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Enterprise Risk Management & Capital Budgeting under Dependent Risks: An Integrated Framework Tianyang Wang, Colorado State University (with Jing Ai, The.

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Presentation on theme: "Enterprise Risk Management & Capital Budgeting under Dependent Risks: An Integrated Framework Tianyang Wang, Colorado State University (with Jing Ai, The."— Presentation transcript:

1 Enterprise Risk Management & Capital Budgeting under Dependent Risks: An Integrated Framework Tianyang Wang, Colorado State University (with Jing Ai, The University of Hawaii)

2 Motivations Enterprise risk management (ERM) help companies optimize operational decisions. Risk management and capital budgeting are two critical components of the dynamic corporate decision process. They naturally connected by the dependent risk exposures and a variety of other synergetic relationships within an intricate corporate structure It is challenging is to fully encompass the two components into the overall corporate decision making. 2

3 Enterprise risk management –Coordinate decisions of other corporate activities Froot et al. 1993 and Froot and Stein (1998) – Incorporated in the decision making process Ai et al. (2010) Capital budgeting – Modeling the internal allocation process The NPV standard (Graham and Harvey 2001, Graham et al. 2010) – Agency problems Centralized vs. Delegated (Marino and Matsusaka 2005) 3 Literature Review

4 Overview Develop an integrated framework that allows to design optimal investment and risk management strategies jointly and endogenously Characterize an optimal decision making problem with – Evaluations of capital requirements, cash flow potentials, and risk exposures first within each division – Corporate level optimal decisions in a multi-division, multi-project, multi-period environment – Fully accounting for dependent risks across business divisions and time periods – Simultaneously obtaining optimal risk management strategies Uses the intuitive interface of decision tree as an auxiliary step 4

5 Solve an optimization problem of the corporate decision maker in capital allocation Model structure – Objective function: Maximize her appropriate utility function – Corporate state of nature tree: probability tree structure to capture dependent risk within and across divisions and periods, using the copula model – Characterize cash flows of the corporate project portfolio with the tree structure and a set of constraints Logical consistency Capital resource Risk – Incorporate RM strategies by recognizing cost and adjusting future cash flow positions 5 Model Set-Up

6 Mapping decisions onto the corporate state of nature tree 6

7 An Illustrative Example Consider a financial services company with a two-period planning horizon – Two business divisions: loan and insurance, each with one potential project to invest in – In period 1, both are subject to market risk – In period 2, loan division is subject to credit risk and insurance division is subject to actuarial pricing risk – These risks are dependent – The CEO makes investment decisions at the corporate level, given an initial capital budget, by maximizing her expected utility from the final resource position at the end of the two-period horizon 7

8 Build the corporate state of nature tree with dependent risks Distributional and dependency assumptions – We use scaled beta distributions for its flexibility – Correlation Structure among Risks Risk Correlation 1-0.5-0.7 -0.510.5 -0.70.51 8 An Illustrative Example

9 Characterize cash flows of the corporate project portfolio – Investment requirements, risk-free interest rate, etc. Decision ForInvestment ($ million)Notation Division 1 (t=0)7Inv1(1) Division 1 (t=1)6Inv1(2) Division 2 (t=0)7Inv2(1) Division 2 (t=1)6Inv2(2) Initial Resources (Inv0)30 Risk-Free Interest Rate6% 9 An Illustrative Example

10 Decision making process for division 1 10

11 An Illustrative Example Decision making process for division 2 11

12 The corporate state of nature tree 12 An Illustrative Example

13 Objective function – Using the mean-risk model, the CEO maximizes the certainty equivalent, CE = Expected future cash flow ( EV T ) – Risk – Using lower semi-absolute deviation (LSAD) as the risk measure to focus on expected downside risk – Risk = risk aversion coefficient (0.5)*LSAD Model constraints – Logical consistency constraints – Capital resource constraints – Risk constraints Incorporating risk management strategies 13

14 14 Optimal Decisions X 1,0,Y 1α 1,0 0.012 X 1,0,N 0α 2,0 0.737 X 1,1,Y 1α 1,1 0 X 1,1,N 0α 1,2 0.644 X 1,2,Y 1α 1,3 1 X 1,2,N 0α 2,1 0 X 1,3,Y 1α 2,2 0.592 X 1,3,N 0α 2,3 1 X 2,0,Y 1 X 2,0,N 0 X 2,1,Y 1 X 2,1,N 0 X 2,2,Y 1 X 2,2,N 0 X 2,3,Y 1 X 2,3,N 0

15 15 Optimal Decisions (ignoring risk dependence) X 1,0,Y 1α 1,0 0 X 1,0,N 0α 2,0 0 X 1,1,Y 1α 1,1 0.581 X 1,1,N 0α 1,2 0.980 X 1,2,Y 1α 1,3 0.538 X 1,2,N 0α 2,1 0 X 1,3,Y 1α 2,2 0 X 1,3,N 0α 2,3 0 X 2,0,Y 0 X 2,0,N 1 X 2,1,Y 0 X 2,1,N 0 X 2,2,Y 0 X 2,2,N 0 X 2,3,Y 0 X 2,3,N 0

16 16 Comparison of Optimal Values and Risk Exposures

17 17 Project Valuation with risk free rate33.71 Forgoing Hedging Assumption of Independent Risks and No Hedging Assumption of Independent RisksComplete Model Optimal Value45.8746.1346.6047.90 Value of Capital Budgeting and Hedging Strategies12.1612.42 12.8914.19 Diff% from The Complete Model-14.31%-12.47% -9.16%0.00% Comparison of Risk Exposure

18 Capital budgeting – Studying optimal capital budgeting in a multi- divisional firm with dependent risk exposures. Corporate risk management and ERM – Operationalizing the concepts of ERM in incorporating risk management into the corporate decision making process. Decision analysis – Solve problem of capital budgeting and enterprise risk management. 18 Summary

19 Thank You! 19


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