Publications of David A. Hsieh

%% Books   
@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}
}

@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}
}


%% Journal Articles   
@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},
   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}
}

@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},
   Doi = {10.1021/acs.jafc.7b03572},
   Key = {fds339313}
}

@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},
   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{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},
   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{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},
   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{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{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},
   Doi = {10.2469/faj.v65.n3.6},
   Key = {fds266751}
}

@article{fds266748,
   Author = {Fung, W and Hsieh, DA and Naik, NY and Ramadorai,
             T},
   Title = {Hedge funds: Performance, risk, and capital
             formation},
   Journal = {The Journal of Finance},
   Volume = {63},
   Number = {4},
   Pages = {1777-1803},
   Publisher = {WILEY},
   Year = {2008},
   Month = {August},
   ISSN = {0022-1082},
   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{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{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},
   Doi = {10.2469/cp.v23.n3.4262},
   Key = {fds312632}
}

@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{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},
   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{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},
   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{fds312633,
   Author = {Fung, W and Hsieh, DA},
   Title = {Risk in Fixed-Income Hedge Fund Styles},
   Journal = {Journal of Fixed Income},
   Volume = {12},
   Number = {2},
   Pages = {6-27},
   Publisher = {Pageant Media US},
   Year = {2002},
   Month = {September},
   ISSN = {1059-8596},
   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},
   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},
   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{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},
   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{fds266743,
   Author = {Fung, W and Hsieh, DA},
   Title = {Performance characteristics of hedge funds and commodity
             funds: Natural vs. spurious biases},
   Journal = {The Journal of Financial and Quantitative
             Analysis},
   Volume = {35},
   Number = {3},
   Pages = {291-307},
   Publisher = {JSTOR},
   Year = {2000},
   Month = {January},
   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},
   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{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},
   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},
   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{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},
   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},
   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
             repository},
   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{fds312634,
   Author = {Fung, W and Hsieh, DA},
   Title = {Global Yield Curve Event Risks},
   Journal = {Journal of Fixed Income},
   Volume = {6},
   Number = {2},
   Pages = {37-48},
   Publisher = {Pageant Media US},
   Year = {1996},
   Month = {September},
   ISSN = {1059-8596},
   Doi = {10.3905/jfi.1996.408175},
   Key = {fds312634}
}

@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},
   Doi = {10.2469/faj.v51.n4.1921},
   Key = {fds312635}
}

@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},
   Doi = {10.2307/2329084},
   Key = {fds328083}
}

@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},
   Doi = {10.2469/faj.v49.n4.75},
   Key = {fds312636}
}

@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},
   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 = {The 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{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},
   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{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{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 and
             Money},
   Volume = {1},
   Pages = {61-71},
   Publisher = {Elsevier},
   Year = {1991},
   ISSN = {1042-4431},
   Key = {fds312638}
}

@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{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},
   Doi = {10.1086/296466},
   Key = {fds312640}
}

@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},
   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{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},
   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{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{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},
   Doi = {10.1214/aos/1176350359},
   Key = {fds312642}
}

@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},
   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{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{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},
   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{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{fds312645,
   Author = {Hsieh, DA and Manski, C and McFadden, D},
   Title = {estimation of response probabilities from
             augmented},
   Journal = {Journal of the American Statistical Association},
   Volume = {80},
   Number = {391},
   Pages = {651-662},
   Publisher = {Taylor & Francis: SSH Journals},
   Year = {1985},
   ISSN = {1537-274X},
   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{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},
   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},
   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{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},
   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{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},
   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},
   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}
}


%% Chapters in Books   
@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}
}

@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{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{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{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{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{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{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}
}