- Regimes & Correlations
- Risk On/Off Bull/Bear Markets
- The Vix
- Normie Starter Pack
- Piglet Pack
- Pig Pack
- Boar Pack
Regime Switches in Interest Rates (1998) by Geert Bekaert (Columbia Business School) and Andrew Ang (BlackRock)
Wifey comment:
We show that the regimes in interest rates correspond reasonably well with business cycles, at least in the US.
Abstract:
This paper examines the econometric performance of regime switching models for interest rate data from the US, Germany and the UK. There is strong evidence supporting the presence of regime switches but univariate models are unlikely to yield consistent estimates of the model parameters. Regime-switching models incorporating international short rate and term spread information forecast better, match sample moments better, and classify regimes better than univariate models. We show that the regimes in interest rates correspond reasonably well with business cycles, at least in the US. This may explain why regime-switching models forecast interest rates better than single regime models. Finally, the non-linear interest rate dynamics implied by regime switching models have potentially important implications for the macro-economic literature documenting the effects of monetary policy shocks on economic aggregates. Moreover, the implied volatility and drift functions are rich enough to resemble those recently estimated using non-parametric techniques.
Handling risk-on/risk-off dynamics with correlation regimes and correlation networks (2015) by Jochen Papenbrock and Peter Schwender
Abstract:
In this paper, we present a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013. These correlation regimes have been significantly different since the financial crisis of 2008 than they were previously; cluster tracking shows that asset classes are now less separated. We identify distinct “risk-on” and “risk-off” assets with the help of correlation networks. In addition to visualizing, we quantify these observations using suitable metrics for the clusters and correlation networks. The framework will be useful for financial risk management, portfolio construction, and asset allocation.
Can Market Regimes Really be Timed with Historical Volatility? (2021) by Richard McGee (Smurfit Business School)
Abstract:
Recent research findings suggest long-term investor utility benefits through scaling expected returns by recent realized volatility. We test for utility gains to volatility timing using a utility regime-based methodology to classify investor-specific market investment regimes based solely on recent realized volatility levels. Under this framework we find limited informational content in using recent realized volatility to forecast utility regimes for the market index. To reconcile our findings we replicate work by Moreira and Muir (2017) and find that their reported Sharpe ratio gains through volatility-managing the US market factor do not appear to be statistically significant. We find that their scheme under-performs buy and hold in terms of Sharpe ratio over 30 of the 70 twenty year sub-periods in our sample (58 out of 70 for an un-leveraged investor). Furthermore, the historical out-performance of volatility management for the market index is highly sensitive to the timing of re-balancing within a month, suggesting that the strategy may not be robust to the precise timing of key market events relative to volatility changes. Strategy adopters should be aware that this timing is not guaranteed to line up favorably over future investment periods.
Oil and Fiscal Policy Regimes (2021) by Hilde Bjornland (Norwegian School of Management), Roberto Casarin (University Ca'Foscari of Venice), Marco Lorusso (Newcastle University Business School), and Francesco Ravazzolo (Free University of Bozen-Bolzano)
Abstract:
We analyse fiscal policy responses in oil rich countries by developing a Bayesian regimes-witching panel country analysis. We use parameter restrictions to identify procyclical and countercyclical fiscal policy regimes over the sample in 23 OECD and non-OECD oil producing countries. We find that fiscal policy is switching between pro- and countercyclial regimes multiple times. Furthermore, for all countries, fiscal policy is more volatile in the countercyclical regime than in the procyclical regime. In the procyclical regime, however, fiscal policy is systematically more volatile and excessive in the non-OECD (including OPEC) countries than in the OECD countries. This suggests OECD countries are able to smooth spending and save more than the non-OECD countries. Our results emphasize that it is both possible and important to separate a procyclical regime from a countercyclical regime when analysing fiscal policy. Doing so, we have encountered new facts about fiscal policy in oil rich countries.
Regime Changes and Financial Markets (2011) by Andrew Ang (BlackRock) and Allan Timmermann (UCSD Centre for Economic Policy Research)
Abstract:
Regime switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. While the regimes captured by regime switching models are identified by an econometric procedure, they often correspond to different periods in regulation, policy, and other secular changes. In empirical estimates, the regime switching means, volatilities, autocorrelations, and cross-covariances of asset returns often differ across regimes, which allow regime switching models to capture the stylized behavior of many financial series including fat tails, heteroskedasticity, skewness, and time-varying correlations. In equilibrium models, regimes in fundamental processes, like consumption or dividend growth, strongly affect the dynamic properties of equilibrium asset prices and can induce non-linear risk-return trade-offs. Regime switches also lead to potentially large consequences for investors' optimal portfolio choice.
