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| Publications of David A. Hsieh :recent first alphabetical combined listing:
%% Books
@book{fds266727,
Author = {Brock, WA and Hsieh, DA and LeBaron, BD},
Title = {Nonlinear Dynamics, Chaos, and Instability: Statistical
Theory and Economic Evidence Hauptbd},
Pages = {328 pages},
Publisher = {Cambridge University Press},
Year = {1991},
Key = {fds266727}
}
@book{fds326061,
Author = {Brock, WA and Hsieh, DA and LeBaron, B},
Title = {Nonlinear Dynamics, Chaos, and Instability - Unix
version},
Volume = {1},
Year = {1992},
Month = {April},
ISBN = {0-262-52172-5},
Abstract = {Chaos theory has touched on such fields as biology,
cognitive science, and physics. By providing a unified and
complete explanation of new statistical methods that are
useful for testing for chaos in data sets, Brock, Hsieh, and
LeBaron show how the principles of chaos theory can be
applied to such areas of economics and finance as the
changing structure of stock returns and nonlinearity in
foreign exchange. They use computer models extensively to
illustrate their ideas and explain this frontier research at
a level of rigor sufficient for others to build upon as well
as to verify the soundness of their arguments. The authors,
who have played a major role in developing basic testing
methods that are effective in detecting chaos and other
nonlinearities, provide a detailed exposition of empirical
techniques for identifying evidence of chaos. They introduce
and describe the BDS statistic, an easy-to-use test that
detects the existence of potentially forecastable structure,
nonstationarity, or hidden patterns in time-series data and
that can be adapted to test for the adequacy of fit of
forecasting models. An extensive performance evaluation of
the BDS is included. Nonlinear Dynamics, Chaos, and
Instability also reviews important issues in the theoretical
economics literature on chaos and complex dynamics, surveys
existing work on the detection of chaos and nonlinear
structure, and develops models and processes to discover
predictable sequencing in time-series data, such as stock
returns, that currently appear random.},
Key = {fds326061}
}
%% Journal Articles
@article{fds266731,
Author = {Hsieh, DA},
Title = {The determination of the real exchange rate. The
productivity approach},
Journal = {Journal of International Economics},
Volume = {12},
Number = {3-4},
Pages = {355-362},
Publisher = {Elsevier BV},
Year = {1982},
Month = {January},
ISSN = {0022-1996},
url = {http://dx.doi.org/10.1016/0022-1996(82)90045-9},
Abstract = {This paper explains deviations of exchange rates from
purchasing power parity with the differences between
countries of the relative growth rates of labor productivity
between traded and nontraded sectors. Two cases are
considered: Germany and Japan versus their respective major
trading partners. The results show that the time series
methodology yields a more favorable confirmation of the
productivity differential model than the cross section
regressions in the literature. © 1982.},
Doi = {10.1016/0022-1996(82)90045-9},
Key = {fds266731}
}
@article{fds312646,
Author = {HSIEH, DA and KULATILAKA, N},
Title = {Rational Expectations and Risk Premia in Forward Markets:
Primary Metals at the London Metals Exchange},
Journal = {The Journal of Finance},
Volume = {37},
Number = {5},
Pages = {1199-1207},
Publisher = {WILEY},
Year = {1982},
Month = {January},
ISSN = {0022-1082},
url = {http://dx.doi.org/10.1111/j.1540-6261.1982.tb03612.x},
Abstract = {This paper tests whether forward prices equal the traders'
expectations of the future spot prices at maturity, under
two different models of expectations formation: full
information rational expectations and incomplete information
mechanical forecasting rule. The tests are performed, over
the period January 1970 through September 1980, on the
forward markets for the primary metals—copper, tin, lead,
and zinc‐traded in the London Metals Exchange. We find
evidence consistent with the existence of time varying risk
premia. 1982 The American Finance Association},
Doi = {10.1111/j.1540-6261.1982.tb03612.x},
Key = {fds312646}
}
@article{fds266730,
Author = {Hsieh, DA},
Title = {A heteroscedasticity-consistent covariance matrix estimator
for time series regressions},
Journal = {Journal of Econometrics},
Volume = {22},
Number = {3},
Pages = {281-290},
Publisher = {Elsevier BV},
Year = {1983},
Month = {January},
ISSN = {0304-4076},
url = {http://dx.doi.org/10.1016/0304-4076(83)90104-5},
Abstract = {This paper provides a covariance matrix estimator for the
ordinary least squares coefficients of a linear time series
model which is consistent even when the disturbances are
heteroscedastic. This estimator does not require a formal
model of the heteroscedasticity. One can also obtain a
direct test of heteroscedasticity, although Monte Carlo
experiments show that it may have low power. ©
1983.},
Doi = {10.1016/0304-4076(83)90104-5},
Key = {fds266730}
}
@article{fds266732,
Author = {Hsieh, DA},
Title = {Tests of rational expectations and no risk premium in
forward exchange markets},
Journal = {Journal of International Economics},
Volume = {17},
Number = {1-2},
Pages = {173-184},
Publisher = {Elsevier BV},
Year = {1984},
Month = {January},
ISSN = {0022-1996},
url = {http://dx.doi.org/10.1016/0022-1996(84)90013-8},
Abstract = {This paper tests the simple efficiency hypothesis, i.e. that
traders have rational expectations and charge no risk
premium in the forward exchange market. It uses a
statistical procedure which is consistent under a large
class of heteroscedasticity, and a set of data which takes
into account the institutional features of the forward
exchange market. The results show that this procedure leads
to stronger rejections of the simple efficiency hypothesis
than do procedures using the standard assumption of
homoscedasticity. © 1984.},
Doi = {10.1016/0022-1996(84)90013-8},
Key = {fds266732}
}
@article{fds266734,
Author = {Hsieh, DA},
Title = {International risk sharing and the choice of exchange-rate
regime},
Journal = {Journal of International Money and Finance},
Volume = {3},
Number = {2},
Pages = {141-151},
Publisher = {Elsevier BV},
Year = {1984},
Month = {January},
ISSN = {0261-5606},
url = {http://dx.doi.org/10.1016/0261-5606(84)90002-0},
Abstract = {This paper examines the argument that the fixed
