Daily stock market return distributions seem to have tails that are much fatter than Normal Distribution models. This paper examines the possibility of the Laplace Distribution as a better alternative for modeling daily stock returns. Visual inspection of Q-Q plots seem to confirm the Laplace Distribution better fit to the data. The Laplace Distribution also managed to outperform the Normal Distribution in the K-S statistical tests, while being rejected by A-D tests. Although it seems like an improvement on the Normal hypothesis, the Laplace Distribution remains far from a perfect fit for real world stock market daily returns.
{"title":"Laplace versus the Normal Distribution for Daily Stock Market Returns","authors":"Gustavo Harckbart","doi":"10.2139/ssrn.3479681","DOIUrl":"https://doi.org/10.2139/ssrn.3479681","url":null,"abstract":"Daily stock market return distributions seem to have tails that are much fatter than Normal Distribution models. This paper examines the possibility of the Laplace Distribution as a better alternative for modeling daily stock returns. Visual inspection of Q-Q plots seem to confirm the Laplace Distribution better fit to the data. The Laplace Distribution also managed to outperform the Normal Distribution in the K-S statistical tests, while being rejected by A-D tests. Although it seems like an improvement on the Normal hypothesis, the Laplace Distribution remains far from a perfect fit for real world stock market daily returns.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122564635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.5089/9781513519180.001
Michal Andrle, Miroslav Plašil
This paper assesses house prices in 11 Canadian Census Metropolitan Areas (CMA) using the borrowing-capacity and the net-present-value approaches. The results indicate that by the end of 2018, house prices in most metropolitan areas are aligned with macroeconomic fundamentals. However, in Hamilton, Toronto, and Vancouver house prices have increased beyond the values implied by the fundamentals.
{"title":"Assessing House Prices in Canada","authors":"Michal Andrle, Miroslav Plašil","doi":"10.5089/9781513519180.001","DOIUrl":"https://doi.org/10.5089/9781513519180.001","url":null,"abstract":"This paper assesses house prices in 11 Canadian Census Metropolitan Areas (CMA) using the borrowing-capacity and the net-present-value approaches. The results indicate that by the end of 2018, house prices in most metropolitan areas are aligned with macroeconomic fundamentals. However, in Hamilton, Toronto, and Vancouver house prices have increased beyond the values implied by the fundamentals.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract There is a general consensus emerging that the uncovered interest parity (UIP) does not hold or ex-post exchange rate change predicts the interest rate differential in the wrong direction. This paper provides a pioneer study to offer a risk premium based solution to the popular UIP failure using a dataset of 44 advanced and emerging currencies. We report that in the absence of risk premium, UIP failure is more prominent in emerging countries relative to advanced countries since only 34% of the total beta coefficients range between 0.5 and 1.5. Next, we include the risk premium in the main UIP equation using a component generalized autoregressive conditional heteroskedastic-in-mean (CGARCH-M) model and show that the results conform more to the UIP theory since 73% of the beta coefficients range between 0.5 and 1.5. Such a finding validates our argument that risk premium is the main factor responsible for UIP violation and including it in the main equation helps uncover the UIP puzzle, especially in the case of emerging countries. Overall, in the presence of risk premium, in most countries, ex-post exchange rate change bear a positive relationship with the interest rate differential as UIP predicts. This is our key contribution to the literature.
{"title":"Does Risk Premium Help Uncover the Uncovered Interest Parity Failure?","authors":"Satish Kumar","doi":"10.2139/ssrn.3754177","DOIUrl":"https://doi.org/10.2139/ssrn.3754177","url":null,"abstract":"Abstract There is a general consensus emerging that the uncovered interest parity (UIP) does not hold or ex-post exchange rate change predicts the interest rate differential in the wrong direction. This paper provides a pioneer study to offer a risk premium based solution to the popular UIP failure using a dataset of 44 advanced and emerging currencies. We report that in the absence of risk premium, UIP failure is more prominent in emerging countries relative to advanced countries since only 34% of the total beta coefficients range between 0.5 and 1.5. Next, we include the risk premium in the main UIP equation using a component generalized autoregressive conditional heteroskedastic-in-mean (CGARCH-M) model and show that the results conform more to the UIP theory since 73% of the beta coefficients range between 0.5 and 1.5. Such a finding validates our argument that risk premium is the main factor responsible for UIP violation and including it in the main equation helps uncover the UIP puzzle, especially in the case of emerging countries. Overall, in the presence of risk premium, in most countries, ex-post exchange rate change bear a positive relationship with the interest rate differential as UIP predicts. This is our key contribution to the literature.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129076213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent difficulties with certain factor models have increased interest in finding methods to “time” factor investing better. So far, however, the consensus is that factor timing is difficult. As inspiration for a possible solution, this paper reviews one of the best long-term return prediction models for the S&P 500 – the level of equity ownership in investor portfolios – which handily outperforms commonly-cited valuation-based forecast methods by relying on the more fundamental dynamics of supply and demand. Indeed, it has been called the “greatest predictor of future stock market returns” you’ve (probably) never heard of! For example, it can explain the earnings-less bull market of the 1980s, and overcomes the negative-PE problem of the 2008 Financial Crisis for which the traditional methods masked a good buying opportunity.
