Understanding Portfolio Risk Analysis through Cumulative Distribution Functions

Jan 05, 2025

70 likes | 102 Views

Explore how cumulative distribution functions impact portfolio risk analysis over various time horizons. Analyze density and cumulative distribution functions to assess risk using VaR calculations.

Share Presentation

Embed Code

Link

portfolio risk analysis
cumulative distribution function
var
density function

gurney

Download Presentation

Understanding Portfolio Risk Analysis through Cumulative Distribution Functions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Figure 4.1. Using the cumulative distribution function
Figure 4.2. Frequency distribution of two portfolios over one year horizon
Figure 4.3. Normal and lognormal density and cumulative distribution functions Panel A. Density distribution functions
Figure 4.3. Normal and lognormal density and cumulative distribution functions Panel B. Cumulative distribution functions
Figure 4.4. VaR as a function of trade size

Cumulative Distribution Functions

Cumulative Distribution Functions. The cumulative distribution function of a random variable X , written, F , indicates the probability that is at and to the left of each point x . CDFs are defined for both discrete and continuous random variables. Properties of a CDF.

2.13k views • 4 slides

Figure 4.1

Figure 4.1.

460 views • 29 slides

Cumulative distribution [%]

Cumulative p-values for MLM_1 with Q5-DArT + K-DArT . Cumulative distribution [%]. (a). Cumulative p-values for MLM_2 with Q5-SSR + K-DArT . Cumulative distribution [%]. (b). Cumulative p-values for MLM_3 with Q5-DArT + K-SSR . Cumulative distribution [%]. (c).

158 views • 5 slides

SEHS Topic 4.1 Neuromuscular Function

SEHS Topic 4.1 Neuromuscular Function. Label a diagram of a motor unit. Foundation required: A motor unit is made up of a motor neuron and all of the skeletal muscle fibers innervated by that axon. What’s neuron? It’s the functional unit of the nervous system (a.k.a. a nerve cell).

554 views • 14 slides

Using the VLookup Function

Using the VLookup Function. Use a cell value as a KEY to a table to find and return a specific element to another cell or use in a calculation. Lookup Tables and Functions. Lookup Tables and Functions.

455 views • 9 slides

Figure 4.1. Measurement framework.

Figure 4.1. Measurement framework. Level One: Program evaluation Who: Children, environments, educators/caregivers/consultants What: Observations of children’s progress across broad developmental and discipline- specific domains/areas; examination of learning environments; assessment of

133 views • 2 slides

Figure 4.1

Figure 4.1. Figure 4.2a. Figure 4.2b. Figure 4.2c. Figure 4.2d. Figure 4.2e. Figure 4.2f. Figure 4.3. Figure 4.4. Figure 4.5. Figure 4.6. Figure 4.7. Figure 4.8. Figure 4.9a. Figure 4.9b. Figure 4.9c. Figure 4.9d. Figure 4.9e. Figure 4.9f. Figure 4.9g. Figure 4.9h. Figure 4.9i.

420 views • 33 slides

Cumulative Frequency distribution

271 views • 2 slides

Figure 4.1 Customer satisfaction

Figure 4.1 Customer satisfaction. Figure 4.2 The satisfaction continuum. Figure 4.3 Simplified gap model. Table 4.1 Reasons for gaps. Figure 4.4 Range of expectations. Figure 4.5 Range of expectations and the zone of tolerance or acceptable outcomes.

831 views • 23 slides

Distribution Channels Lesson 4.1

Distribution Channels Lesson 4.1. The Distribution Function. “Marketing” relatively new term…only used in last 50 – 60 years. Prior to “marketing,” companies focused on distribution when they wanted to improve exchange process. Today, distribution is just one of many marketing activities.

420 views • 17 slides

Figure 4.1

Figure 4.1. Figure 4.2. Figure 4.3. Figure 4.4. Start of header. Ident = x. 0. Offset = 0. Rest of header. (a). 1400 data bytes. Start of header. Ident = x. 1. Offset = 0. Rest of header. 512 data bytes. (b). Start of header. Ident = x. 1. Offset = 64. Rest of header.

703 views • 61 slides

Figure 3 Global Cumulative Treatment Analysis (GCTA)

Figure 3 Global Cumulative Treatment Analysis (GCTA). Shrager, J. & Tenenbaum, J. M. ( 2014) Rapid learning for precision oncology Nat. Rev. Clin. Oncol. doi:10.1038/nrclinonc.2013.244.

176 views • 1 slides

Survival distribution function

REGARD: Time to First Relapse. 1.00. 672 days (96 weeks). 0.75. IFNß-1a. Survival distribution function. 0.50. GA. HR (95% Cl): 0.943 (0.74, 1.21) P =0.643. 0.25. 0.00. 0. 100. 200. 300. 400. 500. 600. 700. Time to relapse (days).

173 views • 2 slides

A cumulative distribution function-based method for yield optimization of CMOS ICs

A cumulative distribution function-based method for yield optimization of CMOS ICs. M . Yakupov*, D . Tomaszewski Division of Silicon Microsystem and Nanostructure Technology, Institute of Electron Technology, Warsaw, Poland * Currently MunEDA GmbH, Munich, Germany. Outline. Introduction

335 views • 25 slides

Using the Memory Function

Using the Memory Function. 9. Press “Recall” again, this is the time of occurrence (00:00 is your ob time). 10. Press “Recall” again, this is the Min for the period. 11. Press “Recall” again, this is the time of occurrence (00:00 is your ob time).

73 views • 1 slides

Figure 1 Probability Distribution

Probability. Probability. dist. r. i. b. ution. dist. r. i. b. ution. Figure 1 Probability Distribution. (a) Less Ce. r. tain. (b) More Ce. r. tain. , %. , %. 40. 40. y. y. Probabilit. Probabilit. 30. 30. 20. 20. 10. 10. 20%. 40%. 20%. 10%. 30%. 40%. 30%.

219 views • 6 slides

On Estimation of the Cumulative Hazard Function

On Estimation of the Cumulative Hazard Function. Consuelo Garcia Dorian Smith Chris Summitt Angela Watson. Introduction. Investigated ordering survival curves subject to a constraint on the ratio of cumulative hazard functions (CHF) Constraint

137 views • 2 slides

4.1 What Is a Function?

4.1 What Is a Function?. Section 4.1 p 1.

1.07k views • 79 slides

Using the Memory Function

Using the Memory Function.

146 views • 1 slides

Figure 4.1 : The Xootr by Novacruz

Figure 4.1 : The Xootr by Novacruz. Weekly demand. 1400. 1200. 1000. 800. 600. 400. 200. 0. Figure 4.2. : Lifecycle demand trajectory for Xooters. July. May. April. June. March. August. January. October. November. December. September. Components. Finished Xootrs.

120 views • 10 slides

Lecture 4.1 Ambiguity Function

Lecture 4.1 Ambiguity Function. Understanding of SISO, SIMO, MISO and MIMO (note that the terms input and output refer to the radio channel carrying the signal, not to the devices having antennas). Radar Ambiguity Function. t : delay n : Doppler. u(t- t )e j2 pn t. u(t).

486 views • 30 slides

Figure 4.1 Performance improvement as a function of cost.

1. Table 4.1 Key characteristics of six passenger aircraft: all figures are approximate; some relate to a specific model/configuration of the aircraft or are averages of cited range of values. Figure 4.1 Performance improvement as a function of cost.

194 views • 18 slides

More Related