This study provides evidence of periodically collapsing bubbles in the British pound to US dollar exchange rate in the post-1973 period. We develop two- and three-state regime-switching models that relate the expected exchange rate return to the bubble size and to an additional explanatory variable. Specifically, we consider six alternative explanatory variables that have been proposed in the literature as early warning indicators of a currency crisis. Our findings suggest that the regime-switching models are, in general, more accurate than the Random Walk model in terms of both statistical and especially economic evaluation criteria for exchange rate forecasts. Our three-state regime-switching model outperforms the two-state models and among the variables considered in our analysis, the short-term interest rate is the optimal variable, closely followed by imports, in both statistical and economic evaluation terms. Results are more promising for one-month predictions and are qualitatively robust to the calculated bubble measure.
{"title":"The Forecasting Performance of Regime-Switching Models of Speculative Behavior for Exchange Rates","authors":"E. Panopoulou, Theologos Pantelidis","doi":"10.2139/ssrn.2063184","DOIUrl":"https://doi.org/10.2139/ssrn.2063184","url":null,"abstract":"This study provides evidence of periodically collapsing bubbles in the British pound to US dollar exchange rate in the post-1973 period. We develop two- and three-state regime-switching models that relate the expected exchange rate return to the bubble size and to an additional explanatory variable. Specifically, we consider six alternative explanatory variables that have been proposed in the literature as early warning indicators of a currency crisis. Our findings suggest that the regime-switching models are, in general, more accurate than the Random Walk model in terms of both statistical and especially economic evaluation criteria for exchange rate forecasts. Our three-state regime-switching model outperforms the two-state models and among the variables considered in our analysis, the short-term interest rate is the optimal variable, closely followed by imports, in both statistical and economic evaluation terms. Results are more promising for one-month predictions and are qualitatively robust to the calculated bubble measure.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116856958","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}
Microfinance programs can improve the economic, social, and spiritual conditions of the poor. Survey responses from more than 29,000 microfinance clients are used here to study what impact the loan officer may have on advancement of this so called triple bottom line. The study seeks to identify what, if any, improvement in the economic, social, and spiritual conditions of the borrowers depends on the staff member administering the loan. While a general improvement in all three conditions over the course of the program is clear in the data, no conclusive evidence is found that this is due to the loan officer.
{"title":"Leading the Clients: Assessing the Importance of the Loan Officer in a Microfinance Program","authors":"P. Crabb","doi":"10.2139/SSRN.2087689","DOIUrl":"https://doi.org/10.2139/SSRN.2087689","url":null,"abstract":"Microfinance programs can improve the economic, social, and spiritual conditions of the poor. Survey responses from more than 29,000 microfinance clients are used here to study what impact the loan officer may have on advancement of this so called triple bottom line. The study seeks to identify what, if any, improvement in the economic, social, and spiritual conditions of the borrowers depends on the staff member administering the loan. While a general improvement in all three conditions over the course of the program is clear in the data, no conclusive evidence is found that this is due to the loan officer.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129905792","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 research note, we price Bermudan structured derivatives including the consequences of default, collateral margining, funding and investment costs. We use LSA Monte Carlo method for finding MTM for collateral margining along all simulation points. We also find 'default MTM' using LSA which helps us calculate cash flows in case of default. Finally we do a third sweep of LS Monte Carlo to calculate 'final MTM' in which we find the price of the derivative while simultaneously calculating funding/investment costs as they themselves depend upon future value of 'final MTM'. All three sets of LSA Monte carlo take just 5-10 seconds for 50K-100K paths and most of the cost associated with computations is in simulation of OIS rates, and Stochastic basis and calculation of basis functions used later for regressions. We also model correlations between default intensities and between default intensities and OIS/Basis rates with an intention to model wrong/Right way risk to some degree. We also give formulas for calculation of survival probabilities/Default Discount Rates in our setting. We also model credit migrations by mapping default probability bands from rating agencies on default intensities.
