Empirical Finance

We forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-theparts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically signicant improvements over historical mean forecasts, with monthly outof-sample R 2 of 3.27 % and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns.

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a "horserace" comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982-2008. Interestingly, when our entire sample of data is used to estimate competing models, the "best" performer in terms of distributional "fit" as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (CHEN: 1996) model examined by Andersen, . Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the "best" continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the "Stable 1990s" period that we examine. Moreover, an analogous result holds for the "Post 1990s" period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the "best" continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall "winning" model, regardless of sample period.

We review and construct consistent in-sample specification and out-of-sample model selection tests on conditional distributions and predictive densities associated with continuous multifactor (possibly with jumps) and (non)linear discrete models of the short term interest rate. The results of our empirical analysis are used to carry out a "horserace" comparing discrete and continuous models across multiple sample periods, forecast horizons, and evaluation intervals. Our evaluation involves comparing models during two distinct historical periods, as well as across our entire weekly sample of Eurodollar deposit rates from 1982-2008. Interestingly, when our entire sample of data is used to estimate competing models, the "best" performer in terms of distributional "fit" as well as predictive density accuracy, both in-sample and out-of-sample, is the three factor Chen (CHEN: 1996) model examined by Andersen, . Just as interestingly, a logistic type discrete smooth transition autoregression (STAR) model is preferred to the "best" continuous model (i.e. the one factor Cox, Ingersoll, and Ross (CIR: 1985) model) when comparing predictive accuracy for the "Stable 1990s" period that we examine. Moreover, an analogous result holds for the "Post 1990s" period that we examine, where the STAR model is preferred to a two factor stochastic mean model. Thus, when the STAR model is parameterized using only data corresponding to a particular sub-sample, it outperforms the "best" continuous alternative during that period. However, when models are estimated using the entire dataset, the continuous CHEN model is preferred, regardless of the variety of model specification (selection) test that is carried out. Given that it is very difficult to ascertain the particular future regime that will ensue when constructing ex ante predictions, thus, the CHEN model is our overall "winning" model, regardless of sample period.

This paper investigates seasonal and cyclical patterns in Nigeria's equity market using the NGX All-Share Index (ASI) from 2012 to 2025. It used descriptive statistics to examine day-of-week, monthly, quarterly, and election-year patterns, alongside changes in volumes and downside risk captured through drawdowns. The results indicate that timing effects exist but are narrower than textbook anomalies: Wednesdays and late-year (Q4/December) periods consistently outperform, while Fridays and early Q1 (February-March) underperform. Contrary to conventional expectations, the "January effect" and "Santa Rally" are not robust in the NGX; instead, December and May emerge as the strongest months. Election cycles exert a powerful influence, with 2015 and 2019 posting steep annual losses (-12% to-17%), while 2023 broke the pattern with a +40% gain amid reforms and liquidity inflows. Drawdowns remain a structural feature, often reaching-40% to-55%, with an average recovery time of about three months (87 days). Volume patterns confirm liquidity clustering midweek and in select months, highlighting its role as a confirmation rather than predictive signal. This paper conclude that investors must pair seasonal insights with event awareness and disciplined drawdown management, as timing advantages alone are insufficient in Nigeria's high-volatility frontier market.

This paper investigates the hidden relations between the Chinese cross-listed firms in the Shanghai and Hong Kong stock exchanges. In China, stocks are classified into four categories: A shares, B shares, H shares and ADR shares. A and B shares are traded on China's two stock exchanges, namely Shanghai and Shenzhen Stock Exchanges while H and ADR shares are traded in Hong Kong and the USA respectively. A shareholders consist of mainly domestic investors with a fraction of foreign investors; B shareholders are however, a mixture of Chinese and international shareholders; H and ADR shares are targeted only towards international investors. A firm can only choose to cross-list A and B shares or A and H shares. 21 companies that have cross-listed A and H shares and 44 companies that have cross-listed A and B shares during the timeframe between July, 2001 and June, 2006 have been studied. We have retrieved closing prices on the 20th of every month and using the general mathematical approach involving log-returns, the expected monthly returns, variances and covariances have been estimated. Our results for A and B cross-listed firms show that the monthly returns are in the same and have reasonably high correlations. The results for A and H cross-listed firms however, show that the expected returns of the H shares are of higher magnitude than the A shares and that they show significantly lower correlations. The reason behind these results seems to be a consequence of the deviant behaviour of the B share market in comparison to the international market, together with the hypothesis that B shares lead A shares.