The first section of the paper recreates and compares the various long-term S&P 500 forecasting methods, using our own robust fitting procedures. This paper then suggests a roadmap for applying this methodology to factor forecasting, since it is already known that standard valuation (or other) methods are not good estimators of factor performance.
{"title":"Which Factors Are Over-Owned? Or, Supply and Demand: A Possible Roadmap to Solving the Factor Timing Problem","authors":"R. Stock","doi":"10.2139/ssrn.3516208","DOIUrl":"https://doi.org/10.2139/ssrn.3516208","url":null,"abstract":"Recent difficulties with certain factor models have increased interest in finding methods to “time” factor investing better. So far, however, the consensus is that factor timing is difficult. As inspiration for a possible solution, this paper reviews one of the best long-term return prediction models for the S&P 500 – the level of equity ownership in investor portfolios – which handily outperforms commonly-cited valuation-based forecast methods by relying on the more fundamental dynamics of supply and demand. Indeed, it has been called the “greatest predictor of future stock market returns” you’ve (probably) never heard of! For example, it can explain the earnings-less bull market of the 1980s, and overcomes the negative-PE problem of the 2008 Financial Crisis for which the traditional methods masked a good buying opportunity. <br><br>The first section of the paper recreates and compares the various long-term S&P 500 forecasting methods, using our own robust fitting procedures. This paper then suggests a roadmap for applying this methodology to factor forecasting, since it is already known that standard valuation (or other) methods are not good estimators of factor performance.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125700014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider a rates-credit hybrid model with the rates governed by a Hull-White short-rate model and the credit intensities by a Black-Karasinski short-rate model. We provide a systematic derivation of a pricing kernel (also known as an Arrow-Debreu formula) for European-style options and/or protection payments, using operator formalism combined with exponential expansion formulae. Our approach gives rise to an analytic expression involving an infinite series in powers of the credit spread (not of its lognormal volatility). We propose that this can be used to provide results to a chosen level of accuracy by truncating the power series at a suitable point and give explicit expressions for all terms up to second order, which level we suggest should in practice suffice. We apply our first-order result to calculate the impact of rates-credit correlation on the pricing of credit default swaps (CDS), extinguishing interest rate swaps, survival-contingent capped Libor flows and contingent CDS with interest rate swap underlying. Very simple analytic expressions are obtained in all cases. Highly favourable comparison is found between even the first order approximations and Monte Carlo simulations.
{"title":"Perturbation Expansion for Arrow-Debreu Pricing with Hull-White Interest Rates and Black-Karasinski Credit Intensity","authors":"C. Turfus","doi":"10.2139/ssrn.3287910","DOIUrl":"https://doi.org/10.2139/ssrn.3287910","url":null,"abstract":"We consider a rates-credit hybrid model with the rates governed by a Hull-White short-rate model and the credit intensities by a Black-Karasinski short-rate model. We provide a systematic derivation of a pricing kernel (also known as an Arrow-Debreu formula) for European-style options and/or protection payments, using operator formalism combined with exponential expansion formulae. Our approach gives rise to an analytic expression involving an infinite series in powers of the credit spread (not of its lognormal volatility). We propose that this can be used to provide results to a chosen level of accuracy by truncating the power series at a suitable point and give explicit expressions for all terms up to second order, which level we suggest should in practice suffice. We apply our first-order result to calculate the impact of rates-credit correlation on the pricing of credit default swaps (CDS), extinguishing interest rate swaps, survival-contingent capped Libor flows and contingent CDS with interest rate swap underlying. Very simple analytic expressions are obtained in all cases. Highly favourable comparison is found between even the first order approximations and Monte Carlo simulations.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124853100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose of the study: The study shows how the financial factor of Leverage affects the empirical model of asset pricing together with other financial factors, i.e. Size, Book to Market, Operating Profit, and Investment. The contribution of Leverage in asset pricing will be tested, and its effect will be shown in the excess return of the asset. Methodology: The methodology used in this paper is based on the Fama and French model of asset pricing with additional factors added in the model. Data processing follows the Fama-Mc Beth procedure. Data comes from the Indonesian Stock Market, which consists of more than 500 stocks for ten years period of observation. Main Findings: The financial factor of Leverage affects the empirical model of asset pricing together with, i.e. Size, Book to Market, Operating Profit, and Investment. All the financial factors in the model are stationary around their mean, or they are non-stationary due to unit-roots. All the independents' variables have P-Value less than 10%. Implications: This study will be useful for financial investors in building an effective portfolio stock investment. By applying this model to their portfolio investment, the investors could effectively manage their portfolio return. On the management side, managing their financing structure, e.g. Leverage is the objective of the firm to maximize returns of the firms. Novelty/Originality of this study: The empirical research with the involvement of the financial factor of Leverage has not been performed in Indonesia. The Leverage as the single factor of asset pricing has been considered as a significant financial factor for asset pricing, however, how the Leverage contributes to asset pricing compares to other financial factors has not examined yet.
