 Average Return and Relevant Volatility 25 яВЧConsequently, investors will all hold portfolios that contain only market volatility; we can ignore the ei component of a stockтАЩs volatility. яВЧEach asset contributes to the volatility of the portfolio return in proportion to its own bi and this is the only part of the assetтАЩs volatility that is relevant. яВЧIf we relate the average return on assets and asset classes to their relevant volatility, we find a reasonably good fit. яВЧFor asset classes which are already diversified, the relevant volatility is simply their own return volatility. яВЧFor individual assets, the relevant volatility is that part of their volatility that cannot be diversified, that is their bi times the volatility of the market portfolio. яВЧAn assetтАЩs bi is called its beta (written b) and its average return in a market where assets are properly priced will be proportional to its beta risk. яВЧIf we plot the betas of single assets against their average returns, they should form a straight line яВЧThe model that describes this relationship is called the Capital Asset Pricing Model тАУ the CAPM.
View full slide show Volatility and Correlations тАв We have already seen that we can easily estimate the volatility and correlation using Excel functions STDEV and CORREL. The variance is defined as the square of the volatility (or standard deviation). тАв Similar to the case of the returns, it is conventional to express the volatility in an annual basis. тАв Annual Volatility = sqrt(12) [Monthly Volatility]. тАв Annual Volatility = sqrt(260)[Daily Volatility]. тАв For example, a daily volatility of 1% implies an annual volatility of about 16%. тАв Recently, we have been observing daily fluctuations of about 1.5% - what does that imply about the annual volatility? 34
View full slide show A bad argument for hedging Since investors donтАЩt like volatility, one argument that is often offered for hedging at the firm level is simply to reduce firm return volatility. If investors prefer lower volatility, shouldnтАЩt it be a good thing if firms also try to reduce their cashflow volatility? The answer is twofold: One, some firm volatility is diversifiable, and will ultimately not show up in investorsтАЩ portfolio return volatility. Most firm level volatility that firm managers can control is of this nature. However, investors and portfolio managers can diversify idiosyncratic risk better and cheaper than firm managers. Two, except for extremely large firms (like GM in its heyday, perhaps), firms cannot affect economy-level non-diversifiable volatility in any appreciable manner. Firm manager focus should be on the primary activities of the firm, on increasing average returns on assets rather than reducing volatility per se. So if portfolio management can better manage investor return volatility, is there, then, a role for volatility reduction at the firm level?
View full slide show Reference Slide # Slide Title Source of Information 6 Transaction Validation CISA: page 225 Exhibit 3.30 7 Batch Processing CISA: page 223, 224, 393 8 Batch Control CISA: page 223, 224 9 Transaction Authorization CISA: page 223 10 Error Handling Alternatives CISA: page 224 12 Processing Controls CISA: page 224, 225 13 Data File Control Procedures CISA: page 225, 226 19 Testing Application: Test Data CISA: page 229 Exhibit 3.32 20 Testing Application: Snapshot CISA: page 229 Exhibit 3.32 21 Integrated Testing Facilities CISA: page 230 Exhibit 3.32 22 Parallel Operation or Parallel Simulation CISA: page 230 Exhibit 3.32 23 Transaction Selection Program CISA: page 230 Exhibit 3.32 24 Embedded Audit Data Collection CISA: page 230 Exhibit 3.32 25 Testing Application Techniques CISA: page 229, 230 Exhibit 3.32 26 Online Auditing Techniques CISA: page 230, 231 27 Concurrent Audit Tools CISA: page 231 Exhibit 3.33 28 Continuous Online Auditing: Audit Hooks CISA: page 230, 231
View full slide show Some Conclusions (6/6) тАв 2. As the correlation decreases, the more we can reduce the portfolio volatility. However, it takes more assets to bring down the portfolio volatility to its theoretical minimum. тАв Example: if the correlation is 0.9 and the average volatility of each stock in the portfolio is 40%, then the lowest portfolio volatility that is possible is about 37.95%. We can reach within 0.5% of this minimum volatility by creating a portfolio of only 4 assets. Suppose instead that the average correlation is 0.5. Then the lowest possible portfolio volatility is 28.28%; however, to reach within 0.5% of this value, we need as many as 30 stocks. 34
View full slide show Some Conclusions (2/2) тАв 2. As the correlation decreases, the more we can reduce the portfolio volatility. However, it takes more assets to bring down the portfolio volatility to its theoretical minimum. тАУ Suppose the average correlation is 0.9, and the average volatility of each stock in the portfolio is 40%, then the lowest portfolio volatility that is possible is about 37.95%. We can reach within 0.5% of this minimum volatility by creating a portfolio of only 4 assets. тАУ Suppose instead that the average correlation is 0.5. Then the lowest possible portfolio volatility is 28.28%; However, to reach within 0.5% of this value, we need as many as 30 stocks.
