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Some of you do not like reading journal articles – but most do. Therefore from now on I will post academic stuff in separate posts, and those who just want to follow the market-moving news can keep up with my regular posts. So, here is my first “Back to School” link fest.
The first set of links from Cato Institute’s conference are worth your time even if you hate published research – they are just bullet points and you can get a full conference knowledge in 15 minutes flat! How’s that for value! Most of the papers here are very recent, or just happen to be thematically so well-related to others that they were included here.
– MoreLiver
At the Cato Institute’s 29th Annual Monetary Conference – The Aleph Blog
Bullet point summaries of the talks. Interesting stuff! Part 1 (Ron Paul), Part 2 (Benn Steil, George Melloan, Gerald P. O’Driscoll Jr.), Part 3 (Jeffrey M. Lacker, Allan H. Meltzer, George Selgin, Roger Garrison), Part 4 (Robert Zoellick, Sebastian Mallaby), Part 5 (Steve H. Hanke, Richard H. Timberlake, Judy Shelton, Lawrence H. White), Part 6 (Alan Reynolds, James Grant, Kevin Dowd, Kurt Schuler), Part 7 (John A. Allison), Epiloque
Research Review: Managing Asset Allocation – The Capital Spectator
Summaries of six recent research articles: Testing Rebalancing Strategies for Stock-Bond Portfolios · Recessions and balanced portfolio returns · Investing for the Long Run · Paired-Switching for Tactical Portfolio Allocation · Tactical Commodity Allocation and the Theory of Storage
DISTRIBUTIONS, OPTION DATA
Does Risk-Neutral Skewness Predict the Cross-Section of Equity Option Portfolio Returns? – SSRN
We find a strong negative relation between implied risk-neutral skewness and the returns of the skewness assets, consistent with a positive skewness preference. The returns are not explained by well-known market, size, book-to-market, momentum, and short-term reversal factors. Additional volatility, stock, and option market factors also fail to explain the portfolio returns. Neither commonly used metrics of portfolio risk (standard deviation, value-at-risk, and expected shortfall), nor analyses of factor sensitivities provide evidence supporting a risk-based explanation of the portfolio returns.
Risk-Neutral Skewness: Return Predictability and Its Sources – SSRN
Using data on all U.S. exchange-traded individual stock options, we show that the currently observed option-implied ex ante skewness is positively related to future stock returns. This contrasts with the existing evidence that uses historical stock or option data to estimate skewness and finds a negative skewness-return relation.
Using data on all U.S. exchange-traded individual stock options, we show that the currently observed option-implied ex ante skewness is positively related to future stock returns. This contrasts with the existing evidence that uses historical stock or option data to estimate skewness and finds a negative skewness-return relation.
Improving Portfolio Selection Using Option-Implied Volatility and Skewness – SSRN
Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility, but does not improve the Sharpe ratio or certainty-equivalent return; option-implied correlation does not improve any of the metrics; however, expected returns estimated using information in the volatility risk premium and option-implied skewness increase substantially both the Sharpe ratio and certainty-equivalent return, even after prohibiting shortsales and accounting for transactions costs.
Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility, but does not improve the Sharpe ratio or certainty-equivalent return; option-implied correlation does not improve any of the metrics; however, expected returns estimated using information in the volatility risk premium and option-implied skewness increase substantially both the Sharpe ratio and certainty-equivalent return, even after prohibiting shortsales and accounting for transactions costs.
Implied Volatility Spreads, Skewness and Expected Market Returns – SSRN
This paper investigates the intertemporal relation between implied volatility spreads and expected returns on the aggregate stock market. The results show a significantly negative link between expected future returns and the spread between the implied volatilities of out-of-the-money put and at-the-money call options written on the S&P 500 index. We argue that this relation is driven by information flow from option markets to stock markets rather than volatility spreads acting as a proxy for skewness.
This paper investigates the intertemporal relation between implied volatility spreads and expected returns on the aggregate stock market. The results show a significantly negative link between expected future returns and the spread between the implied volatilities of out-of-the-money put and at-the-money call options written on the S&P 500 index. We argue that this relation is driven by information flow from option markets to stock markets rather than volatility spreads acting as a proxy for skewness.
Improving Portfolio Selection Using Option-Implied Volatility and Skewness – SSRN
Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility, but does not improve the Sharpe ratio or certainty-equivalent return; option-implied correlation does not improve any of the metrics; however, expected returns estimated using information in the volatility risk premium and option-implied skewness increase substantially both the Sharpe ratio and certainty-equivalent return, even after prohibiting shortsales and accounting for transactions costs.
A New Simple Approach for Constructing Implied Volatility Surfaces – Columbia (pdf)
(presentation) Peter Carr and Warren Liu: Our new approach generates very promising results. Two models with extreme simplicity: The whole implied volatility surface becomes solutions to quadratic equations | 6th grade math. Great performance on both currency options and equity index options. 100 times faster than standard option pricing models, ideal for automated options market making.