{"title":"The Relationship of Financial Factors in Asset Pricing: The Case of Indonesian Market","authors":"Sinta Aryani, S. Wiryono, Deddy P. Koesrindartoto","doi":"10.18510/hssr.2019.7568","DOIUrl":"https://doi.org/10.18510/hssr.2019.7568","url":null,"abstract":"Purpose of the study: The study shows how the financial factor of Leverage affects the empirical model of asset pricing together with other financial factors, i.e. Size, Book to Market, Operating Profit, and Investment. The contribution of Leverage in asset pricing will be tested, and its effect will be shown in the excess return of the asset. Methodology: The methodology used in this paper is based on the Fama and French model of asset pricing with additional factors added in the model. Data processing follows the Fama-Mc Beth procedure. Data comes from the Indonesian Stock Market, which consists of more than 500 stocks for ten years period of observation. Main Findings: The financial factor of Leverage affects the empirical model of asset pricing together with, i.e. Size, Book to Market, Operating Profit, and Investment. All the financial factors in the model are stationary around their mean, or they are non-stationary due to unit-roots. All the independents' variables have P-Value less than 10%. Implications: This study will be useful for financial investors in building an effective portfolio stock investment. By applying this model to their portfolio investment, the investors could effectively manage their portfolio return. On the management side, managing their financing structure, e.g. Leverage is the objective of the firm to maximize returns of the firms. Novelty/Originality of this study: The empirical research with the involvement of the financial factor of Leverage has not been performed in Indonesia. The Leverage as the single factor of asset pricing has been considered as a significant financial factor for asset pricing, however, how the Leverage contributes to asset pricing compares to other financial factors has not examined yet.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We show that investment decisions of ETF market participants when trading country ETFs are mostly driven by shocks to U.S. fundamentals, rather than local risks. Investors react only to negative news about local economies. When U.S. economic uncertainty increases, investors switch to Cash ETFs. We demonstrate that ETF arbitrage mechanism is one of the key channels through which U.S. shocks propagate to local economies leading to increased return correlation with the U.S. market, limiting the benefits from international diversification. We find that countries with stronger ETF price discovery and lower limits to arbitrage have a higher comovement with the U.S. market.
{"title":"ETF Arbitrage and International Diversification","authors":"I. Filippou, A. Gozluklu, Hari Rozental","doi":"10.2139/ssrn.3287417","DOIUrl":"https://doi.org/10.2139/ssrn.3287417","url":null,"abstract":"We show that investment decisions of ETF market participants when trading country ETFs are mostly driven by shocks to U.S. fundamentals, rather than local risks. Investors react only to negative news about local economies. When U.S. economic uncertainty increases, investors switch to Cash ETFs. We demonstrate that ETF arbitrage mechanism is one of the key channels through which U.S. shocks propagate to local economies leading to increased return correlation with the U.S. market, limiting the benefits from international diversification. We find that countries with stronger ETF price discovery and lower limits to arbitrage have a higher comovement with the U.S. market.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114556306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We characterize time-varying disaster risk using interbank rates and their options. The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We make an identification assumption that macroeconomic disasters coincide with banking disasters -- extremely unlikely events in which the interbank market fails and investors suffer significant losses. Based on our flexible reduced-form setup, interbank rates together with their options allow us to extract the short-run and long-run components of disaster risk. Our estimation results serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments.
