Author: Deniz Kebabci
Publisher:
ISBN:
Category :
Languages : en
Pages : 48
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[This paper proposes a Gibbs Sampling approach to modeling returns on industry portfolios. We examine how parameter uncertainty in the returns process with regime shifts affects the optimal portfolio choice in the long run for a static buy-and-hold investor. We find that after we incorporate parameter uncertainty and take into account the possible regime shifts in the returns process, the allocation to stocks can be smaller in the long run. We find this result to be true for both the NASDAQ portfolio and the individual high tech and manufacturing sector portfolios. Finally, we include dividend yields and the Treasury bill rate as predictor variables in our model with regime switching returns and find that the effect of these predictor variables is minimal: the allocation to stocks is still generally smaller in the long run.
Author: Saurabh Gokhale
Publisher:
ISBN:
Category :
Languages : en
Pages : 58
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We propose to separate returns and macro-indicators into regimes and use regime-specific mean returns and covariances for better portfolio construction. We fit a multivariate Gaussian mixture process to observable regime indicators along with a Hidden Markov Model for the unobservable state. We then use the fitted regimes and transition matrix to construct different portfolios based on a probability-weighted average of returns and covariances. Our backtesting uses long-short factor returns as well as industry returns. We find the evidence that regime aware optimizations perform better than the popular mean-variance optimization without assumptions of regimes and has higher out-of-sample expected return and lower skewness, kurtosis, and drawdowns.
Author: Deniz Kebabci
Publisher:
ISBN:
Category :
Languages : en
Pages : 149
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How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation, Allocation to Industry Portfolios under Markov Switching Returns, addresses the effect of parameter estimation error on the relation between asset holdings and the investment horizon. This paper assumes that returns follow a regime switching process with unknown parameters. Parameter uncertainty is accounted for through a Gibbs sampling approach. After accounting for parameter estimation error, buy-and-hold investors are generally found to allocate less to stocks the longer the investment horizon. When the dividend yield and T-bill rates are included as predictor variables, the effect of these predictor variables is minimal, and the allocation to stocks is still smaller, the longer the investor's horizon. The second chapter of my dissertation, Portfolio Choice Implications of Parameter and Model Uncertainty in Factor Models, uses industry portfolios to examine the implications of incorporating uncertainty about a range of (conditionally) linear factor models. The paper specifically examines a CAPM, a linear factor model with different predictor variables (dividend yield, price to book ratio, price to earnings ratio, and price to sales ratio) and a time-varying CAPM specification. All approaches incorporate parameter uncertainty in a mean-variance framework. Time-varying CAPM specifications are intuitive in the sense that one cannot expect the environment for each industry to stay constant through time, and so the underlying parameters can be expected to be time-varying as well. Accounting for time- variation in market betas improves the portfolio performance as measured, e.g., by the Sharpe ratio compared to both an unconditional CAPM and a linear factor model with different predictor variables. The paper also looks at the implications for portfolio performance of utilizing a Black-Litterman approach versus a standard mean-variance approach in the asset allocation step. The former can be thought as a model averaging approach and thus can be expected to help dealing with model uncertainty besides the parameter estimation uncertainty. The third chapter of my dissertation, Style Investing with Uncertainty, develops methods to look at style investing. This paper analyzes the determinants that affect style investing, such as style momentum, and predictor variables such as different macro variables (e.g. yield spread, inflation, term structure, industrial production, etc.) and looks at how learning about these variables affects the predictability of returns. Uncertainty in this paper is incorporated using a time-varying parameter model. Returns on style portfolios such as value and size appear to be related to inflation and other macro variables.
Author: Songsak Sriboonchitta
Publisher: Springer Nature
ISBN: 3030972739
Category : Technology & Engineering
Languages : en
Pages : 762
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Book Description
This book describes state-of-the-art economic ideas and how these ideas can be (and are) used to make economic decision (in particular, to optimally allocate assets) and to gauge the results of different economic decisions (in particular, by using optimal transport methods). Special emphasis is paid to machine learning techniques (including deep learning) and to different aspects of quantum econometrics—when quantum physics and quantum computing models are techniques are applied to study economic phenomena. Applications range from more traditional economic areas to more non-traditional topics such as economic aspects of tourism, cryptocurrencies, telecommunication infrastructure, and pandemic. This book helps student to learn new techniques, practitioners to become better knowledgeable of the state-of-the-art econometric techniques, and researchers to further develop these important research directions
Author: Walter Zucchini
Publisher: CRC Press
ISBN: 1482253844
Category : Mathematics
Languages : en
Pages : 370
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Book Description
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data
Author: Allan Timmermann
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This paper studies asset allocation decisions in the presence of regime switching in asset returns. We find evidence that four separate regimes - characterized as crash, slow growth, bull and recovery states - are required to capture the joint distribution of stock and bond returns. Optimal asset allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in asset returns.
Author: Mr.Allan Timmermann
Publisher: International Monetary Fund
ISBN: 1451847270
Category : Business & Economics
Languages : en
Pages : 51
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Book Description
A perennial question in international finance is to what extent stock returns are influenced by country-location, as opposed to industry-affiliation, factors. This paper develops a novel methodology to measure these effects, in which portfolios mimicking "pure" country and industry factors are first constructed and their joint dynamics then modeled as regime-switching processes. Estimation using global firm-level data allows us to identify well-defined volatility states over the past thirty years and shows that the contribution of the industry factor becomes systematically more prominent during high global volatility states, while the country factor contribution declines. Using the model's estimates, we find that portfolio diversification possibilities vary considerably across economic states.
Author: Massimo Guidolin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This paper investigates the international asset allocation effects of time-variations in higher-order moments of stock returns such as skewness and kurtosis. In the context of a four-moment International Capital Asset Pricing Model (ICAPM) specification that relates stock returns in five regions to returns on a global market portfolio and allows for time-varying prices of covariance, co-skewness, and co-kurtosis risk, we find evidence of distinct bull and bear regimes. Ignoring such regimes, an unhedged US investor's optimal portfolio is strongly diversified internationally. The presence of regimes in the return distribution leads to a substantial increase in the investor's optimal holdings of US stocks, as does the introduction of skewness and kurtosis preferences.
Author: Hening Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Author: Edwin O. Fischer
Publisher:
ISBN:
Category :
Languages : en
Pages :
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