IT investments are assessed on a regular basis based on their performance and risk/return tradeoffs. Also to introduce a leading Web-based tool, ProSight, that helps managers organize IT portfolios.

Portfolio Selection Models and Their Discrimination

2011 ◽  
Vol 2 (2) ◽  
pp. 65-91 ◽  
Author(s):  
Satadal Ghosh ◽  
Sujit Kumar Majumdar
Keyword(s):  
Portfolio Selection ◽  
Portfolio Management ◽  
Stock Return ◽  
Signal To Noise Ratio ◽  
Selection Models ◽  
Markowitz Model ◽  
Risk Return ◽  
Future Return ◽  
Possibility Distributions ◽  
Portfolio Selection Model

The stochastic nature of financial markets is a barrier for successful portfolio management. Besides traditional Markowitz’s model, many other portfolio selection models in Bayesian and Non-Bayesian frameworks have been developed. Starting with the basic Markowitz model, several cardinal models are used to find optimum portfolios with select stock set. Having developed the regression model of the return of each stock with the market return, the unsystematic part of the uncertainty was used to find the optimum portfolio and efficient risk–return frontier within each portfolio selection model. The average stock return as estimated from its historical data and the forecasted stock return were used for maximizing return with quadratic programming formulation in Markowitz model. In the models involving Fuzzy probability and possibility distributions, the future return was estimated using the similarity grade of past returns. In the interval coefficient models, future return was estimated as interval variable. The optimum portfolios of different models were widely divergent and DEA was used to identify the model giving the best portfolio with higher appraisal, both overall and by peers, and least Maverick behavior. Use of Signal to Noise ratio proved equally efficient for model discrimination and yielded identical results.

Timberland Indexes and Portfolio Management

1990 ◽  
Vol 14 (3) ◽  
pp. 119-124 ◽  
Author(s):  
F. Christian Zinkhan ◽  
Kossuth Mitchell
Keyword(s):  
Performance Evaluation ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Financial Assets ◽  
Southern Pine ◽  
Investment Performance ◽  
Risk Return ◽  
Efficient Portfolios ◽  
The Mean ◽  
Mean Risk

Abstract This paper explores two timberland index applications: asset allocation and investment performance evaluation. The Southern Timberland Index Fund (STIF), a southern pine index fund, is adopted for use in these applications. In the asset allocation application, the mean risk of risk-return efficient portfolios containing financial assets and the STIF is discovered to be 43% less than the mean risk of the efficient portfolios containing only financial assets. Efficient portfolios contain the STIF in proportions as high as almost 30%. As far as performance is concerned, a timberland index is suggested for use as a benchmark for evaluating (1) timberland investment managers and (2) the investment performance of timberland versus other investment alternatives. Before such applications become commonplace, it is concluded that problems associated with existing timberland indexes be addressed. South. J. Appl. For. 14(3):119-124.

scholarly journals Investing with Cryptocurrencies—a Liquidity Constrained Investment Approach*

2019 ◽  
Vol 18 (2) ◽  
pp. 280-306 ◽  
Author(s):  
Simon Trimborn ◽  
Mingyang Li ◽  
Wolfgang Karl Härdle
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Liquidity Constraints ◽  
Dark Side ◽  
Weight Optimization ◽  
Trade Off ◽  
Risk Return ◽  
Out Of Sample ◽  
Bounded Risk ◽  
Object Of Study

Abstract Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have low liquidity compared to traditional assets, one needs to take into account liquidity issues when adding them to a portfolio. We propose a Liquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of risk-return portfolio optimization under liquidity constraints. Cryptocurrencies are included in portfolios formed with stocks of the S&P 100, US Bonds, and commodities. We illustrate the importance of the liquidity constraints in an in-sample and out-of-sample study. LIBRO improves the weight optimization in the sense that it only adds cryptocurrencies in tradable amounts depending on the intended investment amount. The returns greatly increase compared to portfolios consisting only of traditional assets. We show that including cryptocurrencies in a portfolio can indeed improve its risk–return trade-off.

scholarly journals Volatility clustering, risk-return relationship and asymmetric adjustment in the Finnish housing market

2020 ◽  
Vol 13 (4) ◽  
pp. 661-688 ◽  
Author(s):  
Josephine Dufitinema
Keyword(s):  
Housing Market ◽  
Portfolio Management ◽  
House Price ◽  
Significant Risk ◽  
Price Volatility ◽  
Asymmetric Adjustment ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Risk Return ◽  
Autoregressive Conditional Heteroscedasticity

Purpose The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types. Design/methodology/approach Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas. Findings Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types. Research limitations/implications The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study. Originality/value To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

The Return of the Loan: Commercial Mortgage Investing after the 2008 Financial Crisis

2017 ◽  
pp. 1-35
Author(s):  
Craig Furfine ◽  
Mike Fishbein
Keyword(s):  
Vice President ◽  
Cash Flows ◽  
List Type ◽  
Mortgage Loans ◽  
Risk Return ◽  
Mortgage Backed Securities ◽  
Direct Lending ◽  
The 2008 Financial Crisis ◽  
Benefits And Costs ◽  
Commercial Mortgages

Zoe Greenwood, vice president at Foundation Investment Advisors, was glancing through the offering memorandum for a new commercial mortgage-backed securities (CMBS) deal on April 1, 2010, a time when the opportunities for commercial mortgage investors had been bleak to the point of comical. This new CMBS deal represented the first opportunity to buy CMBS backed by loans to multiple borrowers since credit markets had shut the securitization pipeline in June 2008.The offering gave Greenwood a new investment opportunity to suggest to her firm's latest client. She had planned to recommend an expansion in her client's traditional commercial mortgage business, but these new bonds looked intriguing. Could the new CMBS offer her client a superior risk-return tradeoff compared with making individual mortgage loans?After students have analyzed the case they will be able to: –Learn how to construct promised cash flows from both commercial mortgages and commercial mortgage-backed securities –Understand the benefits and costs of direct lending versus indirect lending (purchase of mortgage-backed bonds) –Underwrite commercial mortgage loans issued by others to identify potentially hidden risks –Evaluate at what price a mortgage-bond investment makes financial sense

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