Globalization is one of the important issues that have changed investment environment. The development of a country's economy cannot be separated from the country's stock index movement, because the movement of stock indices followed the optimism and pessimism of stock investors. The purpose of this study was to examine the influence of the NYSE index, NIKKEI, FTSE and SSE to JCI. This study uses a correlational approach with data analysis techniques with multiple regression analysis and assisted by using SPSS for Windows version 20. The population in this study is a report on global stock indices, so that the sample used is Composite Stock Price Index (CSPI) in the period January 2011 to by March 2014 when the closing price from January 2001 to March 2014 so that there are 160 data. Results of statistical calculations found that (1) There is a positive influence on the NYSE Composite Index; (2) There is a positive influence NIKKEI against JCI; (3) There is a positive influe...

We examine the effect of qualified audit opinions on private debt contracts. Consistent with the monitoring role of audit opinion on accounting quality, we find that a qualified audit opinion is associated with an average increase of 18 basis points in the interest rate of loan facilities issued in the year following a QAO. We find that this effect persists for at least three years with an average increase of 8 basis points in the interest rate for these loans. Furthermore, we find evidence that lenders replace financial covenants with non-financial covenants following a qualified audit opinion of the borrower's financial statements. We also find that qualified audit opinions are associated with decreases in loan size and increases in the requirement of collateral from the borrower, but we find no evidence that qualified audit opinions are associated with change in the length of loan maturity. Finally, we find that qualified audit opinions are associated with decreased use of performance pricing provisions in debt contracts. A variety of additional tests demonstrate that the effects of a qualified audit opinion on contractual terms are robust after controlling for other indicators of accounting quality such as abnormal accruals, volatility of accounting accruals and disclosure of internal control weakness. These results are not obvious given that private lenders have access to private information unavailable to most market participants and suggest that auditors play a unique role in debt contracting through monitoring borrowers' accounting quality.

Spectral analysis and ARMA models have been the most established weapons of choice for the detection of cycles in time series data. However, such techniques are only appropriate when periodic components are time invariant. This has led some scholars to disregard the possibility of irregular cycles. We aim to contribute to further reconsideration of the possibility of cycles in political phenomena by introducing wavelet analysis. Its main contribution comes from the ability to estimate the spectral characteristics of a time series as a function of time, thus revealing how its different periodic components change over time, while preserving rigorous standards of statistical inference. We demonstrate the usefulness of wavelet analysis with two applications. The first concerns the possibility of long-cycles in wars, one of the central puzzles in the international relations sub-field. The second concerns election returns in presidential and congressional elections in the United States, where the existence of cyclical patterns has been equally controversial. Contrary to the prevailing wisdom, wavelet analysis allows the detection of transient and coexisting cycles, shedding some light over phenomena that have remained unaddressed so far. * We thank Bernard Cazelles for providing us the code used in and Joshua Goldstein for the data used in . We also thank Paul Beck and Nathan Kelly for valuable comments and suggestions. The usual disclaimer applies.

We propose a new method for stationary nonlinear time series analysis which dynamically combines models, either parametric or nonparametric, by using mixture probabilities from so-called variable length Markov chains. The approach is very general and flexible: it can be used for modelling conditional means, conditional variances or conditional densities given the previous lagged values, and the methodology can be applied to dynamically combine almost any kind of models. Parameter estimation (finite or infinite-dimensional) and model selection can be done in a fully data-driven way. We demonstrate the predictive power of the method on finite sample data and an asymptotic consistency result is presented.

