This paper studies predatorytrading: Trading
that induces and/or exploits other investors' need to reduce their
positions. We show that if one trader needs to sell, others also sell
and subsequently buy back the asset. This leads to price overshooting,
and a reduced liquidation value for the distressed trader. Hence, the
market is illiquid when liquidity is most needed. Further, a trader
profits from triggering another trader's crisis, and the crisis can
spill over across traders and across assets.
In the past decades, advanced
probabilistic methods have had significant impact on the field of
finance, both in academia and in the financial industry. Conversely,
financial questions have stimulated new research directions in
probability. In this survey paper, we review some of these developments
and point to some areas that might deserve further investigation. We
start by reviewing the basics of arbitrage pricing theory, with special
emphasis on incomplete markets and on the different roles played by the
'real-world' probability measure and its equivalent martingale measures.
We then focus on the issue of model ambiguity, also called Knightian
uncertainty. We present two case studies in which it is possible to deal
with Knightian uncertainty in mathematical terms. The first case study
concerns the hedging of derivatives, such as variance swaps, in a
strictly path wise sense. The second one deals with capital requirements
and preferences specified by convex and coherent risk measures. In the
final two sections we discuss mathematical issues arising from the
dramatic increase of algorithmic trading in modern financial markets.
This
paper examines the performance of momentum trading strategies in foreign
exchange markets. We find the well-documented profitability of momentum
strategies with equities to hold for currencies as well and to have
continued throughout the 1980s and the 1990s. Our results indicate that
the long/short strategy of buying the most attractive currency and
shorting the least attractive currency obtains average
excess returns that are significantly positive. Of particular note, the
profitability to momentum strategies in foreign exchange markets has
been particularly strong during the latter half of the 1990s. By
examining 354 long/short movingaverage
rules across eight currencies, we show the results are insensitive to
the specification of the trading rule and the base currency for
analysis. We also show that the correlations of the long/short momentum
strategies using differing base currencies are very high - typically
around 0.90. This would indicate that strong/weak momentum currencies
relative to a base currency at a particular time are typically also
strong/weak currencies relative to most other base currencies as well.
Finally, using a bootstrap methodology we show that the performance is
not due to a time-varying risk premium but depends on the underlying
autocorrelation structure of the currency returns. In sum, the results
lend further support to prior momentum studies on equities. The
profitability to momentum-based strategies holds for currencies as well
We
provide a broad empirical investigation of momentum strategies in the
foreign exchange market. We find a significant cross-sectional spread in
excess returns of up to 10% p.a. between past winner and loser
currencies. This spread in excess returns is not explained by
traditional risk factors, it is partially explained by transaction costs
and shows behavior consistent with investor under- and over-reaction.
Moreover, cross-sectional currency momentum has very different
properties from the widely studied carry trade and is not highly
correlated with returns of benchmark technicaltrading
rules. However, there seem to be very effective limits to arbitrage
which prevent momentum returns from being easily exploitable in currency
markets.
Wang, Yu, Cheung, 2014a proposed complex trading strategy called Performance-based Reward Strategy (PRS). PRS combines component rules of 121 moving average (MA) rules and 19 trading
range breakout (TRB) rules. To find the optimal set of parameters for
PRS, the researchers used time variant particle swarm optimization
(TVPSO) algorithm. The objective of the experiment was to measure annual
net profit (ANP) of PRS for trading constituents of NASDAQ100. Wang, Yu, Cheung, 2014b conducted the same experiment but used seven classes of technicaltrading rules resulting in 1,059 component trading
rules. For both of these studies, the researchers conducted the
experiment only one time and PRS’s ANP was better than all component
rules’. Thus both of these studies concluded that PRS outperforms all
component rules. TVPSO algorithm is an approximation algorithm and
running TVPSO only once may bias the conclusion. To improve reliability
of the conclusion, we conduct both experiments 100 times. For both
experiments, we find that mean ANP of PRS was lower than that of the
best of the best component rules. Furthermore, when we replicate PRS, we
find that weight updating equation proposed in both of these studies
was wrong and we propose weight updating equation which we believe both
of these studies used. Furthermore, to speed up the running time of
TVPSO, a simple parallel programming idea for it is proposed.