exchange-rate regime should be preferred to the flexible
rate regime because the former allows risk sharing across
countries while the latter does not. The analysis is
performed in a two-country overlapping generations model,
where markets are incomplete under all exchange regimes. It
is shown that risks are pooled across countries when the
equilibrium exchange rate is constant across states of
nature, which arises under the fixed rate regime with or
without capital restriction, and under the flexible rate
regime without capital restriction. Risks are not pooled
across countries when the equilibrium exchange rate is
different across states of nature, which arises under the
flexible rate regime with capital restriction. But in a
model with incomplete markets, the ability to share risk
across countries in the regimes with constant exchange rates
does not necessarily lead to higher welfare than the
inability to share risk in the regime with random exchange
rates. © 1984.},
Doi = {10.1016/0261-5606(84)90002-0},
Key = {fds266734}
}
@article{fds312644,
Author = {Hsieh, DA and Lee, J},
Title = {Choice of Inventory Accounting Method: a
Ricardian},
Journal = {Journal of Accounting Research},
Volume = {80},
Number = {2},
Pages = {468-485},
Publisher = {Wiley: 24 months - No Online Open},
Year = {1985},
ISSN = {1475-679X},
Key = {fds312644}
}
@article{fds266733,
Author = {Chan, KC and Chen, NF and Hsieh, DA},
Title = {An exploratory investigation of the firm size
effect},
Journal = {Journal of Financial Economics},
Volume = {14},
Number = {3},
Pages = {451-471},
Publisher = {Elsevier BV},
Year = {1985},
Month = {January},
ISSN = {0304-405X},
url = {http://dx.doi.org/10.1016/0304-405X(85)90008-X},
Abstract = {We investigate the firm size effect for the period 1958 to
1977 in the framework of a multi-factor pricing model. The
risk-adjusted difference in returns between the top five
percent and the bottom five percent of the NYSE firms is
about one to two percent a year, a drop from about twelve
percent per year before risk adjustment. The variable most
responsible for the adjustment is the sensitivity of asset
returns to the changing risk premium, measured by the return
difference between low-grade bonds and long-term government
bonds. © 1985.},
Doi = {10.1016/0304-405X(85)90008-X},
Key = {fds266733}
}
@article{fds312645,
Author = {Hsieh, DA and Manski, CF and McFadden, D},
Title = {Estimation of response probabilities from augmented
retrospective observations},
Journal = {Journal of the American Statistical Association},
Volume = {80},
Number = {391},
Pages = {651-662},
Publisher = {Taylor & Francis: SSH Journals},
Year = {1985},
Month = {January},
ISSN = {1537-274X},
url = {http://dx.doi.org/10.1080/01621459.1985.10478165},
Abstract = {When augmented by suitable auxiliary information,
retrospective data can identify response probabilities. The
auxiliary information may take the form of data on marginal
distributions or appropriate structural assumptions. When a
combination of retrospective observation and auxiliary
information suffices in principle to identify response
probabilities, practical use of this fact requires that
statistically sound and computationally tractable estimation
methods be available. This article analyzes the problem of
identification and presents the needed estimators. © 1976
Taylor & Francis Group, LLC.},
Doi = {10.1080/01621459.1985.10478165},
Key = {fds312645}
}
@article{fds312643,
Author = {Hsieh, DA and Leiderman, L},
Title = {Portfolio Implications of Empirical Rejections of the
Expectations Hypothesis},
Journal = {Review of Economics and Statistics},
Volume = {68},
Number = {4},
Pages = {680-684},
Publisher = {Massachusetts Institute of Technology Press (MIT Press):
Economics Titles},
Year = {1986},
ISSN = {1530-9142},
Key = {fds312643}
}
@article{fds266736,
Author = {Bilson, JFO and Hsieh, DA},
Title = {The profitability of currency speculation},
Journal = {International Journal of Forecasting},
Volume = {3},
Number = {1},
Pages = {115-130},
Publisher = {Elsevier BV},
Year = {1987},
Month = {January},
ISSN = {0169-2070},
url = {http://dx.doi.org/10.1016/0169-2070(87)90082-3},
Abstract = {This paper presents the results of a post-sample simulation
of a speculative strategy using a portfolio of foreign
currency forward contracts. The main new features of the
speculative strategy are (a) the use of Kalman filters to
updata the forecasting equation, (b) the allowance for
transactions costs and margin requirements and (c) the
endogeneous determination of the leveraging of the
portfolio. While the forecasting model tended to
overestimate profit and underestimate risk, the strategy was
still profitable over a three year period and it was
possible to reject the hypothesis that the sum of profits
was zero. © 1987.},
Doi = {10.1016/0169-2070(87)90082-3},
Key = {fds266736}
}
@article{fds312642,
Author = {Hsieh, DA and Manski, CF},
Title = {Monte Carlo Evidence on Adaptive Maximum Likelihood
Estimation of a Regression},
Journal = {The Annals of Statistics},
Volume = {15},
Number = {2},
Pages = {541-551},
Publisher = {Institute of Mathematical Statistics},
Year = {1987},
Month = {June},
ISSN = {0090-5364},
url = {http://dx.doi.org/10.1214/aos/1176350359},
Doi = {10.1214/aos/1176350359},
Key = {fds312642}
}
@article{fds312641,
Author = {Hsieh, DA and Manski, CF},
Title = {Empirical Regularities in the Deutsche Mark Futures
Options},
Journal = {Advances in Futures and Options Research},
Volume = {3},
Pages = {183-208},
Year = {1988},
Key = {fds312641}
}
@article{fds266735,
Author = {Hsieh, DA},
Title = {The statistical properties of daily foreign exchange rates:
1974-1983},
Journal = {Journal of International Economics},
Volume = {24},
Number = {1-2},
Pages = {129-145},
Publisher = {Elsevier BV},
Year = {1988},
Month = {January},
ISSN = {0022-1996},
url = {http://dx.doi.org/10.1016/0022-1996(88)90025-6},
Abstract = {This paper examines the statistical properties of daily
rates of change of five foreign currencies from 1974 to
1983. The main purpose is to discriminate between two
competing explanations for the observed heavy tails of the
distribution: that the data are independently drawn from a
heavy tail distribution which remains fixed over time, and
that the data come from distributions which vary over time.
Evidence point to the rejection of the first hypothesis.