In this paper we describe a nonparametric GARCH model of first order and propose a simple iterative algorithm for its estimation from data. We provide a theoretical justification for this algorithm and give examples of its application to stationary time series data showing stochastic volatility. We observe that our nonparametric procedure often gives better estimates of the unobserved latent volatility process than parametric GARCH(1,1) modelling, particularly when asymmetries are present in the data. We show how the basic iterative idea may be extended to more complex time series models combining ARMA or GARCH features of possibly higher order.

We propose a new method for stationary nonlinear time series analysis which dynamically combines models, either parametric or nonparametric, by using mixture probabilities from so-called variable length Markov chains. The approach is very general and flexible: it can be used for modelling conditional means, conditional variances or conditional densities given the previous lagged values, and the methodology can be applied to dynamically combine almost any kind of models. Parameter estimation (finite or infinite-dimensional) and model selection can be done in a fully data-driven way. We demonstrate the predictive power of the method on finite sample data and an asymptotic consistency result is presented.

Background: The MC4R gene harbours one of the strongest susceptibility locus for obesity, and its metabolic consequences. The objective of this study was to analyze whether dietary factors may attenuate the MC4R genotypes effects on obesity and its comorbidities.Methods: In 819 adult participants, genotyped for MC4R SNPs (rs17782313, rs12970134, rs633265, rs1350341) the anthropometric measurements, total body fat, visceral (VAT) and subcutaneous adipose tissue (SAT), blood glucose, insulin, total-, LDL-, HDL-cholesterol, and triglycerides concentrations were assessed. The daily macronutrient intake was calculated based on the three-day food intake records, and daily physical activity based on the validated questionnaire. The ANOVA or Kruskal-Wallis tests were used, and multivariate linear regression models were developed to evaluate the effects of diet on obesity and related factors in various genotypes carriers.Results: The CC genotype (rs17782313) carriers, being in the upper quan...

Gaining a better understanding of the behavior of international investors is key for informing the debate about the optimal response to capital flows and about reforms to the international financial architecture. In this context, recent research on the behavior of international mutual funds at the micro level has expanded our knowledge about the drivers of portfolio flows and the mechanisms behind the transmission of financial shocks across countries. This paper provides a brief survey of this literature, with a focus on the empirical evidence for emerging markets. Overall, the behavior of international mutual funds is complex and overly simplistic characterizations are misleading. However, there is broad-based evidence for momentum trading among funds. Moreover, funds tend to avoid opaque markets and assets, and this behavior becomes more pronounced during volatile times. Portfolio rebalancing mechanisms are clearly important in explaining contagion patterns, even in the absence of common macroeconomic fundamentals. From a surveillance point of view, this implies that monitoring the exposures of large investors at a micro level is crucial to assess vulnerabilities.

This is the first study to empirically examine post-recommendation buy and hold abnormal returns in emerging markets. By analyzing a sample of 13 emerging countries over the decade from 1996 to 2005, we find that stock prices react strongly to stock analyst recommendations and revisions. We also find that there is a stronger positive bias in analyst recommendations and revisions in emerging markets compared with that in developed markets. In our cross-sectional analysis, we find that the Market-to-Book ratio is the primary indicator for Buy and Strong Buy recommendation regressions. This indicates that stock analysts in emerging markets prefer high growth stocks with attractive characteristics.

A number of different cues allow listeners to perceive musical meter. Three experiments examined effects of melodic and temporal accents on perceived meter in excerpts from folksongs, scored in 6/8 or 3/4 meter. Participants matched excerpts with one of two metrical drum accompaniments. Melodic accents included contour change, melodic leaps, extreme register, melodic repetition, and harmonic rhythm. Two experiments with isochronous melodies showed that contour change and repetition predicted judgments. For longer melodies in the second experiment, variables predicted judgments best at the beginning of excerpts. The final experiment with rhythmically varied melodies showed that temporal accents, tempo, and contour change were the strongest predictors of meter. Our findings suggest that listeners combine multiple melodic and temporal features to perceive musical meter.

This study explores the potential of utilising certain prosodic qualities of function-specific vocal expressions in order to design effective non-speech user interface sounds. In an empirical setting, utterances with four context-situated communicative functions were produced by 20 participants. Time series of fundamental frequency (F0) and intensity were extracted from the utterances and analysed statistically. The results show that individual communicative functions have distinct prosodic characteristics that can be statistically ...