{"title":"Pricing Bermudan Callable Derivatives with Default, Collateral Margining, Funding and Investment Costs","authors":"A. Amin","doi":"10.2139/ssrn.2060804","DOIUrl":"https://doi.org/10.2139/ssrn.2060804","url":null,"abstract":"In this research note, we price Bermudan structured derivatives including the consequences of default, collateral margining, funding and investment costs. We use LSA Monte Carlo method for finding MTM for collateral margining along all simulation points. We also find 'default MTM' using LSA which helps us calculate cash flows in case of default. Finally we do a third sweep of LS Monte Carlo to calculate 'final MTM' in which we find the price of the derivative while simultaneously calculating funding/investment costs as they themselves depend upon future value of 'final MTM'. All three sets of LSA Monte carlo take just 5-10 seconds for 50K-100K paths and most of the cost associated with computations is in simulation of OIS rates, and Stochastic basis and calculation of basis functions used later for regressions. We also model correlations between default intensities and between default intensities and OIS/Basis rates with an intention to model wrong/Right way risk to some degree. We also give formulas for calculation of survival probabilities/Default Discount Rates in our setting. We also model credit migrations by mapping default probability bands from rating agencies on default intensities.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122830399","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}
Calculating news sentiment indexes at the company level in real time offer a better understanding of the role of sentiment in asset pricing. This study shows how to construct these indexes, and how to create signals that can form the basis of a news-based short-term stock selection model. Overall, we find that news sentiment holds strong predictive power and delivers high risk-adjusted performance.
{"title":"Short-Term Stock Selection Using News Based Indicators","authors":"Peter Hafez, Junqiang Xie","doi":"10.2139/ssrn.2155679","DOIUrl":"https://doi.org/10.2139/ssrn.2155679","url":null,"abstract":"Calculating news sentiment indexes at the company level in real time offer a better understanding of the role of sentiment in asset pricing. This study shows how to construct these indexes, and how to create signals that can form the basis of a news-based short-term stock selection model. Overall, we find that news sentiment holds strong predictive power and delivers high risk-adjusted performance.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127860532","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 introduce a market impact model for stochastic linear transient impact, extending the model of Gatheral (2010) with the possibility of randomly fluctuating liquidity. We discuss regularity conditions for market impact models, i.e. properties of optimal liquidation strategies in these models. By many examples, we illustrate how regularity might fail and what consequences arise. In particular, there can be arbitrage opportunities although the unaffected price process is a martingale. For our stochastic market impact model, we give a necessary condition, and for exponentially decaying impact a sufficient condition for the regularity of the model. In a numerical example we show that regularity can strongly depend on the liquidation time horizon. Furthermore, we show that even if the liquidity parameter is a martingale, deterministic strategies can be suboptimal.
{"title":"Regularity of Market Impact Models with Stochastic Price Impact","authors":"Florian Klöck","doi":"10.2139/ssrn.2057610","DOIUrl":"https://doi.org/10.2139/ssrn.2057610","url":null,"abstract":"We introduce a market impact model for stochastic linear transient impact, extending the model of Gatheral (2010) with the possibility of randomly fluctuating liquidity. We discuss regularity conditions for market impact models, i.e. properties of optimal liquidation strategies in these models. By many examples, we illustrate how regularity might fail and what consequences arise. In particular, there can be arbitrage opportunities although the unaffected price process is a martingale. For our stochastic market impact model, we give a necessary condition, and for exponentially decaying impact a sufficient condition for the regularity of the model. In a numerical example we show that regularity can strongly depend on the liquidation time horizon. Furthermore, we show that even if the liquidity parameter is a martingale, deterministic strategies can be suboptimal.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133846931","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 estimate stochastic volatility leverage models for a panel of stock returns for 24 S&P 500 firms from six industries. News are measured as differences between daily return and a monthly moving average of past returns. We estimate the models by maximum likelihood using an Efficient Importance Sampling method which produces numerically highly accurate estimates of the likelihood and related test-statistics. We find significant leverage effects for all 24 stocks. These effects are fairly consistent within each industry but there are significant differences across two groups of industries. Our models produce significant improvement in volatility predictability.