Managing Risks in a Risk-On/Risk-Off Environment (2012) by Marcos Lopez de Prado (Cornell University Operations Research and Industrial Engineering)
Abstract:
Every structure has natural frequencies. Minor shocks in these frequencies can bring down any structure, e.g. a bridge. An Investment Universe also has natural frequencies, characterized by its eigenvectors. A concentration of risks in the direction of any such eigenvector exposes a portfolio to the possibility of greater than expected losses (indeed, maximum risk for that portfolio size), even if that portfolio is below the risk limits. This is particularly dangerous in a risk-on/risk-off regime. Managing Risk is not only about limiting its amount, but also controlling how this amount is concentrated around the natural frequencies of the investment universe.
Market regime classification with signatures (2021) by Antoine (Jack) Jacquier (Imperial College London), Paul Bilokon (Imperial College London), and Conor McIndoe
Abstract:
We provide a data-driven algorithm to classify market regimes for time series. We utilise the path signature, encoding time series into easy-to-describe objects, and provide a metric structure which establishes a connection between separation of regimes and clustering of points.
Risk-On/Risk-Off: Financial Market Response to Investor Fear (2016) by Lee A. Smales (University of Western Australia)
Abstract:
This article examines the relationship between changes in the level of investor fear (measured by VIX) and financial market returns. We document a statistically significant relationship, across asset classes, consistent with a flight to quality as investor fear increases. As VIX increase there is a decline in stock markets, bond yields, and high-yielding currencies (AUD and NZD), while the USD appreciates. Returns become more sensitive to changes in the level of investor fear during the financial crisis of 2008-09, when investor fear spikes sharply. Analysis of market returns subsequent to periods of extreme levels of investor fear suggests some predictive ability for future returns, and it is suggested that this may be used to develop a profitable trading strategy. Taken together, the results confirm that financial market returns are closely related to prevailing levels of investor fear.
Risk On-Risk Off: A Regime Switching Model for Active Portfolio Management (2020) by Jose P. Dapena (University of CEMA), Juan A. Serur (NYU), and Julian Ricardo Siri (University of CEMA)
Wifey comment:
Careful, not the regime. Long bonds is not the hedge, short bonds is. If long-only use cash, we know this is the best hedge for inflation and/or equity risk. GLD/GDX also.
Abstract:
Unlike passive management, where investors almost do not buy and sell securities, active management involves a set of trading rules that govern investment decisions regarding mainly market timing. In this paper, we take the basics of active management and the two fund separation approach, to exploit the fact that an investor can switch between the market portfolio and the risk free asset according to the perceived state of the nature. Our purpose is to evaluate if there is an active management premium by testing performance with our own non-conventional multifactor model, constructed with a Hidden Markov Model which depending on the market states signaled by the level of volatility spread. We have documented that effectively, there is present a premium for actively manage the strategies, giving evidence against the idea that “active managers” destroy capital. We then propose the volatility spread as the active management factor into the Carhart's model used to evaluate trading strategies with respect to a benchmark portfolio.
On the Global Impact of Risk-off Shocks and Policy-put Frameworks (2019) by Ricardo J. Caballero (MIT) and Gunes Kamber (Bank for International Settlements)
Abstract:
Global risk-off shocks can be highly destabilizing for financial markets and, absent an adequate policy response, may trigger severe recessions. Policy responses were more complex for developed economies with very low interest rates after the Global Financial Crisis (GFC). We document, however, that the unconventional policies adopted by the main central banks were effective in containing asset price declines. These policies impacted long rates and inspired confidence in a policy-put framework that reduced the persistence of risk-off shocks. We also show that domestic macroeconomic and financial conditions play a key role in benefiting from the spillovers of these policies during risk-off episodes. Countries like Japan, which already had very low long rates, benefited less. However, Japan still benefited from the reduced persistence of risk-off shocks. In contrast, since one of the main channels through which emerging markets are historically affected by global risk-off shocks is through a sharp rise in long rates, the unconventional monetary policy phase has been relatively benign to emerging markets during these episodes, especially for those economies with solid macroeconomic fundamentals and deep domestic financial markets. We also show that unconventional monetary policy in the US had strong effects on long interest rates in most economies in the Asia-Pacific region (which helps during risk-off events but may be destabilizing otherwise we do not take a stand on this tradeoff).
in progress
in progress
"What is a Quant?" by Quantopian, 2:39.
"'How to Become a Quant? A Career in Quant Finance' Panel from QuantCon NYC 2018" by Quantopian, 1:00:45
"Become a Quant" by Quantopian, 0:49
Is the world going quants mad?
