748 research outputs found
Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance
Hedge funds databases are typically subject to high attrition ratesbecause of fund termination and self-selection. Even when all fundsare included up to their last available return, one cannot preventthat ex post conditioning biases a.ect standard estimates ofperformance persistence. In this paper we analyze the persistence inthe performance of U.S. hedge funds taking into account look-aheadbias (multi-period sampling bias). To do so, we model attrition ofhedge funds and analyze how it depends upon historical performance.Next, we use a weighting procedure that eliminates look-ahead bias inmeasures for performance persistence. The results show that the impactof look-ahead bias is quite severe, even though positive and negativesurvival-related biases are sometimes suggested to cancel out. Athorizons of one and four quarters, we find clear evidence of positivepersistence in hedge fund returns, also after correcting forinvestment style. At the two-year horizon, past winning funds tend toperform poorly in the future.survival;performance measurement;investments;individual profiles;hedge funds
Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance
Hedge funds databases are typicall subject to high attrition rates because of fund termination and self-selection.Even when all funds are included up to their last available return, one cannot prevent that ex post conditioning biases affect standard estimates of performance persistence.In this paper we analyze the persistence in the performance of U.S. hedge funds taking into account look-ahead bias (multi-period sampling bias).To do so, we model attrition of hedge funds and analyze how it depends upon historical performance.Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence.The results show that the impact of look-ahead bias is quitesevere, even though positive and negative survivalrelated biases are sometimes suggested to cancel out.At horizons of one and four quarters, we find clear evidence of positive persistence in hedge fund returns, also after correcting for investment style.At the two-year horizon, past winning funds tend to perform poorly in the future.hedging;performance measurement;investment trusts
Equivalence of robust stabilization and robust performance via feedback
One approach to robust control for linear plants with structured uncertainty
as well as for linear parameter-varying (LPV) plants (where the controller has
on-line access to the varying plant parameters) is through
linear-fractional-transformation (LFT) models. Control issues to be addressed
by controller design in this formalism include robust stability and robust
performance. Here robust performance is defined as the achievement of a uniform
specified -gain tolerance for a disturbance-to-error map combined with
robust stability. By setting the disturbance and error channels equal to zero,
it is clear that any criterion for robust performance also produces a criterion
for robust stability. Counter-intuitively, as a consequence of the so-called
Main Loop Theorem, application of a result on robust stability to a feedback
configuration with an artificial full-block uncertainty operator added in
feedback connection between the error and disturbance signals produces a result
on robust performance. The main result here is that this
performance-to-stabilization reduction principle must be handled with care for
the case of dynamic feedback compensation: casual application of this principle
leads to the solution of a physically uninteresting problem, where the
controller is assumed to have access to the states in the artificially-added
feedback loop. Application of the principle using a known more refined
dynamic-control robust stability criterion, where the user is allowed to
specify controller partial-state dimensions, leads to correct
robust-performance results. These latter results involve rank conditions in
addition to Linear Matrix Inequality (LMI) conditions.Comment: 20 page
Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance
Hedge funds databases are typicall subject to high attrition rates because of fund termination and self-selection.Even when all funds are included up to their last available return, one cannot prevent that ex post conditioning biases affect standard estimates of performance persistence.In this paper we analyze the persistence in the performance of U.S. hedge funds taking into account look-ahead bias (multi-period sampling bias).To do so, we model attrition of hedge funds and analyze how it depends upon historical performance.Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence.The results show that the impact of look-ahead bias is quitesevere, even though positive and negative survivalrelated biases are sometimes suggested to cancel out.At horizons of one and four quarters, we find clear evidence of positive persistence in hedge fund returns, also after correcting for investment style.At the two-year horizon, past winning funds tend to perform poorly in the future
Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance
Hedge funds databases are typically subject to high attrition rates
because of fund termination and self-selection. Even when all funds
are included up to their last available return, one cannot prevent
that ex post conditioning biases a.ect standard estimates of
performance persistence. In this paper we analyze the persistence in
the performance of U.S. hedge funds taking into account look-ahead
bias (multi-period sampling bias). To do so, we model attrition of
hedge funds and analyze how it depends upon historical performance.
Next, we use a weighting procedure that eliminates look-ahead bias in
measures for performance persistence. The results show that the impact
of look-ahead bias is quite severe, even though positive and negative
survival-related biases are sometimes suggested to cancel out. At
horizons of one and four quarters, we find clear evidence of positive
persistence in hedge fund returns, also after correcting for
investment style. At the two-year horizon, past winning funds tend to
perform poorly in the future
Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance
We analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence. In contrast to earlier results for mutual funds, the impact of look-ahead bias is exacerbated for hedge funds due to their greater level of total risk. At the four-quarter horizon, look-ahead bias can be as much as 3.8%, depending upon the decile of the distribution. We find positive persistence in hedge fund quarterly returns after correcting for investment style. The empirical pattern at the annual level is also consistent with positive persistence, but its statistical significance is weak
The Bezout equation on the right half plane in a Wiener space setting
This paper deals with the Bezout equation , , in
the Wiener space of analytic matrix-valued functions on the right half plane.
In particular, is an matrix-valued analytic Wiener function,
where , and the solution is required to be an analytic Wiener
function of size . The set of all solutions is described explicitly
in terms of a matrix-valued analytic Wiener function , which has
an inverse in the analytic Wiener space, and an associated inner function
defined by and the value of at infinity. Among the solutions,
one is identified that minimizes the -norm. A Wiener space version of
Tolokonnikov's lemma plays an important role in the proofs. The results
presented are natural analogs of those obtained for the discrete case in [11].Comment: 15 page
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