{"title":"What Interbank Rates Tell Us About Time-Varying Disaster Risk","authors":"Hitesh Doshi, Hyung-joo Kim, S. Seo","doi":"10.2139/ssrn.3469087","DOIUrl":"https://doi.org/10.2139/ssrn.3469087","url":null,"abstract":"We characterize time-varying disaster risk using interbank rates and their options. The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We make an identification assumption that macroeconomic disasters coincide with banking disasters -- extremely unlikely events in which the interbank market fails and investors suffer significant losses. Based on our flexible reduced-form setup, interbank rates together with their options allow us to extract the short-run and long-run components of disaster risk. Our estimation results serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we show that vector auto-regression (VAR) models, which are commonly used to estimate permanent price impact, are misspecified and can produce conflicting and incorrect inferences when the price impact function is nonlinear. We propose an alternative method to estimate permanent price impact by modifying a reinforcement learning (RL) framework. Our approach assumes the data is stationary and Markov, but is otherwise unrestrictive. We obtain empirical estimates for our model using an iterative learning rule and demonstrate that our model captures nonlinearities and makes correct inferences.
{"title":"Estimating Permanent Price Impact via Machine Learning","authors":"R. Philip","doi":"10.2139/ssrn.3488840","DOIUrl":"https://doi.org/10.2139/ssrn.3488840","url":null,"abstract":"In this paper, we show that vector auto-regression (VAR) models, which are commonly used to estimate permanent price impact, are misspecified and can produce conflicting and incorrect inferences when the price impact function is nonlinear. We propose an alternative method to estimate permanent price impact by modifying a reinforcement learning (RL) framework. Our approach assumes the data is stationary and Markov, but is otherwise unrestrictive. We obtain empirical estimates for our model using an iterative learning rule and demonstrate that our model captures nonlinearities and makes correct inferences.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126242385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How does an asset with a higher expected return but higher risk compare to an asset with a lower expected return but lower risk? A natural answer is to rank them based on their risk-adjusted returns. But what if the expected return and risk are not estimated reliably? This is the challenge of investing in private markets and comparing their performance with public markets. We offer a framework to fairly compare private and public investment performance.
We present a methodology to reliably estimate the expected return and risk of private assets using the notion of a self-contained, self-financed portfolio. Our estimates are intuitive as they are based on terminal wealth outcomes (rather than a time-series analysis) resulting from investing in private markets. Using these estimates, we compare a variety of investments including PE funds, private debt, public equity and bond indices. Our comparison also accounts for the limits and cost of leverage, when applicable.
We find that terminal wealth-based means and volatilities of private investment returns are significantly different from those computed using traditional time-series return observations. We show that the ranks of various investments based on levered returns (with interest, fees, expenses and manager alpha) can be potentially different from those based on unlevered returns. Importantly, levered returns in mezzanine investments are competitive with buyout investments and that investment in long public market Baa-corporate bonds are, when levered to match risk, competitive with private market investments.
{"title":"A Fair Comparison Framework: Risk and Reward in Private & Public Investments","authors":"V. Jeet","doi":"10.2139/ssrn.3728660","DOIUrl":"https://doi.org/10.2139/ssrn.3728660","url":null,"abstract":"How does an asset with a higher expected return but higher risk compare to an asset with a lower expected return but lower risk? A natural answer is to rank them based on their risk-adjusted returns. But what if the expected return and risk are not estimated reliably? This is the challenge of investing in private markets and comparing their performance with public markets. We offer a framework to fairly compare private and public investment performance.<br><br>We present a methodology to reliably estimate the expected return and risk of private assets using the notion of a self-contained, self-financed portfolio. Our estimates are intuitive as they are based on terminal wealth outcomes (rather than a time-series analysis) resulting from investing in private markets. Using these estimates, we compare a variety of investments including PE funds, private debt, public equity and bond indices. Our comparison also accounts for the limits and cost of leverage, when applicable.<br><br>We find that terminal wealth-based means and volatilities of private investment returns are significantly different from those computed using traditional time-series return observations. We show that the ranks of various investments based on levered returns (with interest, fees, expenses and manager alpha) can be potentially different from those based on unlevered returns. Importantly, levered returns in mezzanine investments are competitive with buyout investments and that investment in long public market Baa-corporate bonds are, when levered to match risk, competitive with private market investments.","PeriodicalId":209192,"journal":{"name":"ERN: Asset Pricing Models (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132231783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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