{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/404518"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/404518","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Empirical Studies in Financial Econometrics: Market Functioning, Forecasting, and Reform","abstract":"This thesis comprises three chapters at the intersection of empirical finance and public policy. The chapters are linked by their examination of how financial markets and information transmission within these interact with macroeconomic modelling, regulatory intervention, and institutional design. The first chapter, Forecasting Macro with Finance, is co-authored with Dr Niklas Schmitz. While financial markets are known to contain information about future economic developments, the channels through which asset prices enhance macroeconomic forecastability remain insufficiently understood. The chapter develops a set of like-for-like forecasting experiments to isolate how different properties of financial data and model choice affect macroeconomic forecastability. Using U.S. data on inflation, industrial production, unemployment, and equity returns, it tests eight hypotheses along two dimensions: the contribution of financial data given different estimation methods and model classes, and the role of modelling choices given different financial inputs. Data aspects include cross-sectional granularity, intra-period frequency, and real-time, revisionless availability; modelling choices tested include sparsity, direct versus two-step (indirect) specification, nonlinear model classes, and state dependence on volatile periods. The chapter finds financial data can deliver consistent and economically meaningful gains, but only under suitable modelling choices: among the models tested, Random Forests most reliably extract useful signals, whereas an unregularised VAR often fails to do so; by contrast, expanding the financial information set along cross-sectional granularity, frequency, or real-time dimensions yields little systematic benefit. Gains strengthen somewhat under elevated policy uncertainty, especially for inflation, but are otherwise fragile. The analysis clarifies how data and model choices interact and provides practical guidance for forecasters on when and how to use financial inputs. The second chapter, The Effects of LIBOR’s Manipulation and Discontinuation on Volatility and Liquidity in LIBOR Futures Markets, examines how four key events related to the manipulation and eventual phaseout of LIBOR affected liquidity and volatility in 3-month LIBOR futures in the GBP and USD markets. Two events concern the manipulation scandal; two concern regulators' reaction of not only reforming, but altogether discontinuing the LIBOR. Only the final discontinuation in late 2021 generated a measurable deterioration in market functioning; earlier events, including Barclays’ 2012 admission of manipulation and regulators' public announcement of LIBOR's forthcoming cessation in 2017, had no discernible effects. Even at the point of discontinuation, volatility and illiquidity remained smaller than in stress episodes such as the global financial crisis and the COVID-19 shock. These findings suggest that, contrary to widespread concerns among practitioners and regulators, the phaseout of a systemically important benchmark can be managed without causing prolonged or severe disruption. The chapter offers insights for policymakers weighing the risks of reforming versus retiring flawed financial market instruments, including that opposing a discontinuation in favour of a reform on disruption grounds is justified only if the alternative reform offers very little disruption indeed. The third chapter, Institutional Design and the Risk-Return Relation in Carbon Markets: Evidence from the EU ETS, examines the risk-return trade-off in the EU Emissions Trading System (EU ETS) and how it has evolved with the market’s institutional development. Using monthly EU ETS futures and realised risk measures derived from mixed-data sampling aggregation, it traces the functional shape of the risk-return relation and its transformation across market regimes. In the EU ETS’s early years, the relationship was markedly nonlinear: premia were flat or even negative in normal conditions and increased only during episodes of extreme volatility, consistent with a crisis-driven and state-dependent pricing regime. Following the introduction of the Market Stability Reserve in 2019, this pattern gives way to a stable, near-linear trade-off more aligned with standard asset-pricing behaviour. The results support the view that credible, rule-based institutional design can stabilise expectations and normalise risk pricing. More broadly, the chapter documents how changes in institutional architecture coincide with marked shifts in the transmission of risk and return in regulated markets, with implications for the design of carbon markets and their ability to deliver emissions reductions efficiently.","abstract_html":"This thesis comprises three chapters at the intersection of empirical finance and public policy. The chapters are linked by their examination of how financial markets and information transmission within these interact with macroeconomic modelling, regulatory intervention, and institutional design. The first chapter, Forecasting Macro with Finance, is co-authored with Dr Niklas Schmitz. While financial markets are known to contain information about future economic developments, the channels through which asset prices enhance macroeconomic forecastability remain insufficiently understood. The chapter develops a set of like-for-like forecasting experiments to isolate how different properties of financial data and model choice affect macroeconomic forecastability. Using U.S. data on inflation, industrial production, unemployment, and equity returns, it tests eight hypotheses along two dimensions: the contribution of financial data given different estimation methods and model classes, and