Melodic expectations are important in creating patterns of tension and relaxation. The way expectations are formed has been modeled with tonal hierarchies (Krumhansl & Kessler, 1982) and realization of implied intervals (Narmour, 1990). These models have been extensively investigated by asking listeners to rate different possible melodic continuations. However, these models and experimental methods utilize a limited musical context and probe the melody at only one point in time. To clarify the role of a ...

We study time-varying price leadership between international stock markets using a Markov switching causality model. We demonstrate variations in the causality pattern over time, with the US being the dominant country in causing other markets. We examine the factors which determine a country's role in the causal relationship. For country-specific factors, we show that trades openness increases price leadership. We also find that the lead-lag relationship between the stock markets is weaker during crisis periods, confirming the "wake-up call" hypothesis, with markets and investors focusing substantially more on idiosyncratic, country-specific characteristics during the crisis.

This study examines the efficiency of Iranian capital market after the imposition of economic sanctions, currency crisis and Covid 19 pandemic. Some events after 2008 have fundamentally changed the Iranian capital market environment and we expect these reforms to change the level of market efficiency. This research investigates the daily returns behavior in Tehran Stock Exchange (TSE) utilizing autocorrelation, augmented Dickey-Fuller, and runs tests over the period 2008-2022. The statistical significance of the data was assessed using both parametric and nonparametric test statistics. The results confirm that the Tehran Stock Exchange index has been affected by the Covid-19 pandemic crisis. It was also further revealed that economic and banking sanctions have had a direct impact on the Iranian economy and the Tehran Stock Exchange market, such that after the approval of each of the sanctions, significant structural failures were observed in the trend of the Tehran Stock Exchange index. And also the results indicate that all tests don't support that TSE daily returns follow random walk. Therefore, we conclude that in the Iranian capital market, abnormal returns can still be obtained by using technical and fundamental analysis. The study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis. This paper is the first to examine the efficiency of the capital market at a weak form, during the period of the Covid-19 pandemic and the imposition of international sanctions against the Iranian economy.

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model's performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model's efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty.

Individuals typically produce auditory sequences, such as speech or music, at a consistent spontaneous rate or tempo. We addressed whether spontaneous rates would show patterns of convergence across the domains of music and language production when the same participants spoke sentences and performed melodic phrases on a piano. Although timing plays a critical role in both domains, different communicative and motor constraints apply in each case and so it is not clear whether music and speech would display similar timing mechanisms. We report the results of two experiments in which adult participants produced sequences from memory at a comfortable spontaneous (uncued) rate. In Experiment 1, monolingual pianists in Buffalo, New York engaged in three production tasks: speaking sentences from memory, performing short melodies from memory, and tapping isochronously. In Experiment 2, English-French bilingual pianists in Montréal, Canada produced melodies on a piano as in Experiment 1, and...

The aim of this paper is to outline the typical characteristics of the ultra-highfrequency financial data and to present estimation methods of intraday seasonality of trading activity. Ultra-high-frequency financial data (transactions data or tick-by-tick data) is defined to be a full record of transactions and their associated characteristics. We consider two nonparametric estimation methods: cubic splines and a Nadaraya-Watson kernel estimator of regression. Both approaches are compared empirically and applied to financial data of stocks traded at the Warsaw Stock Exchange.

Regulators impose price limits on daily price movements to protect investors from excessive volatility, but several empirical studies have cast serious doubt on the benefits of such mechanisms. Using a large cross-sectional sample combined with intraday data from the Tokyo Stock Exchange, this study finds evidence that partially supports conventional criticisms that price limits spread out volatility, delay price discovery, and interrupt trading activities. More importantly, the transaction data analysis reveals that price limits help to reduce order imbalance and improve information asymmetry, justifying the existence of price limits on the Tokyo Stock Exchange.