CLUSTERING
Tracing the temporal evolution of clusters in a financial stock market – arXiv
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies.
Clustering Time Series Data Stream - A Literature Survey – arXiv
This paper presents a survey on various clustering algorithms available for time series datasets. Moreover, the distinctiveness and restriction of previous research are discussed and several achievable topics for future study are recognized. Furthermore the areas that utilize time series clustering are also summarized.
Identification of Clusters of Investors from Their Real Trading Activity in a Financial Market – SSRN
We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that many statistically detected clusters of investors show a very high degree of synchronization in the time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and we find that several of them show an over-expression of specific categories of investors.
OTHER
Investing in Stock Market Anomalies – SSRN
The results indicate that popular investment choices such as value and small stocks do not dominate growth and big stocks. However, the short-term reversal and momentum strategies create efficient investment alternatives. Bilateral comparisons of stock market anomalies provide evidence for the superior performance of size, short-term reversal, and momentum for 1-month to 12-month horizon and book-to-market and long-term reversal for longer term horizons of 3 to 5 years. The relative strength of small, value, momentum-winner, short-term and long-term losers becomes more prevalent when the time-varying conditional distributions are examined.
Momentum in Microstructure – Kellog (pdf)
We explore the underlying causes of predictable short-term price movements, the link between short-term and longer-term predictable price movements, and the types of traders that trade in a way that is consistent with these movements. Heston, Korajczyk, and Sadka (2010) find that there is predictability in the cross-section of half-hour returns at 24-hour intervals, which we define as micro-momentum. We find that micro-momentum is particularly strong in negative returns, and in the opening half-hour of the trading day, which is consistent with the notion that liquidity clusters at the opening half-hour, and that traders patiently sell within opening half-hours across days.
Dark Pools: Theory and Practice – Pragma Securities (pdf)
(presentation) Dark Pools and dark orders offers unique opportunity for liquidity. Using and interacting with dark pools is a huge challenge. Problems like trade-out, allocation, anti-gaming, adverse selection, pool vetting are researched extensively in the industry.
Carry Trade: Beyond the Fama Regression – (pdf)
(presentation on FX carry trade) Volatility is a key determinant of carry trade returns (realized / implied), Fama coefficients regime dependent, level and slope related to currency risk premium
Stochastic Control Theory and High Frequency Trading – Knight (pdf)
Stochastic Control Theory provides a rigorous framework for making decisions under conditions of uncertainty. High Frequency Trading decisions lend themselves to be cast into such a framework. Even the simplest of market models leads to very complicated differential equations. BUT: Physics, Engineering, Operations Research, and two decades of derivatives pricing all provide a wealth of tools for solving the resulting HJB equations. Very interesting approach which is only now being explored.
Investing in Commodity Indices: Risk Factors and Trading Strategies – Erasmus (pdf)
MSc thesis: This paper researches risk factors and trading strategies using 27 Dow Jones-UBS commodity indices. A stepwise regression procedure with several criteria is established that leads to the final time-series risk model choice. It consists of sector, liquidity, materials sector equity, momentum and non-commercial hedging pressure factors. The model has an average out-of-sample R2 of 0.57.
Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns – SSRN
Portfolio-level analyses and firm-level cross-sectional regressions indicate a negative and significant relation between the maximum daily return over the past one month (MAX) and expected stock returns. Average raw and risk-adjusted return differences between stocks in the lowest and highest MAX deciles exceed 1% per month.
Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence – SSRN
Recent empirical evidence suggests that the variance risk premium, or the difference between risk-neutral and statistical expectations of the future return variation, predicts aggregate stock market returns, with the predictability especially strong at the 2-4 month horizons. We provide extensive Monte Carlo simulation evidence that statistical finite sample biases in the overlapping return regressions underlying these findings can not "explain" this apparent predictability
Recent empirical evidence suggests that the variance risk premium, or the difference between risk-neutral and statistical expectations of the future return variation, predicts aggregate stock market returns, with the predictability especially strong at the 2-4 month horizons. We provide extensive Monte Carlo simulation evidence that statistical finite sample biases in the overlapping return regressions underlying these findings can not "explain" this apparent predictability
Risk measures for autocorrelated hedge fund returns – Banca d’Italia (pdf)
Standard risk metrics tend to underestimate the true risks of hedge funds because of serial correlation in the reported returns. Getmansky, Lo, and Makarov (2004) derive mean, variance, Sharpe ratio, and beta formulae adjusted for serial correlation. Following their lead, we derive adjusted downside and global measures of individual and systemic risks. We distinguish between normally and fat tailed distributed returns and show that adjustment is particularly relevant for downside risk measures in the case of fat tails. A hedge fund case study reveals that the unadjusted risk measures considerably underestimate the true extent of individual and systemic risks.