the role of modelling choices given different financial inputs. Data aspects include cross-sectional granularity, intra-period frequency, and real-time, revisionless availability; modelling choices tested include sparsity, direct versus two-step (indirect) specification, nonlinear model classes, and state dependence on volatile periods. The chapter finds financial data can deliver consistent and economically meaningful gains, but only under suitable modelling choices: among the models tested, Random Forests most reliably extract useful signals, whereas an unregularised VAR often fails to do so; by contrast, expanding the financial information set along cross-sectional granularity, frequency, or real-time dimensions yields little systematic benefit. Gains strengthen somewhat under elevated policy uncertainty, especially for inflation, but are otherwise fragile. The analysis clarifies how data and model choices interact and provides practical guidance for forecasters on when and how to use financial inputs. The second chapter, The Effects of LIBOR’s Manipulation and Discontinuation on Volatility and Liquidity in LIBOR Futures Markets, examines how four key events related to the manipulation and eventual phaseout of LIBOR affected liquidity and volatility in 3-month LIBOR futures in the GBP and USD markets. Two events concern the manipulation scandal; two concern regulators' reaction of not only reforming, but altogether discontinuing the LIBOR. Only the final discontinuation in late 2021 generated a measurable deterioration in market functioning; earlier events, including Barclays’ 2012 admission of manipulation and regulators' public announcement of LIBOR's forthcoming cessation in 2017, had no discernible effects. Even at the point of discontinuation, volatility and illiquidity remained smaller than in stress episodes such as the global financial crisis and the COVID-19 shock. These findings suggest that, contrary to widespread concerns among practitioners and regulators, the phaseout of a systemically important benchmark can be managed without causing prolonged or severe disruption. The chapter offers insights for policymakers weighing the risks of reforming versus retiring flawed financial market instruments, including that opposing a discontinuation in favour of a reform on disruption grounds is justified only if the alternative reform offers very little disruption indeed. The third chapter, Institutional Design and the Risk-Return Relation in Carbon Markets: Evidence from the EU ETS, examines the risk-return trade-off in the EU Emissions Trading System (EU ETS) and how it has evolved with the market’s institutional development. Using monthly EU ETS futures and realised risk measures derived from mixed-data sampling aggregation, it traces the functional shape of the risk-return relation and its transformation across market regimes. In the EU ETS’s early years, the relationship was markedly nonlinear: premia were flat or even negative in normal conditions and increased only during episodes of extreme volatility, consistent with a crisis-driven and state-dependent pricing regime. Following the introduction of the Market Stability Reserve in 2019, this pattern gives way to a stable, near-linear trade-off more aligned with standard asset-pricing behaviour. The results support the view that credible, rule-based institutional design can stabilise expectations and normalise risk pricing. More broadly, the chapter documents how changes in institutional architecture coincide with marked shifts in the transmission of risk and return in regulated markets, with implications for the design of carbon markets and their ability to deliver emissions reductions efficiently.","abstract_has_math":false,"creators":["Bachmair, Kilian"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Linton, Oliver"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-05","date_published":"2025-12-05","updated_at":"2026-07-24T01:33:18Z","subjects":["Financial Econometrics","Forecasting","Market Functioning","Reform"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/2cbfc52b-d07c-4858-a26b-afb8c9439072/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.131082","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Linton, Oliver"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Studienstiftung des deutschen Volkes; University of Cambridge Faculty of Economics"]},{"key":"dc:creator","label":"Author","values":["Bachmair, Kilian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-12-05"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/404518"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Financial Econometrics","Forecasting","Market Functioning","Reform"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/2cbfc52b-d07c-4858-a26b-afb8c9439072/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.131082"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/7a84caf0-0a89-487e-aaac-fcd3a963fee6/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis comprises three chapters at the intersection of empirical finance and public policy. 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Data aspects include cross-sectional granularity, intra-period frequency, and real-time, revisionless availability; modelling choices tested include sparsity, direct versus two-step (indirect) specification, nonlinear model classes, and state dependence on volatile periods. The chapter finds financial data can deliver consistent and economically meaningful gains, but only under suitable modelling choices: among the models tested, Random Forests most reliably extract useful signals, whereas an unregularised VAR often fails to do so; by contrast, expanding the financial information set along cross-sectional granularity, frequency, or real-time dimensions yields little systematic benefit. Gains strengthen somewhat under elevated policy uncertainty, especially for inflation, but are otherwise fragile. The analysis clarifies how data and model