Further investigations show that the rejection can be
attributed to changing means and variances in the data,
which can be described by a simple statistical model. ©
1988.},
Doi = {10.1016/0022-1996(88)90025-6},
Key = {fds266735}
}
@article{fds312639,
Author = {Hsieh, DA},
Title = {Modeling Heteroskedasticity in Daily Exchange
Rates},
Journal = {Journal of Business and Economic Statistics},
Volume = {7},
Number = {3},
Pages = {307-317},
Publisher = {Informa UK Limited},
Year = {1989},
url = {http://dx.doi.org/10.1080/07350015.1989.10509740},
Abstract = {This article estimates autoregressive conditionally
heteroscedastic (ARCH) and generalized ARCH (GARCH) models
for five foreign currencies, using 10 years of daily data, a
variety of ARCH and GARCH specifications, a number of
nonnormal error densities, and a comprehensive set of
diagnostic checks. It finds that ARCH and GARCH models can
usually remove all heteroscedasticity in price changes in
all five currencies. Goodness-of-fit diagnostics indicate
that exponential GARCH with certain nonnormal distributions
fits the Canadian dollar extremely well and the Swiss franc
and the deutsche mark reasonably well. Only one nonnormal
distribution fits the Japanese yen reasonably well. None fit
the British pound. © 1989 American Statistical
Association.},
Doi = {10.1080/07350015.1989.10509740},
Key = {fds312639}
}
@article{fds312640,
Author = {Hsieh, DA},
Title = {Testing for Nonlinear Dependence in Daily Foreign Exchange
Rates},
Journal = {The Journal of Business},
Volume = {62},
Number = {3},
Pages = {339-339},
Publisher = {University of Chicago Press},
Year = {1989},
Month = {January},
ISSN = {0021-9398},
url = {http://dx.doi.org/10.1086/296466},
Doi = {10.1086/296466},
Key = {fds312640}
}
@article{fds312651,
Author = {HSIEH, DA and MILLER, MH},
Title = {Margin Regulation and Stock Market Volatility},
Journal = {The Journal of Finance},
Volume = {45},
Number = {1},
Pages = {3-29},
Publisher = {WILEY},
Year = {1990},
Month = {January},
ISSN = {0022-1082},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:A1990CU53000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=47d3190e77e5a3a53558812f597b0b92},
Abstract = {Using daily and monthly stock returns we find no convincing
evidence that Federal Reserve margin requirements have
served to dampen stock market volatility. The contrary
conclusion, expressed in recent papers by Hardouvelis
(1988a, b), is traced to flaws in his test design. We do
detect the expected negative relation between margin
requirements and the amount of margin credit outstanding. We
also confirm the recent finding by Schwert (1988) that
changes in margin requirements by the Fed have tended to
follow rather than lead changes in market volatility. 1990
The American Finance Association},
Doi = {10.1111/j.1540-6261.1990.tb05078.x},
Key = {fds312651}
}
@article{fds312637,
Author = {Hsieh, DA and Fung, W},
Title = {Estimating the Dynamics of Foreign Currency
Futures},
Journal = {Review of Futures Markets (Kent)},
Volume = {10},
Pages = {490-514},
Year = {1991},
ISSN = {1933-7116},
Key = {fds312637}
}
@article{fds312638,
Author = {Hsieh, DA},
Title = {Implications of Observed Properties of Daily Exchange Rate
Movements},
Journal = {Journal of International Financial Markets, Institutions &
Money},
Volume = {1},
Pages = {61-71},
Publisher = {Elsevier},
Year = {1991},
ISSN = {1042-4431},
Key = {fds312638}
}
@article{fds312652,
Author = {HSIEH, DA},
Title = {Chaos and Nonlinear Dynamics: Application to Financial
Markets},
Journal = {The Journal of Finance},
Volume = {46},
Number = {5},
Pages = {1839-1877},
Publisher = {WILEY},
Year = {1991},
Month = {January},
ISSN = {0022-1082},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:A1991GW12200011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=47d3190e77e5a3a53558812f597b0b92},
Abstract = {After the stock market crash of October 19, 1987, interest
in nonlinear dynamics, especially deterministic chaotic
dynamics, has increased in both the financial press and the
academic literature. This has come about because the
frequency of large moves in stock markets is greater than
would be expected under a normal distribution. There are a
number of possible explanations. A popular one is that the
stock market is governed by chaotic dynamics. What exactly
is chaos and how is it related to nonlinear dynamics? How
does one detect chaos? Is there chaos in financial markets?
Are there other explanations of the movements of financial
prices other than chaos? The purpose of this paper is to
explore these issues. 1991 The American Finance
Association},
Doi = {10.1111/j.1540-6261.1991.tb04646.x},
Key = {fds312652}
}
@article{fds266737,
Author = {Hsieh, DA},
Title = {A nonlinear stochastic rational expectations model of
exchange rates},
Journal = {Journal of International Money and Finance},
Volume = {11},
Number = {3},
Pages = {235-250},
Publisher = {Elsevier BV},
Year = {1992},
Month = {January},
ISSN = {0261-5606},
url = {http://dx.doi.org/10.1016/0261-5606(92)90044-X},
Abstract = {This paper constructs an example of a nonlinear stochastic
rational expectations exchange rate with an explicit
solution, which is consistent with nonlinearities in short
term movements in exchange rates. The model consists of risk
neutral agents, who know the intervention rule of the
central bank. The resulting exchange rate switches between
two linear stochastic processes, one when intervention is
present, and another when intervention is absent.