"Is the world going quants mad? Dr Paul Wilmott" by The Open University Business School, 23:11
"Richard Feynman. Why." by firewalker, 7:32
Machine Learning and Statistics
"Machine Learning and Statistics: Don't Mind the Gap. By Thomas Wiecki at ODSC Europe 2018" by Thomas Wiecki, 52:16
https://gist.github.com/ih2502mk/50d8f7feb614c8676383431b056f4291
Books by Ernest P. Chan (https://www.epchan.com/books/)
Quantitative Trading (Amazon)
Dr. Chan’s first book Quantitative Trading, now in Second Edition, is addressed to traders who are new to the field. It covers basics such as how to find and evaluate trading strategies, the practice and common pitfalls of backtesting, example strategies such as mean reversion of ETF pairs and seasonal futures trading, and optimal leverage and asset allocation through Kelly’s formula.
Algorithmic Trading: Winning Strategies and Their Rationale (Amazon)
Ernie’s second book Algorithmic Trading: Winning Strategies and Their Rationale is an in-depth study of two types of strategies: mean reverting and momentum. It delves into the reasons certain markets display either mean reversion or momentum, and describes the common techniques that can exploit these profit opportunities. Numerous strategy examples are drawn from stocks, ETFs, futures, and currencies.
Machine Trading: Deploying Computer Algorithms To Conquer the Market (Amazon)
Ernie’s third book Machine Trading: Deploying Computer Algorithms To Conquer the Markets covers a variety of advanced quantitative trading and investment techniques from state space models to machine learning, applicable to a variety of instruments from ETF’s to options. Readers will find most of the materials quite accessible to anyone who has some experience in a quantitative field.
- CAPM Alpha Ranking Strategy on Dow 30 Companies
- Combining Mean reversion and Momentum in Forex Market
- Pairs Trading-Cpula vs Cointegration
- The Dynamic Breakout II Strategy
- Dual Thrust Trading Algorithm
- Can Cruide Oil Predict Equity Returns
- Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach
- The Momentum Strategy Based on the Low Frequency Component of Forex Market
- Stock Selection Strategy Based on Fundamental Factors
- Short-Term Reversal Strategy in Stocks
- Fundamental Factor Long Short Strategy
- Asset Class Trend Following
- Asset Class Momentum
- Residual Momentum
- Sector Momentum
- Overnight Anomaly
- Forex Carry Trade
- Volatility Effect in Stocks
- Forex Momentum
- Pairs Trading with Stocks
- Short Term Reversal
- Momentum Effect in Stocks
- Momentum Effect in Country Equity Indexes
- Mean Reversion Effect in Country Equity Indexes
- Liquidity Effect in Stocks
- Volatility Risk Premium Effect
- Momentum Effect in Commodities Futures
- Small Capitalization Stocks Premium Anomaly
- Paired Switching
- Term Structure Effect in Commodities
- Momentum Effect Combined with Term Structure in Commodities
- Book-to-Market Value Anomaly
- Gold Market Timing
- Turn of the Month in Equity Indexes
- Momentum - Short Term Reversal Strategy
- Pairs Trading with Country ETFs
- Sentiment and Style Rotation Effect in Stocks
- Asset Growth Effect
- Momentum and State of Market Filters
- Accrual Anomaly
- Momentum in Mutual Fund Returns
- Momentum and Style Rotation Effect
- Trading with WTI Brent Spread
- Momentum Effect in REITs
- Option Expiration Week Effect
- Earnings Quality Factor
- January Effect in Stocks
- Momentum and Reversal Combined with Volatility Effect in Stocks
- ROA Effect within Stocks
- January Barometer
- Lunar Cycle in Equity Market
- VIX Predicts Stock Index Returns
- Combining Momentum Effect with Volume
- Short Term Reversal with Futures
- Pre-holiday Effect
- Beta Factors in Stocks
- Exploiting Term Structure of VIX Futures
- 12 Month Cycle in Cross-Section of Stocks Returns
- Momentum Effect in Stocks in Small Portfolios
- Value Effect with Countries
- Beta Factor in Country Equity Indexes
- Price to Earnings Anomaly
- Fama French Five Factors
- Mean-Reversion Statistical Arbitrage Strategy in Stocks
- Expected Idiosyncratic Skewness
- Risk Premia in Forex Markets
- Seasonality Effect based on Same-Calendar Month Returns
- Standardized Unexpected Earnings
- Price and Earnings Momentum
- Improved Momentum Strategy on Commodities Futures
- Commodities Futures Trend Following
- Forecasting Stock prices using a Temporal CNN model
- Leveraged ETFs with Systematic Risk Management
- Ichimoku Clouds in the Energy Sector
- Intraday ETF Momentum
- Intraday Arbitrage Between Index ETFs
- Optimal Pairs Trading
- G-Score Investing
- SVM Waveley Forecasting
- Gradient Boosting Model
- Using News Sentiment to Predict Price Direction of Drug Manufacturers
- Gaussian Naive Bayes Model
in progress
in progress
in progress