The views expressed in this Working Paper arc those of the author(s) and do not necessarily represent those of the IMP or IMP policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. This paper assesses the extent to which crashes in emerging market currencies are predictable using simple logit models based on lagged macroeconomic and financial data. To evaluate our model, we calculate trading strategies in which an investor goes long or short in the currency depending on whether crash probabilities are low or high. When we estimate the model on part of the data and then use the parameter estimates to generate predictions for the remainder of the sample, we find that substantial profits may be made. Furthermore, the model correctly forecasts major crashes even on an out-of-sample basis.

We investigate whether momentum or reversal is the dominant phenomenon in short horizon (one-to four-week) foreign exchange rate returns. We find, based on a broad sample of 63 emerging and developed market currencies, evidence of momentum rather than reversal. Momentum strategy returns are as large as 8% p.a. The short-term momentum effect appears to be robust. Returns are larger in the earlier sub-period but still exist in the more recent period. The strategies are also profitable when the USD is appreciating or depreciating but they perform better in business cycle expansions.

This study carried out an empirical test of stochastic dominance application on portfolio selection in the Nigerian stock market. December daily stock price of ten (10) listed insurance firms in the period 2014 to 2020 were selected and tested for stochastic dominance occurrence. The findings indicate that the selection of firm stock followed the Markowitz mean-variance and risk preference behavior of investors in the stock market. It also shows that two (2) firm stocks were first order stochastically dominant (FSD), four (4) stocks of firms were second order stochastically dominant while nine (9) stocks were third order stochastically dominant (TSD) in the period after the stock market meltdown in Nigeria. The study recommends that future researchers should empirically investigate portfolio dominance on a sector by sector basis. This will guide potential investors at selecting securities on the basis of mean-variance and utility function.

Experimental studies in behavioral finance historically have confirmed that subjects are highly influenced by reference points when making economic decisions. A recent study by analyzed the dynamics of reference price formation using experimental methods with subjects forming reference prices for stocks based on observed price sequences. Their study helps to clarify behavioral effects of past prices on reference price formation but it does not consider contextual information. We extend the BWW study by adding market price information as background to the experiment, and we investigate how this additional information affects reference point formation and updating. Our overarching hypothesis is that if the market background information has no impact then it should not alter the results of BWW; otherwise, the additional market information should be an explanatory variable for the reference point. Our results confirm the hypothesis that market information matters. We also investigate the impact of pessimism and optimism on reference prices by the combining the BWW framework with a model of disappointment aversion and anticipatory feelings developed by Gollier and Muerman (2010). Our study fills a void in the literature by providing new evidence on the impact of contextual market information on reference point formation in an investment setting.

We use a new database of long-run stock, bond, bill, inflation, and currency returns to estimate the equity risk premium for 17 countries and a world index over a 106-year interval. Taking U.S. Treasury bills (government bonds) as the risk-free asset, the annualised equity premium for the world index was 4.7% (4.0%). We report the historical equity premium for each market in local currency and US dollars, and decompose the premium into dividend growth, multiple expansion, the dividend yield, and changes in the real exchange rate. We infer that investors expect a premium on the world index of around 3-3½% on a geometric mean basis, or approximately 4½-5% on an arithmetic basis.

In this paper we investigate short-run co-movements before and after the Lehman Brothers' collapse among the volatility series of US and a number of European countries. The series under investigation (implied and realized volatility) exhibit long-memory and, in order to avoid missspecification errors related to the parameterization of a long memory multivariate model, we rely on wavelet analysis. More specifically, we retrieve the time series of wavelet coefficients for each volatility series for high frequency scales, using the Maximal Overlapping Discrete Wavelet transform and we apply Maximum Likelihood for a factor decomposition of the short-run covariance matrix. The empirical evidence shows an increased interdependence in the post-break period and points at an increasing (decreasing) role of the common shock underlying the dynamics of the implied (realized) volatility series, once we move from the 2-4 days investment time horizon to the 8-16 days. Moreover, there is evidence of contagion from the US to Europe immediately after the Lehman Brothers' collapse, only for realized volatilities over an investment time horizon between 8 and 16 days.