Nonlinearity enters through the probability of intervention,
which is time varying and depends on past outcomes. This
model is consistent with the empirical observations that the
rate of change of the exchange rate has little
autocorrelation, but it exhibits strong nonlinear
dependence, and its variance changes over time. (JEL J31,
G15). © 1992.},
Doi = {10.1016/0261-5606(92)90044-X},
Key = {fds266737}
}
@article{fds266738,
Author = {Hsieh, DA},
Title = {Using non-linear methods to search for risk premia in
currency futures},
Journal = {Journal of International Economics},
Volume = {35},
Number = {1-2},
Pages = {113-132},
Publisher = {Elsevier BV},
Year = {1993},
Month = {January},
ISSN = {0022-1996},
url = {http://dx.doi.org/10.1016/0022-1996(93)90007-K},
Abstract = {This paper uses currency futures prices to test the joint
null hypotheses of rational expectations and absence of a
time-varying risk premium in the foreign exchange market. We
find no linear predictability in the logarithm of futures
price changes, either using its own past or past interest
differentials. Also we establish that there is no non-linear
predictability in log price changes, conditioning on its own
past, or past interest rate differentials. Thus, if a
time-varying risk premium exists in currency futures market,
it is not related to its own past or past interest rate
differentials. © 1993.},
Doi = {10.1016/0022-1996(93)90007-K},
Key = {fds266738}
}
@article{fds312650,
Author = {Hsieh, DA},
Title = {Implications of Nonlinear Dynamics for Financial Risk
Management},
Journal = {Journal of Financial and Quantitative Analysis},
Volume = {28},
Number = {1},
Pages = {41-64},
Publisher = {JSTOR},
Year = {1993},
Month = {January},
ISSN = {0022-1090},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:A1993LA56400003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=47d3190e77e5a3a53558812f597b0b92},
Abstract = {This paper demonstrates that when log price changes are not
IID, their conditional density may be more accurate than
their unconditional density for describing short-term
behavior. Using the BDS test of independence and identical
distribution, daily log price changes in four currency
futures contracts are found to be not IID. While there
appear to be no predictable conditional mean changes,
conditional variances are predictable, and can be described
by an autoregressive volatility model that seems to capture
all the departures from independence and identical
distribution. Based on this model, daily log price changes
are decomposed into a predictable part, which is described
parametrically by the autoregressive volatility model, and
an unpredictable part, which can be modeled by an empirical
density, either parametrically or nonparametrically. This
two-step seminonparametric method yields a conditional
density for daily log price changes, which has a number of
uses in financial risk management. © 1993, School of
Business Administration, University of Washington. All
rights reserved.},
Doi = {10.2307/2331150},
Key = {fds312650}
}
@article{fds312653,
Author = {BANSAL, R and HSIEH, DA and VISWANATHAN, S},
Title = {A New Approach to International Arbitrage
Pricing},
Journal = {The Journal of Finance},
Volume = {48},
Number = {5},
Pages = {1719-1747},
Publisher = {WILEY},
Year = {1993},
Month = {January},
ISSN = {0022-1082},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:A1993MP99100006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=47d3190e77e5a3a53558812f597b0b92},
Abstract = {This paper uses a nonlinear arbitrage‐pricing model, a
conditional linear model, and an unconditional linear model
to price international equities, bonds, and forward currency
contracts. Unlike linear models, the nonlinear
arbitrage‐pricing model requires no restrictions on the
payoff space, allowing it to price payoffs of options,
forward contracts, and other derivative securities. Only the
nonlinear arbitrage‐pricing model does an adequate job of
explaining the time series behavior of a cross section of
international returns. 1993 The American Finance
Association},
Doi = {10.1111/j.1540-6261.1993.tb05126.x},
Key = {fds312653}
}
@article{fds312636,
Author = {Hsieh, DA},
Title = {Assessing the Market and Credit Risks of Long-Term Interest
Rate and Foreign Currency Products},
Journal = {Financial Analysts Journal},
Volume = {49},
Number = {4},
Pages = {75-79},
Publisher = {Informa UK Limited},
Year = {1993},
Month = {July},
ISSN = {0015-198X},
url = {http://dx.doi.org/10.2469/faj.v49.n4.75},
Doi = {10.2469/faj.v49.n4.75},
Key = {fds312636}
}
@article{fds328083,
Author = {Hsieh, DA and Peters, EE},
Title = {Chaos and Order in the Capital Markets: A New View of
Cycles, Prices, and Market Volatility.},
Journal = {The Journal of Finance},
Volume = {48},
Number = {5},
Pages = {2041-2041},
Publisher = {JSTOR},
Year = {1993},
Month = {December},
url = {http://dx.doi.org/10.2307/2329084},
Doi = {10.2307/2329084},
Key = {fds328083}
}
@article{fds312635,
Author = {Hsieh, DA},
Title = {Nonlinear Dynamics in Financial Markets: Evidence and
Implications},
Journal = {Financial Analysts Journal},
Volume = {51},
Number = {4},
Pages = {55-62},
Publisher = {CFA Institute},
Year = {1995},
Month = {July},
ISSN = {0015-198X},
url = {http://dx.doi.org/10.2469/faj.v51.n4.1921},
Doi = {10.2469/faj.v51.n4.1921},
Key = {fds312635}
}
@article{fds312634,
Author = {Fung, W and Hsieh, DA},
Title = {Global Yield Curve Event Risks},
Journal = {The Journal of Fixed Income},
Volume = {6},
Number = {2},
Pages = {37-48},
Publisher = {Pageant Media US},
Year = {1996},
Month = {September},
ISSN = {1059-8596},
url = {http://dx.doi.org/10.3905/jfi.1996.408175},
Doi = {10.3905/jfi.1996.408175},
Key = {fds312634}
}
@article{fds266739,
Author = {Fung, W and Hsieh, DA},
Title = {Survivorship bias and investment style in the returns of
CTAs: The information content of performance track
records},
Journal = {Journal of Portfolio Management},
Volume = {24},
Number = {1},
Pages = {30-41},
Publisher = {Institutional Investor Journals},
Year = {1997},
Month = {January},
url = {http://dx.doi.org/10.3905/jpm.1997.409630},
Doi = {10.3905/jpm.1997.409630},
Key = {fds266739}
}
@article{fds266740,
Author = {Fung, W and Hsieh, DA},
Title = {Empirical characteristics of dynamic trading strategies: The
case of hedge funds},
Journal = {Review of Financial Studies},
Volume = {10},
Number = {2},
Pages = {275-302},
Publisher = {Oxford University Press (OUP)},
Year = {1997},
Month = {January},
url = {http://dx.doi.org/10.1093/rfs/10.2.275},
Abstract = {This article presents some new results on an unexplored
dataset on hedge fund performance. The results indicate that
hedge funds follow strategies that are dramatically
different from mutual funds, and support the claim that
these strategies are highly dynamic. The article finds five
dominant investment styles in hedge funds, which when added
to Sharpe's (1992) asset class factor model can provide an
integrated framework for style analysis of both buy-
and-hold and dynamic trading strategies.},
Doi = {10.1093/rfs/10.2.275},
Key = {fds266740}
}
@article{fds312649,
Author = {Gallant, AR and Hsiehb, D and Tauchen, G},
Title = {Estimation of stochastic volatility models with
diagnostics},
Journal = {Journal of Econometrics},
Volume = {81},
Number = {1},
Pages = {159-192},
Publisher = {Elsevier BV},
Year = {1997},
Month = {January},
ISSN = {0304-4076},
url = {http://hdl.handle.net/10161/2057 Duke open
access},
Abstract = {Efficient method of moments (EMM) is used to fit the
standard stochastic volatility model and various extensions
to several daily financial time series. EMM matches to the
score of a model determined by data analysis called the
score generator. Discrepancies reveal characteristics of
data that stochastic volatility models cannot approximate.