This
paper approaches risk management from three perspectives: firm-level
risk measurement, governance and incentives, and systemic concerns.
These are three essential dimensions of best practices in risk
management; although we discuss each dimension separately, they are
interrelated. The paper begins with a brief review of salient changes
and unmet challenges in risk measurement in the wake of the financial
crisis. It proceeds with a discussion of the interplay between
volatility regimes and the potential for risk amplification at a system-wide
level through simultaneous risk mitigation at the individual firm
level. Quantitative risk measurement cannot be effective without a sound
corporate risk culture, so the paper then develops a model of
governance that recognizes cognitive biases in managers. The model
allows a comparison of the incentive effects of compensation contracts
and leads to recommendations for improving risk management through
improved contract design. The last section takes a systemic perspective
on risk management. Risk managers must recognize important ways in which
market dynamics deviate from simple,
idealized models of hedging an individual firm’s exposures. Firms’
collective hedging, funding, and collateral arrangements can channel
through the financial system in ways that amplify shocks. Understanding these effects requires an appreciation for the organization of trading operations within firms. The article concludes with a summary and recommendations.
We model a large panel of time series as a VAR where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system
has a network representation in terms of a directed graph representing
predictive Granger relations and an undirected graph representing
contemporaneous partial correlations. A LASSO algorithm called NETS is
introduced to estimate the model. We apply the methodology to analyse a
panel of volatility measures of ninety bluechips. The model captures an
important fraction of the overall variability of the time series and
improves out-of-sample forecasting.
A new factor model
consisting of the market factor, an investment factor, and a
return-on-equity factor is a good start to understanding the
cross-section of expected stock returns. Firms will invest a lot when
their profitability is high and the cost of capital is low. As such,
controlling for profitability, investment should be negatively
correlated with expected returns, and controlling for investment,
profitability should be positively correlated with expected returns. The
new three-factor model reduces the magnitude of the abnormal returns of a wide range of anomalies-based trading strategies, often to insignificance. The model's
performance, combined with its economic intuition, suggests that it can
be used to obtain expected return estimates in practice.
We
provide a mathematical definition of fragility and antifragility as
negative or positive sensitivity to a semi-measure of dispersion and
volatility (a variant of negative or positive “vega”) and examine the
link to nonlinear effects. We integrate model error (and biases) into
the fragile or antifragile context. Unlike risk, which is linked to
psychological notions such as subjective preferences (hence cannot apply
to a coffee cup) we offer a measure that is universal and concerns any
object that has a probability distribution (whether such distribution is
known or, critically, unknown).
We propose a detection of
fragility, robustness, and antifragility using a single
“fast-and-frugal”, model-free, probability free heuristic that also
picks up exposure to model error. The heuristic lends itself to
immediate implementation, and uncovers hidden risks related to company
size, forecasting problems, and bank tail exposures (it explains the forecasting biases). While simple to implement, it outperforms stress testing and other such methods such as Value-at-Risk.
In
this paper, the authors explore the complex, long, and unique process of
accession to the World Trade Organization, with its intertwined
economic, legal, and political dimensions. Referring to country case
studies and sector-specific issues, the paper organizes some of the
current reflections on the topic around three main themes. First, it
explores the rationale of accession to the World Trade Organization: Why
would new members join the WTO? And why would incumbent members let new
members in? Second, it analyzes the World Trade Organization accession
process in detail: What are the main characteristics and challenges of
the accession process? Has it evolved over time, and how? Third, the
paper looks at the implementation of World Trade Organization accession
deals: Is accession the end or the beginning of the story? What are the
implications for the participating countries and the multilateral tradingsystem?
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We propose a detection of fragility, robustness, and antifragility using a single “fast-and-frugal”, model-free, probability free heuristic that also picks up exposure to model error. The heuristic lends itself to immediate implementation, and uncovers hidden risks related to company size, forecasting problems, and bank tail exposures (it explains the forecasting biases). While simple to implement, it outperforms stress testing and other such methods such as Value-at-Risk.