{"id":630,"date":"2015-10-08T12:02:45","date_gmt":"2015-10-08T12:02:45","guid":{"rendered":"http:\/\/quantpedia.com\/?p=630"},"modified":"2019-08-22T05:47:54","modified_gmt":"2019-08-22T05:47:54","slug":"quantpedia-update-8th-october-2015","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-8th-october-2015\/","title":{"rendered":"Quantpedia Update – 8th October 2015"},"content":{"rendered":"

\n\tNew strategies:<\/u><\/strong><\/p>\n

\n\t#280 – Trading the VIX Futures Roll and Volatility Premiums with VIX Options<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> daily
\n\tMarkets traded: <\/strong>equities
\n\tInstruments used for trading:<\/strong> options
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period:<\/strong> 2007 – 2014
\n\tIndicative performance:<\/strong> 22.50%
\n\tEstimated volatility:<\/strong> not stated
\n\tSource paper:<\/strong><\/p>\n

\n\tSimon: Trading the VIX Futures Roll and Volatility Premiums with VIX Options<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2624713<\/a>
\n\tAbstract:
\n\tThis study examines the efficiency of VIX option trading strategies that exploit the VIX futures roll and the often substantial VIX futures volatility premiums from January 2007 through March 2014. The study first assesses the related issue of whether VIX options typically are overpriced by examining long VIX option delta-hedged returns and demonstrates that average losses on front contract calls and puts over 5-business day horizons either are not statistically significant or are economically small. In light of the evidence that VIX option buyers on average do not overpay at all or by much for the limited risk associated with VIX options, the study then turns to whether long VIX option positions can be used to exploit the well-documented tendencies of VIX futures to rise and fall when the VIX futures curve is in backwardation and in contango, respectively, as well as the tendency of VIX futures to build in large ex-ante volatility premiums. The results demonstrate that these defined-risk strategies are highly profitable and offer attractive risk-reward tradeoffs. Moreover, the systematic tendencies of VIX futures have far more power for predicting attractive VIX option returns than the ex-ante volatility premiums built into VIX options. The study also shows that long VIX option strategies importantly benefit from a strong tailwind that owes to the tendency of VIX option implied volatilities to rise with increases in the actual volatilities of underlying VIX futures contracts, as VIX futures move toward settlement and their volatilities rise to the typically higher volatility of the VIX.<\/p>\n

\n\tNew research papers related to existing strategies:<\/strong><\/u><\/p>\n

\n\t#15 – Momentum Effect in Country Equity Indexes<\/strong><\/p>\n

\n\tGuilmin: The Effective Combination of Risk-Based Strategies with Momentum and Trend Following<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2556747<\/a>
\n\tAbstract:
\n\tThe Efficient Market Hypothesis (EMH) has been widely called into question in the investment literature, through two main anomalies: timing and low-volatility anomalies. In this paper, we aim to combine the predictive power of timing and low-volatility strategies to deliver better risk-adjusted portfolio performance. We adopt a two-step approach for a constant dataset composed of 18 country MSCI stock market indices over the 1975-2014 period. First, we use different timing strategies: moving averages and momentum. We select stock market indices based on different moving averages (6, 8, 10, and 12 months), while the momentum strategy ranks the different stock market indices into momentum subsets (low, medium, and high momentum). After the first step using the different timing strategies, the second step consists in building risk-based portfolios (MV, ERC, and MD) as well as 1\/N benchmark portfolios for each of these timing strategies. Our results highlight the effectiveness, the relevance and the robustness of our approach. First, risk-based portfolios using relevant timing strategy indeed provide better returns, lower volatilities, higher Sharpe ratios, and lower Value-at-Risk (VaR) and Expected Shortfall (ES) than traditional risk-based portfolios. The second contribution of our approach features that risk-based strategies provide better risk-adjusted returns and lower VaR and ES than the 1\/N portfolio within a context in which the first step is dedicated to the application of a relevant timing strategy. Finally, among these risk-based portfolios using relevant timing strategies, the MD and MV portfolios usually obtain the best risk-adjusted performance.<\/p>\n

\n\t#54 – Momentum and State of Market (Sentiment) Filters<\/strong><\/p>\n

\n\tRahim: Market Condition and Momentum<\/strong>
\n\thttp:\/\/www.econ.kyushu-u.ac.jp\/~kuchida\/1M-2-2_Khan.pdf<\/a>
\n\tAbstract:
\n\tThis  study  examines  momentum  effect  in  Japanese  stock  returns  conditioned  on  the  market dynamics.  Momentum profits for the Japanese stock returns depending on the market condition arefound  to  be  significant.  When market  is divided into  UP  and  DOWN  states,  momentum profits  are  found  in  the  UP  market  states  but  DOWN  market  states  produces  significant  loss. When UP and DOWN market states are further divided into continuation and reversion (UP-UP, UP-DOWN,  DOWN-UP,  and  DOWN-DOWN),  momentum  profits  are  found  when  market moves  in  the  similar  direction.  Importantly,  significant  momentum  profits  are  found  in  the DOWN states when the market continues to perform poorly. Market reversion, on the other hand, produces significant  loss both  in  the  UP  and  DOWN  markets.  This  study  argues  that  lack  of continuity in the  market  condition  appears  to  be  the reason  for  non-existence of momentum  in the Japanese stock returns for the whole market. However, long-term reversion is not found to be followed  by the  intermediate-term  momentum,  which  invalidates  the  hypothesis  that  investors`  overreaction cause momentum in the continuing markets.<\/p>\n

