Jasmina Hasanhodzic

Can Hedge-Fund Returns Be Replicated?: The Linear Case

In contrast to traditional investments such as stocks and bonds, hedge-fund returns have more complex risk exposures that yield additional and complementary sources of risk premia. This raises the possibility of creating passive replicating portfolios or “clones” using liquid exchange-traded instruments that provide similar risk exposures at lower cost and with greater transparency. By using monthly returns data for 1610 hedge funds in the TASS database from 1986 to 2005, we estimate linear factor models for individual hedge funds using six common factors, and measure the proportion of the funds’ expected returns and volatility that are attributable to such factors. For certain hedge-fund style categories, we find that a significant fraction of both can be captured by common factors corresponding to liquid exchange-traded instruments. While the performance of linear clones is often inferior to their hedge-fund counterparts, they perform well enough to warrant serious consideration as passive, transparent, scalable, and lower-cost alternatives to hedge funds.

Black's Leverage Effect is not Due to Leverage

One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility, first documented by Black (1976) who attributed it to the effects of financial leverage. As a company’s stock price declines, it becomes more highly leveraged given a fixed level of debt outstanding, and this increase in leverage induces a higher equity-return volatility. In a sample of all-equity-financed companies from January 1972 to December 2008, we find that the leverage effect is just as strong if not stronger, implying that the inverse relationship between price and volatility is not driven by financial leverage.

Generational Risk -- Is It a Big Deal? Simulating an 80-Period OLG Model with Aggregate Shocks

The theoretical literature presumes generational risk is large enough to merit study and that such risk can be meaningfully shared via appropriate government policies. This paper assesses these propositions. It develops an 80-period OLG model to directly measure generational risk and the extent to which it can be mitigated via financial markets or Social Security. The model is trend stationary as is common in the literature. It features isoelastic preferences, moderate risk aversion, Cobb-Douglas technology, and shocks to both TFP and capital depreciation. Our computation method builds on Marcet (1988), Marcet and Marshall (1994), and Judd, Maliar, and Maliar (2009, 2011), who overcome the curse of dimensionality by limiting a models state space to its ergodic set. Our baseline calibration uses the literatures estimate of the TFP shock process and sets depreciation shocks to match the variability of the return to U.S. wealth. The baseline results feature higher than observed output variability. Nonetheless, we find relatively little generational risk. This calibration produces a very small risk premium. Resolving this puzzle by adding increasing borrowing costs does not affect our conclusions regarding the size of generational risk. Our second calibration increases depreciation shocks, as in Krueger and Kubler (2006), to match the models return variability with that of the equity market. Doing so reproduces the equity premium (even absent borrowing costs), but substantially overstates the variability of output and wages. This calibration generates significant cross-generational risk. Under both calibrations, the one-period bond market is very effective in sharing risks among contemporaneous generations. But the simulated sizes of short and long bond positions associated with unrestricted use of this market appear unrealistically large. Finally, we find that Social Security can be effective in reducing generational risk no matter its initial size.

A Computational View of Market Efficiency

We study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be efficient with respect to resources S (e.g., time, memory) if no strategy using resources S can make a profit. As a first step, we consider memory-m strategies whose action at time t depends only on the m previous observations at times t − m, … , t − 1. We introduce and study a simple model of market evolution, where strategies impact the market by their decision to buy or sell. We show that the effect of optimal strategies using memory m can lead to ‘market conditions’ that were not present initially, such as (1) market spikes and (2) the possibility for a strategy using memory m′ > m to make a bigger profit than was initially possible. We suggest ours as a framework to rationalize the technological arms race of quantitative trading firms.