View full slide show The M Square [2/3] тАв The M square measures the difference in the return of the portfolio P and the benchmark M, when portfolio P is mixed with a riskfree asset to make the volatility of portfolio (P + riskfree) the same as the volatility of the benchmark. тАв The M2 answers the following question: if the investor wants the same volatility as the benchmark, then how much worse or better would the investor do by investing in the actively managed portfolio? тАв Recall that portfolio P has a volatility of 42% and benchmarkтАЩs volatility is 30%. тАв We create a portfolio of w=0.714 in P and 0.286 in the riskfree asset. This portfolio now has a volatility of (0.714) (42)=30%.
View full slide show Why Why Do Do Structures Structures Differ? Differ? тАУтАУ Environment Environment Key KeyDimensions: Dimensions: тАв тАв Capacity: Capacity:the thedegree degreeto to which whichan anenvironment environment can cansupport supportgrowth. growth. тАв тАв Volatility: Volatility:the thedegree degreeof of instability instabilityininthe the environment. environment. тАв тАв Complexity: Complexity:the thedegree degree of ofheterogeneity heterogeneityand and concentration concentrationamong among environmental environmental elements. elements.
View full slide show Classification of complexity Complexity ╬Ш(1) ╬Ш(log n) ╬Ш(log n)c ╬Ш(n) ╬Ш(nc) ╬Ш(bn) (b>1) ╬Ш(n!) Terminology Constant complexity Logarithmic complexity Poly-logarithmic complexity Linear complexity Polynomial complexity Exponential complexity Factorial complexity We also use such terms when ╬Ш is replaced by O (big-O)
View full slide show Using a One-Dimensional Array Lesson A Objectives After completing this lesson, you will be able to: яВз Declare and initialize a one-dimensional array яВз Assign data to a one-dimensional array яВз Display the contents of a one-dimensional array яВз Access an element in a one-dimensional array яВз Search a one-dimensional array яВз Compute the average of a one-dimensional arrayтАЩs contents яВз Find the highest entry in a one-dimensional array яВз Update the contents of a one-dimensional array яВз Sort a one-dimensional array 2
View full slide show The The Three Three Dimensional Dimensional Model Model of of the the Environment Environment Volatility Capacity Complexity EXHIBIT 15-10
View full slide show Exposure of an MNCтАЩs Portfolio яБо Measurement of currency volatility (Exhibit 10.4) The standard deviation statistic measures the degree of movement for each currency. In any given period, some currencies clearly fluctuate much more than others. яБо Currency volatility over time (Exhibit 10.5) The volatility of a currency may not remain consistent from one time period to another. An MNC can identify currencies whose values are most likely to be stable or highly volatile in the future. яБо Measurement of currency correlations (Exhibit 10.6) 11 The correlations coefficients indicate the degree to which two currencies move in relation to each other. ┬й 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use.