The two score generators employed here are 'semiparametric
ARCH' and 'nonlinear nonparametric'. With the first, the
standard model is rejected, although some extensions are
accepted. With the second, all versions are rejected. The
extensions required for an adequate fit are so elaborate
that nonparametric specifications are probably more
convenient. © 1997 Elsevier Science S.A.},
Doi = {10.1016/S0304-4076(97)00039-0},
Key = {fds312649}
}
@article{fds266741,
Author = {Fung, W and Hsieh, DA},
Title = {Is mean-variance analysis applicable to hedge
funds?},
Journal = {Economics Letters},
Volume = {62},
Number = {1},
Pages = {53-58},
Publisher = {Elsevier BV},
Year = {1999},
Month = {January},
url = {http://dx.doi.org/10.1016/s0165-1765(98)00140-2},
Abstract = {This paper shows that the mean-variance analysis of hedge
funds approximately preserves the ranking of preferences in
standard utility functions. This extends the results of
[Levy, H., Markowitz, H.M., 1979. Approximating expected
utility by a function of mean and variance. American
Economic Review 69, 308-317] and [Hlawitschka, W., 1994. The
empirical nature of Taylor-series approximations to expected
utility. American Economic Review 84, 713-719] for
individual stocks and portfolios of stocks.},
Doi = {10.1016/s0165-1765(98)00140-2},
Key = {fds266741}
}
@article{fds266742,
Author = {Fung, W and Hsieh, DA},
Title = {A primer on hedge funds},
Journal = {Journal of Empirical Finance},
Volume = {6},
Number = {3},
Pages = {309-331},
Year = {1999},
Month = {January},
url = {http://dx.doi.org/10.1016/S0927-5398(99)00006-7},
Abstract = {In this paper, we provide a rationale for how hedge funds
are organized and some insight on how hedge fund performance
differs from traditional mutual funds. Statistical
differences among hedge fund styles are used to supplement
qualitative differences in the way hedge fund strategies are
described. Risk factors associated with different trading
styles are discussed. We give examples where standard linear
statistical techniques are unlikely to capture the risk of
hedge fund investments where the returns are primarily
driven by non-linear dynamic strategies.},
Doi = {10.1016/S0927-5398(99)00006-7},
Key = {fds266742}
}
@article{fds266743,
Author = {Fung, W and Hsieh, DA},
Title = {Performance characteristics of hedge funds and commodity
funds: Natural vs. spurious biases},
Journal = {Journal of Financial and Quantitative Analysis},
Volume = {35},
Number = {3},
Pages = {291-307},
Publisher = {JSTOR},
Year = {2000},
Month = {January},
url = {http://dx.doi.org/10.2307/2676205},
Abstract = {It is well known that the pro forma performance of a sample
of investment funds contains biases. These biases are
documented in Brown, Goetzmann, Ibbotson, and Ross (1992)
using mutual funds as subjects. The organization structure
of hedge funds, as private and often offshore vehicles,
makes data collection a much more onerous task, amplifying
the impact of performance measurement biases. This paper
reviews these biases in hedge funds. We also propose using
funds-of-hedge funds to measure aggregate hedge fund
performance, based on the idea that the investment
experience of hedge fund investors can be used to estimate
the performance of hedge funds.},
Doi = {10.2307/2676205},
Key = {fds266743}
}
@article{fds266744,
Author = {Fung, W and Hsieh, DA},
Title = {Measuring the market impact of hedge funds},
Journal = {Journal of Empirical Finance},
Volume = {7},
Number = {1},
Pages = {1-36},
Publisher = {Elsevier BV},
Year = {2000},
Month = {January},
url = {http://dx.doi.org/10.1016/S0927-5398(00)00005-0},
Abstract = {Hedge funds often employ opportunistic trading strategies on
a leveraged basis. It is natural to find their footprints in
most major market events. A "small bet" by large hedge funds
can be a sizeable transaction that can impact a market. This
study estimates hedge fund exposures during a number of
major market events. In some episodes, hedge funds had
significant exposures and were in a position to exert
substantial market impact. In other episodes, hedge fund
exposures were insignificant, either in absolute terms or
relative to other market participants. In all cases, we
found no evidence of hedge funds using positive feedback
trading strategies. There was also little evidence that
hedge funds systematically caused market prices to deviate
from economic fundamentals. © 2000 Elsevier Science
B.V.},
Doi = {10.1016/S0927-5398(00)00005-0},
Key = {fds266744}
}
@article{fds266745,
Author = {Fung, W and Hsieh, DA},
Title = {The risk in hedge fund strategies: Theory and evidence from
trend followers},
Journal = {Review of Financial Studies},
Volume = {14},
Number = {2},
Pages = {313-341},
Publisher = {Oxford University Press (OUP)},
Year = {2001},
Month = {January},
url = {http://dx.doi.org/10.1093/rfs/14.2.313},
Abstract = {Hedge fund strategies typically generate option-like
returns. Linear-factor models using benchmark asset indices
have difficulty explaining them. Following the suggestions
in Glosten and Jagannathan (1994), this article shows how to
model hedge fund returns by focusing on the popular
"trend-following" strategy. We use lookback straddles to
model trend-following strategies, and show that they can
explain trend-following funds' returns better than standard
asset indices. Though standard straddles lead to similar
empirical results, lookback straddles are theoretically
closer to the concept of trend following. Our model should
be useful in the design of performance benchmarks for
trend-following funds.},
Doi = {10.1093/rfs/14.2.313},
Key = {fds266745}
}
@article{fds312633,
Author = {Hsieh, DA},
Title = {The Risk in Fixed-Income Hedge Fund Styles},
Journal = {Journal of Fixed Income},
Volume = {12},
Number = {2},
Pages = {6-27},
Publisher = {Institutional Investor Inc},
Year = {2002},
ISSN = {1059-8596},
url = {http://dx.doi.org/10.3905/jfi.2002.319321},
Doi = {10.3905/jfi.2002.319321},
Key = {fds312633}
}
@article{fds266746,
Author = {Fung, W and Hsieh, DA},
Title = {Asset-Based Style Factors for Hedge Funds},
Journal = {Financial Analysts Journal},
Volume = {58},
Number = {5},
Pages = {16-27},
Publisher = {Informa UK Limited},
Year = {2002},
Month = {January},