choices interact and provides practical guidance for forecasters on when and how to use financial inputs. The second chapter, The Effects of LIBOR’s Manipulation and Discontinuation on Volatility and Liquidity in LIBOR Futures Markets, examines how four key events related to the manipulation and eventual phaseout of LIBOR affected liquidity and volatility in 3-month LIBOR futures in the GBP and USD markets. Two events concern the manipulation scandal; two concern regulators' reaction of not only reforming, but altogether discontinuing the LIBOR. Only the final discontinuation in late 2021 generated a measurable deterioration in market functioning; earlier events, including Barclays’ 2012 admission of manipulation and regulators' public announcement of LIBOR's forthcoming cessation in 2017, had no discernible effects. Even at the point of discontinuation, volatility and illiquidity remained smaller than in stress episodes such as the global financial crisis and the COVID-19 shock. These findings suggest that, contrary to widespread concerns among practitioners and regulators, the phaseout of a systemically important benchmark can be managed without causing prolonged or severe disruption. The chapter offers insights for policymakers weighing the risks of reforming versus retiring flawed financial market instruments, including that opposing a discontinuation in favour of a reform on disruption grounds is justified only if the alternative reform offers very little disruption indeed. The third chapter, Institutional Design and the Risk-Return Relation in Carbon Markets: Evidence from the EU ETS, examines the risk-return trade-off in the EU Emissions Trading System (EU ETS) and how it has evolved with the market’s institutional development. Using monthly EU ETS futures and realised risk measures derived from mixed-data sampling aggregation, it traces the functional shape of the risk-return relation and its transformation across market regimes. In the EU ETS’s early years, the relationship was markedly nonlinear: premia were flat or even negative in normal conditions and increased only during episodes of extreme volatility, consistent with a crisis-driven and state-dependent pricing regime. Following the introduction of the Market Stability Reserve in 2019, this pattern gives way to a stable, near-linear trade-off more aligned with standard asset-pricing behaviour. The results support the view that credible, rule-based institutional design can stabilise expectations and normalise risk pricing. 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Data aspects include cross-sectional granularity, intra-period frequency, and real-time, revisionless availability; modelling choices tested include sparsity, direct versus two-step (indirect) specification, nonlinear model classes, and state dependence on volatile periods. The chapter finds financial data can deliver consistent and economically meaningful gains, but only under suitable modelling choices: among the models tested, Random Forests most reliably extract useful signals, whereas an unregularised VAR often fails to do so; by contrast, expanding the financial information set along cross-sectional granularity, frequency, or real-time dimensions yields little systematic benefit. Gains strengthen somewhat under elevated policy uncertainty, especially for inflation, but are otherwise fragile. The analysis clarifies how data and model choices interact and provides practical guidance for forecasters on when and how to use financial inputs. The second chapter, The Effects of LIBOR’s Manipulation and Discontinuation on Volatility and Liquidity in LIBOR Futures Markets, examines how four key events related to the manipulation and eventual phaseout of LIBOR affected liquidity and volatility in 3-month LIBOR futures in the GBP and USD markets. Two events concern the manipulation scandal; two concern regulators' reaction of not only reforming, but altogether discontinuing the LIBOR. Only the final discontinuation in late 2021 generated a measurable deterioration in market functioning; earlier events, including Barclays’ 2012 admission of manipulation and regulators' public announcement of LIBOR's forthcoming cessation in 2017, had no discernible effects. Even at the point of discontinuation, volatility and illiquidity remained smaller than in stress episodes such as the global financial crisis and the COVID-19 shock. These findings suggest that, contrary to widespread concerns among practitioners and regulators, the phaseout of a systemically important benchmark can be managed without causing prolonged or severe disruption. The chapter offers insights for policymakers weighing the risks of reforming versus retiring flawed financial market instruments, including that opposing a discontinuation in favour of a reform on disruption grounds is justified only if the alternative reform offers very little disruption indeed. The third chapter, Institutional Design and the Risk-Return Relation in Carbon Markets: Evidence from the EU ETS, examines the risk-return trade-off in the EU Emissions Trading System (EU ETS) and how it has evolved with the market’s institutional development. Using monthly EU ETS futures and realised risk measures derived from mixed-data sampling aggregation, it traces the functional shape of the risk-return relation and its transformation across market regimes. In the EU ETS’s early years, the relationship was markedly nonlinear: premia were flat or even negative in normal conditions and increased only during episodes of extreme volatility, consistent with a crisis-driven and state-dependent pricing regime. Following the introduction of the Market Stability Reserve in 2019, this pattern gives way to a stable, near-linear trade-off more aligned with standard asset-pricing behaviour. The results support the view that credible, rule-based institutional design can stabilise expectations and normalise risk pricing. 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