We examine the impact of age similarity between independent directors and the CEO on earnings management. Using changes in independent director composition due to sameaged director deaths and retirements for identification, we find that firms with the presence of independent directors who have the same age with the CEO are more likely to manage earnings. We further find that age similarity between these two parties increases earnings management through lowering the effectiveness of board monitoring. Additionally, this positive impact decreases as the age gap widens, but intensifies if independent directors share other characteristics with the CEO, if independent directors sit on audit or nomination committees, if firms with lower information asymmetry and if CEOs are older. Our results are robust to alternative proxies of earnings management.

This study aims to analyze the influence of foreign exchange reserves, export values, and import values based on SITC classifications on the Indonesia Composite Stock Price Index (IHSG) from 2011 to 2020. The background of this study lies in the importance of external sector indicators in shaping capital market dynamics, particularly in emerging economies. Using a quantitative approach, this research employs multiple linear regression with time series secondary data obtained from the Central Statistics Agency (BPS). The findings indicate that foreign exchange reserves have a positive and significant effect on IHSG, showing that stronger reserves enhance investor confidence. Export values, however, have a negative and significant impact, suggesting that increased exports do not always correlate with improved stock performance, potentially due to the low value-added nature of exported goods. Conversely, import values have a positive and significant effect, indicating that importsparticularly of capital goods and raw materials-support industrial activity and market confidence. Simultaneously, all independent variables significantly influence IHSG, with an R-squared of 91.42%. The model passes all classical assumption tests, confirming its statistical validity. These results highlight the importance of external sector performance in influencing stock market movements and can serve as a reference for evidence-based economic policy formulation.

In this paper we examine the time-varying integration between eight European post-transition government bond markets and the Eurozone bond market. The objective is twofold: first is to measure the level of integration in these economies, and the second is to better understand some of the fundamental drivers of integration. Our results suggest that integration varies widely across the region. One of the major drivers of integration is economic development, as more advanced countries generally had higher levels of integration. Moreover, joining the European Union either exerted a positive boost or was neutral with regards to sovereign bond integration. Finally, integration changes over time; in particular, integration has decreased with the financial crisis, although the decrease levelled off relatively swiftly soon after.

In this paper we examine the time-varying integration between eight European post-transition government bond markets and the Eurozone bond market. The objective is twofold: first is to measure the level of integration in these economies, and the second is to better understand some of the fundamental drivers of integration. Our results suggest that integration varies widely across the region. One of the major drivers of integration is economic development, as more advanced countries generally had higher levels of integration. Moreover, joining the European Union either exerted a positive boost or was neutral with regards to sovereign bond integration. Finally, integration changes over time; in particular, integration has decreased with the financial crisis, although the decrease levelled off relatively swiftly soon after.

How strong has been the effect of the Global Financial Crisis (GFC) on systemic risk in sovereign bond markets? Was the increase in credit spreads relative to triple-A benchmarks which followed the GFC the result of higher sovereign credit risk or the result of a re-pricing that reflected changes in broader market conditions and risk aversion? In this paper we examine these issues by specifying a sovereign credit yield curve which relates sovereign yield spreads to credit ratings and global variables. The model allows for time-variation in both the price of credit risk and the average spread across all rating categories, which proxies the effect of global risk factors on yield spreads. We use daily data of ten-year bond yields and ratings from a large database of 64 countries, covering both emerging markets and developed economies, for the period from 1/1/2000 to 1/1/2015. Our estimates suggest that sovereign risk premia increased significantly after the GFC with most of the increase due to a re-pricing of broader market risks rather than an increase in the quantity or price of sovereign risk per se. This increase in global risk could be the result of a flight-to-quality from lower-rated sovereign bonds to AAA benchmark bonds. Interestingly, we find that global risk in the sovereign bond market is driven by global variables that relate to investor confidence, volatility risk, central bank liquidity and the slope of the yield curve in the US.