url = {http://dx.doi.org/10.2469/faj.v58.n5.2465},
Abstract = {Asset-based style factors link returns of hedge fund
strategies to observed market prices. They provide explicit
and unambiguous descriptions of hedge fund strategies that
reveal the nature and quantity of risk. Asset-based style
factors are key inputs for portfolio construction and for
benchmarking hedge fund performance on a risk-adjusted
basis. We used previously developed models to construct
asset-based style factors and demonstrate that one model
correctly predicted the return behavior of trend-following
strategies during out-of-sample periods - in particular,
during stressful market conditions like those of September
2001.},
Doi = {10.2469/faj.v58.n5.2465},
Key = {fds266746}
}
@article{fds266747,
Author = {Fung, W and Hsieh, DA},
Title = {Hedge-Fund Benchmarks: Information Content and
Biases},
Journal = {Financial Analysts Journal},
Volume = {58},
Number = {1},
Pages = {22-34},
Publisher = {Informa UK Limited},
Year = {2002},
Month = {January},
url = {http://dx.doi.org/10.2469/faj.v58.n1.2507},
Abstract = {We discuss the information content and potential measurement
biases in hedge-fund benchmarks. Hedge-fund indexes built
from databases of individual hedge funds inherit the
measurement biases in the databases. In addition,
broad-based indexes mask the diversity of individual
hedge-fund return characteristics. Consequently, these
indexes provide incomplete information to investors seeking
diversification from traditional asset classes through the
use of hedge funds. The approach to constructing hedge-fund
benchmarks we propose is based on the simple idea that the
most direct way to measure hedge-fund performance is to
observe the investment experience of hedge-fund investors
themselves - the funds of hedge funds (FOFs). In terms of
measurement biases, returns of FOFs can deliver a cleaner
estimate of the investment experience of hedge-fund
investors than the traditional approach. In terms of risk
characteristics, indexes of FOFs are more indicative of the
demand-side dynamics driven by hedge-fund investors'
preferences than are broad-based indexes. Therefore, indexes
of FOFs can provide valuable information for assessing the
hedge-fund industry's performance.},
Doi = {10.2469/faj.v58.n1.2507},
Key = {fds266747}
}
@article{fds266749,
Author = {Fung, W and Hsieh, DA},
Title = {Hedge fund benchmarks: A risk-based approach},
Journal = {Financial Analysts Journal},
Volume = {60},
Number = {5},
Pages = {65-80},
Publisher = {Informa UK Limited},
Year = {2004},
Month = {January},
ISSN = {0015-198X},
url = {http://dx.doi.org/10.2469/faj.v60.n5.2657},
Abstract = {Following a review of the data and methodological
difficulties in applying conventional models used for
traditional asset class indexes to hedge funds, this article
argues against the conventional approach. Instead, in an
extension of previous work on asset-based style (ABS)
factors, the article proposes a model of hedge fund returns
that is similar to models based on arbitrage pricing theory,
with dynamic risk-factor coefficients. For diversified hedge
fund portfolios (as proxied by indexes of hedge funds and
funds of hedge funds), the seven ABS factors can explain up
to 80 percent of monthly return variations. Because ABS
factors are directly observable from market prices, this
model provides a standardized framework for identifying
differences among major hedge fund indexes that is free of
the biases inherent in hedge fund databases.},
Doi = {10.2469/faj.v60.n5.2657},
Key = {fds266749}
}
@article{fds312648,
Author = {Funga, W and Hsieh, DA},
Title = {Extracting portable alphas from equity long/short hedge
funds},
Volume = {2},
Number = {4},
Pages = {161-180},
Publisher = {World Scientific},
Year = {2005},
Month = {January},
url = {http://dx.doi.org/10.1142/9789812569448_0008},
Abstract = {This paper shows empirically that Equity Long/Short (Equity
L/S) hedge funds have significant alpha to both conventional
as well as alternative (hedge fund-like) risk factors
utilizing hedge fund data from three major data bases.
Following the terminology introduced in Fung and Hsieh
(2003) Journal of Fixed Income 58, 16–27, we call these
Equity alternative alphas (or Equity AAs for short). Equity
AAs are extracted from Equity L/S hedge fund returns by
first identifying the systematic risk factors inherent in
their strategies. Hedging out these systematic risk factors,
the resultant AA return series are empirically shown to be
independent of systematic risks during normal as well as
stressful conditions in asset markets. This provides
collaborative evidence that AA returns are portable across
conventional asset-class indexes. By modeling the AA return
series as GARCH(1,1)–AR(1) processes, it is shown that the
unconditional return distributions are normal with
time-varying variance free of serial correlations, skewness,
and kurtosis. Alpha-enhanced equity alternative are
constructed admitting higher mean return, better annual
returns, and Sharpe ratios to the S&P 500 index over the
sample period 1996–2002.},
Doi = {10.1142/9789812569448_0008},
Key = {fds312648}
}
@article{fds312631,
Author = {Hsieh, DA and Fung, W},
Title = {Hedge Funds: An Industry in Its Adolescence},
Journal = {Economic Review},
Volume = {65},
Number = {4},
Pages = {1-33},
Year = {2006},
Key = {fds312631}
}
@article{fds312632,
Author = {Hsieh, DA},
Title = {The Search for Alpha—Sources of Future Hedge Fund
Returns},
Journal = {CFA Institute Conference Proceedings Quarterly},
Volume = {23},
Number = {3},
Pages = {79-89},
Publisher = {CFA Institute},
Year = {2006},
Month = {September},
ISSN = {1930-2703},
url = {http://dx.doi.org/10.2469/cp.v23.n3.4262},
Doi = {10.2469/cp.v23.n3.4262},
Key = {fds312632}
}
@article{fds312630,
Author = {Hsieh, DA},
Title = {Hedge Fund Replication Strategies: Implications for
Investors and Regulators},
Journal = {Financial Stability Review},
Volume = {10},
Pages = {55-66},
Year = {2007},
Key = {fds312630}
}
@article{fds312647,
Author = {Fung, W and Hsieh, DA},
Title = {Will Hedge Funds Regress Towards Index-Like
Products?},
Journal = {Journal of Investment Management},
Volume = {5},
Number = {2},
Year = {2007},
Abstract = {Hedge funds have grown substantially in the past few years.
