INDUSTRIAL ENGINEERING APPLICATIONS IN FINANCIAL ASSET MANAGEMENT:TIME HORIZON

By -
TIME HORIZON

Beyond the taxation issue, a key component of MV problem specification is properly defining the horizon over which the optimization is applied. In particular, risk and correlation can differ signifi- cantly, depending on the time frame considered. For example, equities are much less risky over five- year horizons than over one year (Figure 8). This has led some analysts to argue for larger equity allocations for long-term investors because stock risk declines relative to bonds over lengthy horizons.

Industrial Engineering Applications in Financial Asset Management-0021

This issue is especially pertinent if illiquid assets such as private equity are to be included in the portfolio. Using annual volatility for private equity makes no sense—10-year risk must be considered.

In general, the longer the time horizon, the lower the risk for assets that appear to be risky when evaluated based on short-term data. As a consequence, long-horizon investors will allocate more to assets such as stocks and private equity. The reason for this is that annual risk is related to risk over other intervals by the identity ua = uTT1/2, where T is the periodicity of the data. Note that T = 4, 12, or 52 for quarterly, monthly, and weekly data and T = 1 / 2, 1 / 5, or 1 / 10 for 2-, 5-, and 10-year intervals. Thus, assets with high annual risk have exponentially declining risk over lengthy time intervals. This is why long-horizon portfolios often have relatively small allocations to fixed-income instruments and higher allocations to what are perceived as riskier assets (Figure 9). Some managers go as far as to suggest that a long-horizon investor hold 100% equities if they desire liquidity and the ability to exit portfolios at a time of their choosing.

Comments

Post a Comment

Popular posts from this blog

DUALITY THEORY:THE ESSENCE OF DUALITY THEORY

By -
Image
THE ESSENCE OF DUALITY THEORY Given our standard form for the primal problem at the left (perhaps after conversion from another form), its dual problem has the form shown to the right. Thus, with the primal problem in maximizatio n form, the dual problem is in minimization form instead. Furthermore, the dual problem uses exactly the same pa r amete r s as the primal problem, but in different locations, as summarized below. 1. The coefficients in the objective function of the primal problem are the right-hand sides of the functional constraints in the dual problem. 2. The right-hand sides of the functional constraints in the primal problem are the coef- ficients in the objective function of the dual problem. 3. The coefficients of a variable in the functional constraints of the primal problem are the coefficients in a functional constraint of the dual problem. To highlight the comparison, now look at these same two problems in matrix notation (as introduced at the beginn...
Read more

NETWORK OPTIMIZATION MODELS:THE MINIMUM SPANNING TREE PROBLEM

By -
Image
THE MINIMUM SPANNING TREE PROBLEM The minimum spanning tree problem bears some similarities to the main version of the shortest-path problem presented in the preceding section. In both cases, an undi r ecte d and connected network is being considered, where the given information includes some mea- sure of the positive length (distance, cost, time, etc.) associated with each link. Both problems also involve choosing a set of links that have the shortest total length among all sets of links that satisfy a certain property. For the shortest-path problem, this property is that the chosen links must provide a path between the origin and the destination. For the minimum spanning tree problem, the required property is that the chosen links must provide a path between ea c h pair of nodes. The minimum spanning tree problem can be summarized as follows: 1. You are given the nodes of a network but not the links. Instead, you are given the po- tential links and the positive length for each ...
Read more

INTEGER PROGRAMMING:THE BRANCH-AND-CUT APPROACH TO SOLVING BIP PROBLEMS

By -
TH E BRANCH-AND-CUT APPROACH TO SOLVING BIP PROBLEMS Integer programming has been an especially exciting area of OR since the mid-1980s be- cause of the dramatic progress being made in its solution methodology. Background To place this progress into perspective, consider the historical background. One big break- through had come in the 1960s and early 1970s with the development and refinement of the branch-and-bound approach. But then the state of the art seemed to hit a plateau. Rel- atively small problems (well under 100 variables) could be solved very efficiently, but even a modest increase in problem size might cause an explosion in computation time be- yond feasible limits. Little progress was being made in overcoming this exponential growth in computation time as the problem size was increased. Many important problems arising in practice could not be solved. Then came the next breakthrough in the mid-1980s, with the introduction of the b r an c h- and-cu t approach to sol...
Read more