\n\t#118 – Time Series Momentum Effect<\/strong><\/p>\n

\n\tDudler, Gmur, Malamud: Momentum and Risk Adjustment<\/strong>
\n\thttp:\/\/www.iijournals.com\/doi\/pdfplus\/10.3905\/jai.2015.2015.1.044<\/a>
\n\tAbstract:
\n\tThe goal of this article is therefore to study this inefficiency within the time series momentum (TSMOM) strategies introduced in an important article by Moscowitz, Ooi, and Pedersen [2012]. To this end, we introduce a new class of momentum strategies, risk-adjusted time series momentum (RAMOM) strategies, which are based on averages of past futures returns, normalized by their volatility. We test these strategies on a universe of 64 liquid futures contracts and demonstrate that RAMOM strategies outperform the TSMOM strategies of Moscowitz, Ooi, and Pedersen [2012] for short-, medium-, and long-term momentum strategies. Additionally, RAMOM trading signals have another useful and important feature: They are naturally less dependent on high volatility. In other words, standard TSMOM strategies tend to positively correlate (see, e.g., Hurst et al. [2013]) with a long-straddle position  (long-call, long-put) and, as a result, perform  better in volatile market environments. As  we show, this is much less the case for the  RAMOM returns because, by risk-adjusting  the trading signals according to volatility, we  render RAMOM returns more sensitive to  new information precisely at the time when  volatility is low. As a result, outperformance  of RAMOM relative to TSMOM tends to  be negatively related to volatility.<\/p>\n

\n\tTwo additional related research paper have been included into existing free strategy reviews during last 2 week:<\/strong><\/u><\/p>\n

\n\t#3 – Sector Momentum – Rotational System<\/strong><\/p>\n

\n\tHuhn: Industry Momentum: The Role of Time-Varying Factor Exposures and Market Conditions<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2650378<\/a>
\n\tAbstract:
\n\tThis paper focuses on momentum strategies based on recent and intermediate past returns of U.S. industry portfolios. Our empirical analysis shows that strategies based on intermediate past returns yield higher mean returns. Moreover, strategies involving both return specifications exhibit time-varying factor exposures, especially the Fama and French (2015) five-factor model. After risk-adjusting for these dynamic exposures, the profitability of industry momentum strategies diminishes and becomes insignificant for strategies based on recent past returns. However, most strategies built on intermediate past returns remain profitable and highly significant. Further analyses reveal that industry momentum strategies are disrupted by periods of strong negative risk-adjusted returns. These so-called momentum crashes seem to be driven by specific market conditions. We find that industry momentum strategies are related to market states and to the business cycle. However, there is no clear evidence that industry momentum can be linked to market volatility or sentiment.<\/p>\n

\t#14 – Momentum Effect in Stocks<\/strong><\/p>\n

\n\tvan Oord: Optimization of Equity Momentum: (How) Does it Work?<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2653680<\/a>
\n\tAbstract:
\n\tStandard mean-variance optimized momentum outperforms the traditional equally weighted momentum strategy if the expected return vector used reflects momentum's top and bottom only characteristic. This top and bottom only characteristic is the phenomenon that only the stocks in the top decile of momentum's ranking outperform and that only stocks in the bottom decile underperform, while all stocks in the intermediate deciles of the ranking have similar performance. If the optimization does not take this phenomenon into account the portfolio is also long the deciles 2 to 5 and short the deciles 6 to 9, while all these positions thus do not add anything to the return of the strategy. A new simplified bootstrapping methodology shows that the Sharpe-ratio of 52.8 percent of the optimized portfolio is significantly higher (p-value of 0.006) than the Sharpe-ratio of 29.3 percent for traditional equally weighted momentum. The optimized portfolio also exhibit less time-varying equity risk factor return exposures than traditional momentum and as such have more stable returns over the business cycle and have smaller drawdowns.<\/p>\n","protected":false},"excerpt":{"rendered":"

\n\tOne new strategy has been added:<\/p>\n

\n\t#280 – Trading the VIX Futures Roll and Volatility Premiums with VIX Options<\/strong><\/p>\n

\n\tThree new related research papers have been included into existing strategy reviews. And two additional related research paper have been included into existing free strategy reviews during last 2 weeks.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-630","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/630","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=630"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/630\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=630"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=630"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=630"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}