Economic instability in emerging economies presents substantial challenges for firms, particularly in accessing debt funding, due to heightened perceived risk. This often results in a less favorable debt-toequity ratio and complicates the overall composition of capital structure. Macroeconomic conditions play a pivotal role in influencing investor sentiment and risk perceptions, which in turn complicate capital structure decisions. This study aims to investigate the impact of various macroeconomic variables on the capital structure decisions of firms within the Indian automobile and automobile ancillary sectors over a comprehensive 17-year period from 2004 to 2020. They are utilizing secondary data collected from reputable sources like ProwessIQ, the Reserve Bank of India, and financial reports. The study employs various statistical tools, including descriptive statistics, correlation analysis, and dynamic panel data regression models, to analyze the data. The findings indicate that macroeconomic variables significantly shape the optimal capital structure decisions in the Indian automotive sector. Key variables such as the bank rate, GDP growth rate, inflation rate, and public debt substantially impact leverage ratios. For instance, an increase in the bank rate or public debt levels correlates with higher leverage ratios, suggesting that firms adjust their capital structures in response to changes in these macroeconomic indicators. This study provides valuable insights into the complex interplay between macroeconomic conditions and capital structure financing decisions. By highlighting the significant influence of these broader economic factors, the research underscores the necessity for firms, especially in emerging economies like India, to consider these determinants when making financial decisions. The findings thus contribute to a deeper understanding of capital structure dynamics in the face of macroeconomic challenges within the Indian automotive sector.

In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path variation and discontinuous jump variation to provide a practical and robust framework for non- parametrically measuring the jump component in asset return volatility. By using 5-minute high-frequency data of MASI Index in Morocco for the period (January 15, 2010 - January 29, 2016), we estimate parameters of the constructed GARCH and EGARCH-type models (namely, GARCH, GARCH-RV, GARCH-CJ, EGARCH, EGARCH-RV, and EGARCH-CJ) and evaluate their predictive power to forecast future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility while the discontinuous jump variation contains relatively less information for forecasting volatility. More interestingly, the findings show that the GARCH-CJ-type models have stronger predictive power for future volatility than the other two types of models. The...

We extend the VAR based intertemporal asset allocation approach from to the case where the VAR parameter estimates are adjusted for smallsample bias. We apply the analytical bias formula from Pope (1990) using both Campbell et al.'s dataset, and an extended dataset with quarterly data from 1952 to 2006. The results show that correcting the VAR parameters for small-sample bias has both quantitatively and qualitatively important effects on the strategic intertemporal part of optimal portfolio choice, especially for bonds: for intermediate values of risk-aversion, the intertemporal hedging demand for bonds -and thereby the total demand for bonds -is strongly reduced by the bias-adjustment. We also investigate the robustness of the results by changing the lag-length and one of the state variables of the VAR, and we conduct a simulation study to analyze the properties of the bias adjustment procedure.

We find that changes in commercial banks' balance sheet size possess strong explanatory power for the cross-section of U.S. stock and bond returns during the 1947-2022 period. The rationale is that fluctuations in commercial banks' balance sheet size are a proxy for funding conditions. When bank lending growth is high (good funding conditions) commercial banks' balance sheet size increases. Assets with high sensitivity to changes in commercial banks' balance sheet size command higher premium because they are more sensitive to changes in funding conditions. These findings are consistent with the literature that has found that commercial banks' asset growth predicts future returns.

In this paper, I conduct a comprehensive study of using machine learning tools to forecast the U.S. stock returns. I use three sets of predictors: the past history summarized by 120 lagged returns, the technical indicators measured by 120 moving average trading signals, and the 79 firm fundamentals, which helps to understand the weak-form market efficiency, algorithm trading and fundamental analysis. I find each set independently has strong predictive power, and buying the top 20% stocks with the greatest predicted returns and shorting bottom 20% with the lowest earns economically significant profits, and the profitability is robust to a number of controls. Econometrically, neural network generally improves forecasting over linear models, but makes little difference with firm fundamental predictors. Ensemble method tends to perform the best. However, when combining information from all the predictors, traditional machine learning improves little the performance due to perhaps not enough time series for too large dimensionality. In contrast, simple forecasting combination and portfolio diversification approach provide large gains.