View full slide show Reference Slide # Slide Title Source of Information 6 Risk Management Process CISM: page 97 Exhibit 2.2 8 Continuous Risk Mgmt Process CISM: page 97 Exhibit 2.3 9 Security Evaluation: Risk Assessment CISM: page 100 12 Matric of Loss Scenario CISM: page 114 Exhibit 2.15 14 Step 2: Determine Loss Due to Threats CISM: page 105 16 Step 2: Determine Threats Due to Vulnerabilities CISM: page 105 17 Step 3: Estimate Likelihood of Exploitation CISM: page 107-110 18 Likelihood of Exploitation Sources of Losses CISM: page 118 Exhibit 2.11 19 Step 4; Compute Expected Loss Risk Analysis Strategies CISM: page 108- 110 20 Step 4: Compute Loss Using Qualitative Analysis CISM: page 108 22 Step 4: Compute Loss Using Semi- Quantitative Analysis CISM: page 108,109 23 SemiQuantitative Impact Matrix CISM: page 109 Exhibit 2.12 24 Step 4: Compute Loss Using Quantitative Analysis CISM: page 109, 110 26 Annualized Loss Expectancy CISM: page 110 28 Step 5: Treat Risk CISM: page 110, 111 29 NIST Risk Assessment Methodology CISM: page 102 Exhibit 2.7 30 Control Types CISM: page 186 Exhibit 3.18 32 Controls & Countermeasures CISM: page 184, 185 36 Security Control Baselines & Metrics CISM: page 191-193 37 Risk Management CISM: page 91, 92 38 Risk Management Roles CISM: page 94
View full slide show COLT Conclusions The PAC framework provides a reasonable model for theoretically analyzing the effectiveness of learning algorithms. The sample complexity for any consistent learner using the hypothesis space, H, can be determined from a measure of HтАЩs expressiveness (|H|, VC(H)) If the sample complexity is tractable, then the computational complexity of finding a consistent hypothesis governs the complexity of the problem. Sample complexity bounds given here are far from being tight, but separate learnable classes from non-learnable classes (and show whatтАЩs important). Computational complexity results exhibit cases where information theoretic learning is feasible, but finding good hypothesis is intractable. The theoretical framework allows for a concrete analysis of the complexity of learning as a function of various assumptions (e.g., relevant variables) COLT CS446 -SPRING тАШ17
View full slide show Query volatility тАв Query results volatility: spam-prone queries are likely to produce semantically incoherent results over time тАв Query impressions volatility: buzzy queries are less likely to be spam-prone тАв Query clicks volatility: click-through densities on different search results positions are more consistent for less spam-prone queries тАв Query sessions volatility: users are less likely to be satisfied with search results and click on them for spam-prone queries
View full slide show Traditional volatility modeling яБ▒ яБ▒ Mainstream ARCH, GARCH, and similar models focus on fundamental/informational volatility. яВз Statistically: volatility in the unit-root component of prices. яВз Economically important for portfolio allocation, derivatives valuation and hedging. Quote volatility is non-informational яВз Statistically: short-term, stationary, transient volatility яВз Economically important for trading and market making. 10
View full slide show Portfolio of Risky + Riskless Asset тАв To calculate the portfolio return and portfolio variance when we combine the risky asset and riskless asset, we can use the usual formulas, noting that the volatility of the riskfree rate is zero. тАв Portfolio Return = w1 Rf + w2 Rp. тАв Portfolio Variance = (w1)^2 (0) + (w2 )^2 (vol of risky asset)^2 + 2 (correlation) (w1 )(w2 ) (0)(vol of risky asset). тАв Portfolio Volatility = w2 *(vol of risky asset). тАв This simplification in the formula for the portfolio volatility occurs because the vol of the riskfree asset is zero. тАв To understand the tradeoff between risk and return, we can graph the portfolio return vs the the portfolio volatility. тАв The following graph shows this graph for the case when the mean return for the riskfree asset is 5%, the mean return for the risky asset is 12%, and the volatility of the risky asset is 15%. 6
View full slide show Portfolio of Risky + Riskless Asset тАв To calculate the portfolio return and portfolio variance when we combine the risky asset and riskless asset, we can use the usual formulas, noting that the volatility of the riskfree rate is zero. тАв Portfolio Return = w1 Rf + w2 Rp. тАв Portfolio Variance = (w1)^2 (0) + (w2 )^2 (vol of risky asset)^2 + 2 (correlation) (w1 )(w2 ) (0)(vol of risky asset). тАв Portfolio Volatility = w2 * (vol of risky asset). тАв This simplification in the formula for the portfolio volatility occurs because the vol of the riskfree asset is zero. тАв To understand the tradeoff between risk and return, we can graph the portfolio return vs the the portfolio volatility. тАв The following graph shows this graph for the case when the mean return for the riskfree asset is 5%, the mean return for the risky asset is 12%, and the volatility of the risky asset is 15%.
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