{"title":"A Stochastic Volatility and Leverage: Application to a Panel of S&P Stocks","authors":"S. Ozturk, J. Richard","doi":"10.2139/ssrn.2052034","DOIUrl":"https://doi.org/10.2139/ssrn.2052034","url":null,"abstract":"We estimate stochastic volatility leverage models for a panel of stock returns for 24 S&P 500 firms from six industries. News are measured as differences between daily return and a monthly moving average of past returns. We estimate the models by maximum likelihood using an Efficient Importance Sampling method which produces numerically highly accurate estimates of the likelihood and related test-statistics. We find significant leverage effects for all 24 stocks. These effects are fairly consistent within each industry but there are significant differences across two groups of industries. Our models produce significant improvement in volatility predictability.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133184451","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}
This paper studies oil market and other macroeconomic shocks in a structural vector autoregression with sign restrictions. It introduces a new indicator for oil demand, and uniquely, performs a sign restriction set-up with a penalty function approach in an oil market vector autoregression. The model also allows for macroeconomic shocks in the US. The results underline the importance of the source of an oil shock for its macroeconomic consequences. Oil supply shocks have been less relevant in driving real oil prices, and had less of an effect on US inflation than demand shocks. Overall, the effects of oil shocks on US real activity have been relatively limited, as also highlighted by a counterfactual experiment of recent oil market developments. JEL Classification: C01, C32, E32
{"title":"Macroeconomic Shocks in an Oil Market VAR","authors":"Marko Melolinna","doi":"10.2139/ssrn.2050326","DOIUrl":"https://doi.org/10.2139/ssrn.2050326","url":null,"abstract":"This paper studies oil market and other macroeconomic shocks in a structural vector autoregression with sign restrictions. It introduces a new indicator for oil demand, and uniquely, performs a sign restriction set-up with a penalty function approach in an oil market vector autoregression. The model also allows for macroeconomic shocks in the US. The results underline the importance of the source of an oil shock for its macroeconomic consequences. Oil supply shocks have been less relevant in driving real oil prices, and had less of an effect on US inflation than demand shocks. Overall, the effects of oil shocks on US real activity have been relatively limited, as also highlighted by a counterfactual experiment of recent oil market developments. JEL Classification: C01, C32, E32","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127095253","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 many cases, a company’s capital investment decision is not a one-off “yes/no”, but occurs as a result of a sequence of decisions of a more preliminary nature. Major resource investment projects, for example, typically have to pass several “feasibility” tests before companies fully commit to them. Accordingly, there is an “investment pipeline” of projects. In this study, we examine the stock-market reaction to announcements of the progress of investment projects as they flow down the pipeline. Using a sample of Australian stocks in the resources sector, we find substantial positive abnormal returns when firms announce a change in the status of their planned projects. Interestingly, the magnitude of reaction varies substantially with the location of the project in the pipeline (such as the project being “committed”, “under construction” and “completed”). These results reveal the value-enhancing effects of the market being informed of projects in the later stages of the investment pipeline. Further analysis shows that larger stock-market reactions tend to be associated with bigger projects, smaller firms and those with lower free cash flow.