According to estimates by Tremont Capital Management (2006),
the industry's assets under management increased from just
over $200b in 2000 to over $800b by the end of 2005. Along
with the rapid inflow of capital, hedge fund performance has
declined. According to HFR, the average fund of hedge funds
returned 10.5% per annum during 1996-2000, but only 5.8%
during 2001-5. This development is consistent with the
prediction of Berk and Green (2004) that unchecked inflow of
funds will ultimately erode performance due to diminishing
returns to scale. There is a sense of deja vu among hedge
fund investors that many hedge fund managers are beginning
to resemble active managers in the mutual fund industry of
the past - failing to deliver returns commensurate to the
fees and expenses they imposed on investors. History tells
us that over-priced active managers will be replaced by
low-cost passive index-liked alternatives. Could the same
process be taking place in the hedge fund industry? Against
this background, it is not surprising that investors are
demanding more cost efficient hedge fund products. But, is
existing technology capable of support the creation of
rule-based, low-cost, passive hedge funds? The term
"alternative beta" refers to the returns achievable from
low-cost replication of rule-based trading strategies that
capture return characteristics common across hedge funds,
while "alternative alpha" refers to the returns that are not
easily replicated. The introduction of this terminology was
partly motivated by the need to stress that the search for
hedge fund alpha properly begins with the identification of
beta exposure to systematic risk factors which can go beyond
conventional asset-class factors. This in turn points to the
need for new technology if alternative beta factors are to
be replicated successfully - a new tool kit is
needed.},
Key = {fds312647}
}
@article{fds266748,
Author = {Fung, W and Hsieh, DA and Naik, NY and Ramadorai,
T},
Title = {Hedge funds: Performance, risk, and capital
formation},
Journal = {Journal of Finance},
Volume = {63},
Number = {4},
Pages = {1777-1803},
Publisher = {WILEY},
Year = {2008},
Month = {August},
ISSN = {0022-1082},
url = {http://dx.doi.org/10.1111/j.1540-6261.2008.01374.x},
Abstract = {We use a comprehensive data set of funds-of-funds to
investigate performance, risk, and capital formation in the
hedge fund industry from 1995 to 2004. While the average
fund-of-funds delivers alpha only in the period between
October 1998 and March 2000, a subset of funds-of-funds
consistently delivers alpha. The alpha-producing funds are
not as likely to liquidate as those that do not deliver
alpha, and experience far greater and steadier capital
inflows than their less fortunate counterparts. These
capital inflows attenuate the ability of the alpha producers
to continue to deliver alpha in the future. © 2008 The
American Finance Association.},
Doi = {10.1111/j.1540-6261.2008.01374.x},
Key = {fds266748}
}
@article{fds266751,
Author = {Fung, W and Hsieh, DA},
Title = {Measurement biases in hedge fund performance data: An
update},
Journal = {Financial Analysts Journal},
Volume = {65},
Number = {3},
Pages = {36-38},
Publisher = {Informa UK Limited},
Year = {2009},
Month = {May},
ISSN = {0015-198X},
url = {http://dx.doi.org/10.2469/faj.v65.n3.6},
Doi = {10.2469/faj.v65.n3.6},
Key = {fds266751}
}
@article{fds328082,
Author = {Fung, W and Hsieh, DA},
Title = {Perspectives: Measurement Biases in Hedge Fund Performance
Data: An Update},
Journal = {Financial Analysts Journal},
Volume = {65},
Number = {3},
Year = {2009},
Month = {June},
Key = {fds328082}
}
@article{fds266750,
Author = {Fung, W and Hsieh, DA},
Title = {The risk in hedge fund strategies: Theory and evidence from
long/short equity hedge funds},
Journal = {Journal of Empirical Finance},
Volume = {18},
Number = {4},
Pages = {547-569},
Publisher = {Elsevier BV},
Year = {2011},
Month = {September},
ISSN = {0927-5398},
url = {http://dx.doi.org/10.1016/j.jempfin.2011.04.001},
Abstract = {Theory suggests that long/short equity hedge funds' returns
come from directional as well as spread bets on the stock
market. Empirical analysis finds persistent net exposures to
the spread between small vs large cap stocks in addition to
the overall market. Together, these factors account for more
than 80% of return variation. Additional factors are price
momentum and market activity. Combining two major branches
of hedge fund research, our model is the first that
explicitly incorporates the effect of funding (stock loan)
on alpha. Using a comprehensive dataset compiled from three
major database sources, we find that among the three
thousand plus hedge funds with similar style classification,
less than 20% of long/short equity hedge funds delivered
significant, persistent, stable positive non-factor related
returns. Consistent with the predictions of the Berk and
Green (2004) model we find alpha producing funds decays to
"beta-only" over time. However, we do not find evidence of a
negative effect of fund size on managers' ability to deliver
alpha. Finally, we show that non-factor related returns, or
alpha, are positively correlated to market activity and
negatively correlated to aggregate short interest. In
contrast, equity mutual funds and long-bias equity hedge
funds have no significant, persistent, non-factor related
return. Expressed differently, L/S equity hedge funds, as
the name suggests, do benefit from shorting. Besides
differences in risk taking behavior, this is a key feature
distinguishing L/S funds from long-bias funds. © 2011
Elsevier B.V.},
Doi = {10.1016/j.jempfin.2011.04.001},
Key = {fds266750}
}
@article{fds266752,
Author = {Edelman, D and Fung, W and Hsieh, DA and Naik, NY},
Title = {Funds of hedge funds: Performance, risk and capital
formation 2005 to 2010},
Journal = {Financial Markets and Portfolio Management},
Volume = {26},
Number = {1},
Pages = {87-108},
Publisher = {Springer Nature},
Year = {2012},
Month = {March},
ISSN = {1555-4961},
url = {http://dx.doi.org/10.1007/s11408-011-0180-z},
Abstract = {Using a comprehensive data set of funds-of-hedge funds, we
extend the results of Fung et al. (J. Finance 63:1777-1803,
2008) (FHNR) with an augmented version of the Fung and Hsieh
(Financ. Anal. J. 60:65-80, 2004a; J. Empir. Finance
18:547-569, 2004b) model to document performance
characteristics from January 2005 to December 2010. We find
that our sample period is divided into three distinct
subperiods: January 2005 to June 2007 (pre-subprime crisis);
July 2007 to March 2009; and April 2009 to December 2010
(post-credit crunch) during which the average fund of hedge
funds delivered positive alpha only in the first subperiod.