This study examines the relationship between the copper price and the nominal exchange rate between the Zambian kwacha and the U.S. dollar. For this purpose the study utilized monthly data on copper prices and the kwacha/US dollar exchange rate for the period 2000 to 2014 and controlled for the effects of domestic productivity, inflation and interest rates on government securities in Zambia and the United States. The study applied econometric techniques of cointegration, granger causality, impulse responses and variance decomposition to analyze interrelationships among these variables. The study found that international copper prices, domestic productivity and short term interest rates on government securities have a positive long run impact on the exchange rate between the two currencies. The study further finds that domestic inflation and short term interest rates on Treasury securities in the United States have a negative effect on the exchange rate between the two currencies in ...

This study examines the relationship between the copper price and the nominal exchange rate between the Zambian kwacha and the U.S. dollar. For this purpose the study utilized monthly data on copper prices and the kwacha/US dollar exchange rate for the period 2000 to 2014 and controlled for the effects of domestic productivity, inflation and interest rates on government securities in Zambia and the United States. The study applied econometric techniques of cointegration, granger causality, impulse responses and variance decomposition to analyze interrelationships among these variables. The study found that international copper prices, domestic productivity and short term interest rates on government securities have a positive long run impact on the exchange rate between the two currencies. The study further finds that domestic inflation and short term interest rates on Treasury securities in the United States have a negative effect on the exchange rate between the two currencies in ...

We provide evidence of the strong long-run relation between expected returns and market beta risk in the challenging (for the CAPM) post-1963 period as well as over longer time periods. We show that returns averaged over long horizons (up to 10 years) are related positively and signi…cantly to the market risk betas computed over similar periods. For the Fama-French 25 size and book-to-market portfolios and the industry portfolios, the market risk premium is estimated signi…cantly at economically-meaningful values between 6% and 11% per annum over a 5-to 10year period. When paired with alphas whose economic and statistical signi…cance decreases with the length of the horizon, our evidence is suggestive of the near long-run mean-variance e¢ ciency of the market portfolio and an approximate long-run version of the classical CAPM.

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We examine the predictive power of the CDS-bond basis for future corporate bond returns. We find that residual basis, the part of the CDS-bond basis that cannot be explained by a wide range of market frictions such as counterparty risk, funding risk, and liquidity risk, strongly negatively predicts excess returns. Controlling for systematic risk factors, including credit risk and liquidity risk, we find that a bond portfolio formed on the residual basis generates a significant abnormal bond return of 1.79% at the 20-day horizon. The abnormal returns due to the residual basis reflect mispricing rather than missing systematic risk factors. These results are robust to different horizons and sample periods and to the various characteristics of bonds. Overall, our results imply a beneficial role of CDS in the bond market as the existence of mispricing between CDS and bonds results in a subsequent price convergence in bonds.

In this paper we analyze investment sensitivity to cash flows in family-controlled businesses (FCBs) before and after the initial VC investment. We argue that highly constrained ones will be more inclined to change the preservation of the socioemotional wealth as the highest order reference point and, hence, accept the entry of external shareholders such as Venture Capital (VC) institutions. We find that financial constraints are significantly higher in first generation VC-backed FCBs than in similar untreated firms. We also find that VC involvement alleviates but does not fully eliminate the investment-cash flow sensitivity in investee first generation FCBs.

The article develops a long-short portfolio construction technique that captures the fundamentals of backwardation and contango present in commodity futures markets and simultaneously deviates from the equal-weighting scheme traditionally employed in the literature. The sophisticated weighting schemes based on risk minimization and risk timing are found to dominate the traditional naive allocation and the schemes based on utility maximization. The conclusion applies to both momentum and term structure portfolios and persists after accounting for transaction costs, lack of liquidity, various model specifications, and different sub-periods.

Automated markets are becoming increasingly widespread, and their efficiency properties are of corresponding concern to regulators and exchange policy makers. Many systems are implemented in settings characterized by a distinct lack of liquidity, however, often by design. We evaluate the performance of such a market, the GLOBEX overnight trading system, in absolute terms and relative to a liquid benchmark, the floor market of the Chicago Mercantile Exchange. Our results with respect to bid-ask spreads and adverse selection suggest that the nature of the environment is an important determinant of market performance, but that an automated market can operate well in a relatively illiquid setting. Price clustering, indicative of a lack of pricing efficiency, is prevalent on the automated system, but price resolution improves as trading frequency increases.