{"title":"How Much are Resource Projects Worth? A Capital Market Perspective","authors":"Liangfu Li","doi":"10.2139/ssrn.2132559","DOIUrl":"https://doi.org/10.2139/ssrn.2132559","url":null,"abstract":"In many cases, a company’s capital investment decision is not a one-off “yes/no”, but occurs as a result of a sequence of decisions of a more preliminary nature. Major resource investment projects, for example, typically have to pass several “feasibility” tests before companies fully commit to them. Accordingly, there is an “investment pipeline” of projects. In this study, we examine the stock-market reaction to announcements of the progress of investment projects as they flow down the pipeline. Using a sample of Australian stocks in the resources sector, we find substantial positive abnormal returns when firms announce a change in the status of their planned projects. Interestingly, the magnitude of reaction varies substantially with the location of the project in the pipeline (such as the project being “committed”, “under construction” and “completed”). These results reveal the value-enhancing effects of the market being informed of projects in the later stages of the investment pipeline. Further analysis shows that larger stock-market reactions tend to be associated with bigger projects, smaller firms and those with lower free cash flow.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128016589","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}
This paper addresses the question whether the net asset value (NAV) return of listed private equity is similar to the NAV return of unlisted private equity funds. I use NAV indices from LPX and NAV data from Preqin. I find a high correlation between the NAV of listed and unlisted private equity. A cointegration analysis shows that the NAV of listed and unlisted private equity are cointegrated. I also find that the NAV returns of unlisted private equity funds can be explained by the NAV returns of listed private equity. Volatility of LPX NAV indices is substantially lower than volatility of market price based total return (TR) indices.
{"title":"Listed vs. Unlisted Private Equity (First Version)","authors":"Michel Degosciu","doi":"10.2139/ssrn.2081997","DOIUrl":"https://doi.org/10.2139/ssrn.2081997","url":null,"abstract":"This paper addresses the question whether the net asset value (NAV) return of listed private equity is similar to the NAV return of unlisted private equity funds. I use NAV indices from LPX and NAV data from Preqin. I find a high correlation between the NAV of listed and unlisted private equity. A cointegration analysis shows that the NAV of listed and unlisted private equity are cointegrated. I also find that the NAV returns of unlisted private equity funds can be explained by the NAV returns of listed private equity. Volatility of LPX NAV indices is substantially lower than volatility of market price based total return (TR) indices.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129845682","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}
Why do countries differ so much in terms of their financial systems? Are banks and equity competing or complementary sources of financing for firms? To address these questions, I study various determinants of capital market development and whether these determinants favor one form of capital (equity capital) over the other (private bank credit) during the process of financial development. I find that capital market development is primarily demand driven. The demand for finance affects the equity market development disproportionately more (in a non-linear way) than it does the private bank credit market. Furthermore, the relative strength of enforcement of creditors' and minority shareholders' rights also significantly determines whether external financing is predominantly raised through private arrangement of credit or through the public equity market. Through the power of demand and enforcement, we can explain the prominence of the market-based financial system in recent years. I find that the signs and significance of various political and legal endowments in affecting financial development crucially depend on the conditioning information set of the regression, and thus may not be very informative in understanding the co-development of various segments of the capital market. Despite the prominence of the market-based system in recent years, I show that no financial system is inherently superior to the other, rather differences in the underlying determinants of capital market development shape which form of capital is going to be the dominant one in a financial system.
{"title":"Does Equity Market Stifle Private Credit Market?","authors":"Mohammad M. Rahaman","doi":"10.2139/ssrn.2046342","DOIUrl":"https://doi.org/10.2139/ssrn.2046342","url":null,"abstract":"Why do countries differ so much in terms of their financial systems? Are banks and equity competing or complementary sources of financing for firms? To address these questions, I study various determinants of capital market development and whether these determinants favor one form of capital (equity capital) over the other (private bank credit) during the process of financial development. I find that capital market development is primarily demand driven. The demand for finance affects the equity market development disproportionately more (in a non-linear way) than it does the private bank credit market. Furthermore, the relative strength of enforcement of creditors' and minority shareholders' rights also significantly determines whether external financing is predominantly raised through private arrangement of credit or through the public equity market. Through the power of demand and enforcement, we can explain the prominence of the market-based financial system in recent years. I find that the signs and significance of various political and legal endowments in affecting financial development crucially depend on the conditioning information set of the regression, and thus may not be very informative in understanding the co-development of various segments of the capital market. Despite the prominence of the market-based system in recent years, I show that no financial system is inherently superior to the other, rather differences in the underlying determinants of capital market development shape which form of capital is going to be the dominant one in a financial system.","PeriodicalId":214104,"journal":{"name":"Econometrics: Applied Econometric Modeling in Financial Economics - Econometrics of Financial Markets eJournal","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715438","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：