We divide the funds of hedge funds sample into those who
have alpha and the rest, which we call beta-only. The
empirical results show a dramatic decline in the population
of alpha producing funds of hedge funds post 2008 compared
to the FHNR findings. When we repeat our analysis with a
synthetic hedge fund index replicator, we find qualitatively
similar results. © 2012 Swiss Society for Financial Market
Research.},
Doi = {10.1007/s11408-011-0180-z},
Key = {fds266752}
}
@article{fds266729,
Author = {Edelman, D and Fung, W and Hsieh, DA},
Title = {Exploring uncharted territories of the hedge fund Industry:
Empirical characteristics of mega hedge fund
firms},
Journal = {Journal of Financial Economics},
Volume = {109},
Number = {3},
Pages = {734-758},
Publisher = {Elsevier BV},
Year = {2013},
Month = {September},
ISSN = {0304-405X},
url = {http://dx.doi.org/10.1016/j.jfineco.2013.04.003},
Abstract = {This paper investigates mega hedge fund management companies
that collectively manage over 50% of the industry's assets,
incorporating previously unavailable data from those that do
not report to commercial databases. We find similarities
among mega firms that report performance to commercial
databases compared with those that do not. We show that the
largest divergences between the performance of reporting and
nonreporting mega firms can be traced to differential
exposure to credit markets. Thus, the performance of
hard-to-observe mega firms can be inferred from observable
data. This conclusion is robust to delisting bias and the
presence of serially correlated returns. © 2013 Elsevier
B.V.},
Doi = {10.1016/j.jfineco.2013.04.003},
Key = {fds266729}
}
@article{fds339313,
Author = {Esquivel, P and Orjuela, A and Barros, MP and Osorio,
C},
Title = {Potential Opportunities and Challenges for Research
Collaboration with Latin America in Agriculture and Food
Science.},
Journal = {Journal of agricultural and food chemistry},
Volume = {65},
Number = {37},
Pages = {8096-8098},
Year = {2017},
Month = {September},
url = {http://dx.doi.org/10.1021/acs.jafc.7b03572},
Doi = {10.1021/acs.jafc.7b03572},
Key = {fds339313}
}
@article{fds355499,
Author = {Fung, W and Hsieh, D and Naik, N and Teo, M},
Title = {Hedge fund franchises},
Journal = {Management Science},
Volume = {67},
Number = {2},
Pages = {1199-1226},
Year = {2021},
Month = {February},
url = {http://dx.doi.org/10.1287/mnsc.2019.3516},
Abstract = {We investigate the growth strategies of hedge fund firms. We
find that firms with successful first funds are able to
launch follow-on funds that charge higher performance fees,
set more onerous redemption terms, and attract greater
inflows. Motivated by the aforementioned spillover effects,
first funds outperform follow-on funds, after adjusting for
risk. Consistent with the agency view, greater incentive
alignment moderates the performance differential between
first and follow-on funds. Moreover, multiple-product firms
underperform single-product firms but harvest greater fee
revenues, thereby hurting investors while benefitting firm
partners. Investors respond to this growth strategy by
redeeming from first funds of firms with follow-on funds
that do poorly. Empirically, the multiple-product firm has
become the dominant business model for the hedge fund
industry.},
Doi = {10.1287/mnsc.2019.3516},
Key = {fds355499}
}
%% Chapters in Books
@misc{fds266726,
Author = {Hsieh, DA and Huizinga, J},
Title = {Gold in the Optimal Portfolio},
Pages = {212-261},
Booktitle = {The Reconstruction of International Monetary
Arrangements},
Publisher = {MacMillan},
Editor = {Aliber, R},
Year = {1987},
Key = {fds266726}
}
@misc{fds266725,
Author = {Hsieh, DA and Gallant, RA and Barnett, W},
Title = {On Fitting a Recalcitrant Series: the Pound/Dollar Exchange
Rate, 1974-83},
Pages = {199-240},
Booktitle = {Nonparametric and Semiparametric Methods in Econometrics and
Statistics, Proceedings of the Fifth International Symposium
in Economic Theory and Econometrics},
Publisher = {Cambridge University Press},
Editor = {Barnett, W and Powell, J and Tauchen, G},
Year = {1991},
ISBN = {0521370906},
Key = {fds266725}
}
@misc{fds319293,
Author = {Hsieh, DA and Gallant, AR and Tauchen, G},
Title = {On Fitting a Recalcitrant Series: the Pound/Dollar Exchange
Rate},
Pages = {199-240},
Booktitle = {Nonparametric and Semiparametric Methods in Econometrics and
and Statistics, Proceedings of the Fifth International
Symposium in Econmic Theory and Econometrics},
Publisher = {Cambridge University Press},
Editor = {Barnett, WA and Powell, J and Tauchen, G},
Year = {1991},
ISBN = {0521370906},
Key = {fds319293}
}
@misc{fds266723,
Author = {Hsieh, DA},
Title = {Estimating the Dynamics of Volatility},
Pages = {507-521},
Booktitle = {Conference on Financial Innovation: 20 Years of
Black/Scholes and Merton},
Publisher = {Fuqua School of Business},
Year = {1993},
Key = {fds266723}
}
@misc{fds266724,
Author = {Hsieh, DA and Fung, W and Leitner, J},
Title = {Exploiting the Interest Rate Differential in Currency
Trading},
Pages = {260-286},
Booktitle = {Strategic Currency Investing: Trading and Hedging in the
Foreign Exchange Market},
Publisher = {Probus Publishing Company},
Editor = {Gitlin, A},
Year = {1993},
Key = {fds266724}
}
@misc{fds266722,
Author = {Hsieh, DA and Kleidon, A},
Title = {Bid-Ask Spreads in Foreign Exchange Markets: Implications
for Models of Asymmetric Information},
Pages = {41-65},
Booktitle = {The Microstructure of Foreign Exchange Markets},
Publisher = {National Bureau of Economic Research},
Editor = {Galli, G and Giovannini, A},
Year = {1996},
ISBN = {0226260003},
Key = {fds266722}
}
@misc{fds319292,
Author = {Hsieh, DA and Kleidon, A},
Title = {Bid-Ask Spreads in Foreign Exchange Markets: Implications
for Models of Asymmetric Information},
Pages = {41-65},
Publisher = {University of Chicago Press},
Editor = {Frankel, J and Galli, G and Giovannini, A},
Year = {1996},
ISBN = {0226260003},
Key = {fds319292}
}
@misc{fds266721,
Author = {Hsieh, DA and Fung, W and Tsatsaronis, K},
Title = {Do Hedge Funds Disrupt Emerging Markets},
Pages = {377-421},
Booktitle = {Wharton-Brookings Papers on Financial Services},
Year = {2000},
Key = {fds266721}
}
@misc{fds338595,
Author = {Hsieh, DA},
Title = {Hedge funds styles},
Journal = {COMPUTATIONAL FINANCE 1999},
Pages = {359-367},
Publisher = {M I T PRESS},
Editor = {AbuMostafa, YS and LeBaron, B and Lo, AW and Weigend,
AS},
Year = {2000},
Month = {January},
ISBN = {0-262-01178-6},
Key = {fds338595}
}
@misc{fds266720,
Author = {Hsieh, DA and Fung, W},
Title = {The Risks in Hedge Fund Strategies: Alternative Alphas and
Alternative Betas},
Booktitle = {Managing the Risks of Alternative Investment
Strategies},
Publisher = {Prentice Hall},
Editor = {Jaeger, L},
Year = {2003},
Key = {fds266720}
}
@misc{fds338594,
Author = {Hsieh, DA},
Title = {What Can Central Bankers Learn from Hedge Fund Replication
Strategies?},
Pages = {331-347},
Year = {2009},
Month = {January},
Abstract = {AbstractThe following sections are included:IntroductionThe
Sample of Large Hedge FundsStyle distribution of large
fundsPrincipal component analysisA Simple 8-Factor Model of
Hedge Fund RiskEquity factorsBond factorsTrend-following
factorsEmerging market factorsExposures of Large Hedge Funds
Using Monthly ReturnsThe effects of serial correlation in
hedge fund returnsExposure of average hedge
fundsCorroboration of Exposures Using Daily Investible
IndicesConclusionReferences},
Key = {fds338594}
}
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